Thesis_draft0.2.bbl

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      \field{abstract}{Building maps have a plethora of applications in government, industry and academia. In most cases, large scale maps can be retrieved from {OpenStreetMap} vector data. However, for certain rapidly changing built and semi-built environments, corresponding maps are not as accurate and contain label noise such as missing, incorrectly present, shifted labels, etc.; mainly because buildings in those regions are constantly being constructed, deconstructed, replaced and altered. One such case is extant in the Rohingya camps of southeastern border region of Bangladesh. Mass refugee influx in late 2017 and following population growth has necessitated the construction of buildings and expansion of camps. Consequently, reliable methods are necessary for detecting and documenting camp buildings. Ultra-high-resolution drone images of Rohingya camps are semantically segmented through fully convolutional U-Net deep learning systems for generating accurate building maps from noisy labels. A wide variety of noises are prevalent in the labels. Deep learning systems provide less noisy predictions compared to the classification tool in the most widely used Geographic Information System ({GIS}) software, {ArcGIS}. Data augmentation and regularization allows reliable learning, even in the presence of label noise. During testing, calculation of numeric performance metrics against noisy labels can grossly underestimate true skill and performance of the model. A subset of 22 million pixels of the testing data is relabelled by hand to obtain noise-free labels. Testing our generated maps against noisy and noise-free labels confirms that true performance is higher than otherwise indicated by freely available building maps. Empirical results reveal that utilized pipeline is able to learn from noisy data and produce labels which are more accurate and less noisy. Labels generated by our best performing system provide Intersection-over-Union ({IoU}) gain of 17.6\% and Dice score gain of 13.6\% over freely available labels from {OpenStreetMap}. Finally, spatio-temporal building maps are generated to portray the applicability of this research.}
      \field{day}{1}
      \field{issn}{0143-1161}
      \field{journaltitle}{International Journal of Remote Sensing}
      \field{month}{11}
      \field{note}{Publisher: Taylor \& Francis \_eprint: https://doi.org/10.1080/01431161.2020.1763496}
      \field{number}{21}
      \field{title}{Learning to extract buildings from ultra-high-resolution drone images and noisy labels}
      \field{urlday}{31}
      \field{urlmonth}{10}
      \field{urlyear}{2021}
      \field{volume}{41}
      \field{year}{2020}
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      \field{pages}{8216\bibrangedash 8237}
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    \entry{alix-garcia_refugee_2018}{article}{}
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      \field{abstract}{We combine nighttime lights data, official statistics, and new household survey data from northern Kenya in order to assess the impact of long-term refugee camps on host populations. The nighttime lights estimates show that refugee inflows increase economic activity in areas very close to Kakuma refugee camp: the elasticity of the luminosity index to refugee population is 0.36 within a 10 km distance from the camp center. In addition, household consumption within the same proximity to the camp is 25\% higher than in areas farther away. Price, household survey, and official statistics suggest that the mechanisms driving this positive effect are increased availability of new employment and price changes in agricultural and livestock markets that are favorable to local producers.}
      \field{day}{1}
      \field{issn}{0304-3878}
      \field{journaltitle}{Journal of Development Economics}
      \field{langid}{english}
      \field{month}{1}
      \field{shortjournal}{Journal of Development Economics}
      \field{shorttitle}{Do refugee camps help or hurt hosts?}
      \field{title}{Do refugee camps help or hurt hosts? The case of Kakuma, Kenya}
      \field{urlday}{4}
      \field{urlmonth}{6}
      \field{urlyear}{2022}
      \field{volume}{130}
      \field{year}{2018}
      \field{dateera}{ce}
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      \field{pages}{66\bibrangedash 83}
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      \keyw{Forced migration impacts,Household data,Nighttime lights,Price analysis,Refugee impacts}
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    \entry{anderson_corporate_2019}{article}{}
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      \field{abstract}{{OpenStreetMap} ({OSM}), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the {OSM} community largely focuses on the motivations for why members contribute map data and the resulting data quality. Recently, large corporations including Apple, Microsoft, and Facebook have been hiring editors to contribute to the {OSM} database. In this article, we explore the influence these corporate editors are having on the map by first considering the history of corporate involvement in the community and then analyzing historical quarterly-snapshot {OSM}-{QA}-Tiles to show where and what these corporate editors are mapping. Cumulatively, millions of corporate edits have a global footprint, but corporations vary in geographic reach, edit types, and quantity. While corporations currently have a major impact on road networks, non-corporate mappers edit more buildings and points-of-interest: representing the majority of all edits, on average. Since corporate editing represents the latest stage in the evolution of corporate involvement, we raise questions about how the {OSM} community—and researchers—might proceed as corporate editing grows and evolves as a mechanism for expanding the map for multiple uses.}
      \field{issn}{2220-9964}
      \field{journaltitle}{{ISPRS} International Journal of Geo-Information}
      \field{langid}{english}
      \field{month}{5}
      \field{note}{Number: 5 Publisher: Multidisciplinary Digital Publishing Institute}
      \field{number}{5}
      \field{title}{Corporate Editors in the Evolving Landscape of {OpenStreetMap}}
      \field{urlday}{10}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{volume}{8}
      \field{year}{2019}
      \field{dateera}{ce}
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      \field{pages}{232}
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      \keyw{corporations,geospatial data,open data,{OpenStreetMap},Volunteered Geographic Information}
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    \entry{audebert_deep_2017}{inproceedings}{}
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           given={Bertrand},
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           given={Marin},
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           family={Lefèvre},
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           given={Sébastien},
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           given={Renaud},
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      \field{abstract}{This work shows how deep learning techniques can benefit to remote sensing. We focus on tasks which are recurrent in Earth Observation data analysis. For classification and semantic mapping of aerial images, we present various deep network architectures and show that context information and dense labeling allow to reach better performances. For estimation of normals in point clouds, combining Hough transform with convolutional networks also improves the accuracy of previous frameworks by detecting hard configurations like corners. It shows that deep learning allows to revisit remote sensing and offers promising paths for urban modeling and monitoring.}
      \field{booktitle}{2017 Joint Urban Remote Sensing Event ({JURSE})}
      \field{eventtitle}{2017 Joint Urban Remote Sensing Event ({JURSE})}
      \field{month}{3}
      \field{title}{Deep learning for urban remote sensing}
      \field{year}{2017}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1\bibrangedash 4}
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      \verb{doi}
      \verb 10.1109/JURSE.2017.7924536
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      \verb{file}
      \verb IEEE Xplore Abstract Record:/home/chris/Zotero/storage/DNSCKS75/7924536.html:text/html;Submitted Version:/home/chris/Zotero/storage/LNMYULRL/Audebert et al. - 2017 - Deep learning for urban remote sensing.pdf:application/pdf
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      \keyw{Remote sensing,Three-dimensional displays,Training,Feature extraction,Support vector machines,Labeling,Semantics}
    \endentry
    \entry{azimi_automatic_2021}{inproceedings}{}
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        {{hash=242757d71f1099f01a60a398d5409e76}{%
           family={Merkle},
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           given={N.},
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        {{hash=a38521de03fdc4016f14d4eedfcf4d4e}{%
           family={Henry},
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        {Copernicus {GmbH}}%
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      \field{abstract}{{<}p{>}{<}strong class="journal-{contentHeaderColor}"{>}Abstract.{<}/strong{>} The management of large-scale events with a widely distributed camping area is a special challenge for organisers and security forces and requires both comprehensive preparation and attentive monitoring to ensure the safety of the participants. Crucial to this is the availability of up-to-date situational information, e.g. from remote sensing data. In particular, information on the number and distribution of people is important in the event of a crisis in order to be able to react quickly and effectively manage the corresponding rescue and supply logistics. One way to estimate the number of persons especially at night is to classify the type and size of objects such as tents and vehicles on site and to distinguish between objects with and without a sleeping function. In order to make this information available in a timely manner, an automated situation assessment is required. In this work, we have prepared the first high-quality dataset in order to address the aforementioned challenge which contains aerial images over a large-scale festival of different dates. We investigate the feasibility of this task using Convolutional Neural Networks for instance-wise semantic segmentation and carry out several experiments using the Mask-{RCNN} algorithm and evaluate the results. Results are promising and indicate the possibility of function-based tent classification as a proof-of-concept. The results and thereof discussions can pave the way for future developments and investigations.{<}/p{>}}
      \field{booktitle}{The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}
      \field{day}{28}
      \field{eventtitle}{{XXIV} {ISPRS} Congress {<}q{>}Imaging today, foreseeing tomorrow{<}/q{>}, Commission {II} - 2021 edition, 5\&ndash;9 July 2021}
      \field{month}{6}
      \field{note}{{ISSN}: 1682-1750}
      \field{title}{{AUTOMATIC} {OBJECT} {SEGMENTATION} {TO} {SUPPORT} {CRISIS} {MANAGEMENT} {OF} {LARGE}-{SCALE} {EVENTS}}
      \field{urlday}{22}
      \field{urlmonth}{12}
      \field{urlyear}{2021}
      \field{volume}{{XLIII}-B2-2021}
      \field{year}{2021}
      \field{dateera}{ce}
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      \true{nocite}
      \field{pages}{433\bibrangedash 440}
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    \endentry
    \entry{bengio_practical_2012}{article}{}
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           given={Yoshua},
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      \field{abstract}{Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyper-parameters. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyper-parameters, in particular in the context of learning algorithms based on back-propagated gradient and gradient-based optimization. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. It closes with open questions about the training difficulties observed with deeper architectures.}
      \field{day}{24}
      \field{langid}{english}
      \field{month}{6}
      \field{title}{Practical recommendations for gradient-based training of deep architectures}
      \field{urlday}{16}
      \field{urlmonth}{3}
      \field{urlyear}{2022}
      \field{year}{2012}
      \field{dateera}{ce}
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      \true{nocite}
      \verb{doi}
      \verb 10.48550/arXiv.1206.5533
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      \verb{file}
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    \entry{bengio_deep_2017}{book}{}
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        {{hash=5d2585c11210cf1d4512e6e0a03ec315}{%
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           given={Aaron},
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      \list{publisher}{1}{%
        {MIT} press Cambridge, {MA}, {USA}%
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      \field{abstract}{Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance. To avoid a time consuming and unreproducible manual trial-and-error process to find well-performing hyperparameter configurations, various automatic hyperparameter optimization ({HPO}) methods, e.g., based on resampling error estimation for supervised machine learning, can be employed. After introducing {HPO} from a general perspective, this paper reviews important {HPO} methods such as grid or random search, evolutionary algorithms, Bayesian optimization, Hyperband and racing. It gives practical recommendations regarding important choices to be made when conducting {HPO}, including the {HPO} algorithms themselves, performance evaluation, how to combine {HPO} with {ML} pipelines, runtime improvements, and parallelization. This work is accompanied by an appendix that contains information on specific software packages in R and Python, as well as information and recommended hyperparameter search spaces for specific learning algorithms. We also provide notebooks that demonstrate concepts from this work as supplementary files.}
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      \field{journaltitle}{{arXiv}:2107.05847 [cs, stat]}
      \field{month}{11}
      \field{shorttitle}{Hyperparameter Optimization}
      \field{title}{Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges}
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      \keyw{Computer Science - Machine Learning,Statistics - Machine Learning}
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    \entry{blaschke_object_2010}{article}{}
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      \field{abstract}{Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems ({GIS}). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and {GIS} functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 {GIS} and image processing started to grow together rapidly through object based image analysis ({OBIA} - or {GEOBIA} for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 {OBIA}-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the {OBIA}/{GEOBIA} developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the {OBIA} methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.}
      \field{day}{1}
      \field{issn}{0924-2716}
      \field{journaltitle}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
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      \field{month}{1}
      \field{number}{1}
      \field{shortjournal}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
      \field{title}{Object based image analysis for remote sensing}
      \field{urlday}{10}
      \field{urlmonth}{6}
      \field{urlyear}{2022}
      \field{volume}{65}
      \field{year}{2010}
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      \field{pages}{2\bibrangedash 16}
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      \verb{url}
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      \keyw{{GIScience},{GEOBIA},Multiscale image analysis,{OBIA},Object based image analysis}
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      \field{abstract}{The amount of scientific literature on (Geographic) Object-based Image Analysis – {GEOBIA} has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use {GIS}-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science ({GIScience}). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether {GEOBIA} can be regarded as a paradigm according to this definition. We crystallize core concepts of {GEOBIA}, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and {GEOBIA} are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that {GEOBIA} is a new and evolving paradigm.}
      \field{day}{1}
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      \field{journaltitle}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
      \field{langid}{english}
      \field{month}{1}
      \field{shortjournal}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
      \field{title}{Geographic Object-Based Image Analysis – Towards a new paradigm}
      \field{urlday}{10}
      \field{urlmonth}{6}
      \field{urlyear}{2022}
      \field{volume}{87}
      \field{year}{2014}
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      \field{pages}{180\bibrangedash 191}
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      \keyw{Image segmentation,Remote sensing,{GIScience},{GEOBIA},{OBIA},Image classification}
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           given={Paul},
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      }
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        {Eider ({PressMinnesota})}%
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      \field{isbn}{1-5066-9587-6}
      \field{title}{{GIS} fundamentals: A first text on geographic information systems}
      \field{year}{2019}
      \field{dateera}{ce}
      \true{nocite}
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    \entry{bruno_evaluating_2021}{article}{}
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        {{hash=46aca57519d449b63032692d8df2f873}{%
           family={Stadnicka},
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           given={Dorota},
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      \field{abstract}{Task assignment methods usually rely on the fixed mean processing times of operations with the intent of balancing the workload assigned to operators or workstations in the production line. This assignment usually neglects the variability of operator processing times. In this work, a methodology in which the time in which an operator executes a task is variable, accordingly to a learning model, is proposed. It is exploited in order to assess the real-time task assignment adopted in the actual factory. The results show that, by including a learning model, it is possible to predict more accurately the long-term cycle time of the process. Standard scheduling strategies (first operator available, the operator closest to the machine) were compared with learning-oriented strategies (the most skilled, the least skilled). Through the case study, the paper addresses the problem of using a dynamic task assignment.an illustration.}
      \field{day}{17}
      \field{issn}{2168-1015}
      \field{journaltitle}{Journal of Industrial and Production Engineering}
      \field{month}{2}
      \field{note}{Publisher: Taylor \& Francis \_eprint: https://doi.org/10.1080/21681015.2021.1883133}
      \field{number}{2}
      \field{title}{Evaluating the effect of learning rate, batch size and assignment strategies on the production performance}
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      \field{volume}{38}
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      \keyw{assembly,computer simulation,value flow improvement,Workflow balancing}
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    \entry{carrivick_structure_2016}{book}{}
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        {John Wiley \& Sons}%
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      \field{title}{Structure from Motion in the Geosciences}
      \field{year}{2016}
      \field{dateera}{ce}
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      \field{abstract}{We present a pipeline for geomorphological analysis that uses structure from motion ({SfM}) and deep learning on close-range aerial imagery to estimate spatial distributions of rock traits (size, roundness, and orientation) along a tectonic fault scarp. The properties of the rocks on the fault scarp derive from the combination of initial volcanic fracturing and subsequent tectonic and geomorphic fracturing, and our pipeline allows scientists to leverage {UAS}-based imagery to gain a better understanding of such surface processes. We start by using {SfM} on aerial imagery to produce georeferenced orthomosaics and digital elevation models ({DEM}). A human expert then annotates rocks on a set of image tiles sampled from the orthomosaics, and these annotations are used to train a deep neural network to detect and segment individual rocks in the entire site. The extracted semantic information (rock masks) on large volumes of unlabeled, high-resolution {SfM} products allows subsequent structural analysis and shape descriptors to estimate rock size, roundness, and orientation. We present results of two experiments conducted along a fault scarp in the Volcanic Tablelands near Bishop, California. We conducted the first, proof-of-concept experiment with a {DJI} Phantom 4 Pro equipped with an {RGB} camera and inspected if elevation information assisted instance segmentation from {RGB} channels. Rock-trait histograms along and across the fault scarp were obtained with the neural network inference. In the second experiment, we deployed a hexrotor and a multispectral camera to produce a {DEM} and five spectral orthomosaics in red, green, blue, red edge, and near infrared. We focused on examining the effectiveness of different combinations of input channels in instance segmentation.}
      \field{booktitle}{2020 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
      \field{eventtitle}{2020 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
      \field{month}{10}
      \field{note}{{ISSN}: 2153-0866}
      \field{title}{Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning}
      \field{year}{2020}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1276\bibrangedash 1283}
      \range{pages}{8}
      \verb{doi}
      \verb 10.1109/IROS45743.2020.9341354
      \endverb
      \verb{file}
      \verb IEEE Xplore Full Text PDF:/home/chris/Zotero/storage/KCM3SSAD/Chen et al. - 2020 - Geomorphological Analysis Using Unpiloted Aircraft.pdf:application/pdf;IEEE Xplore Abstract Record:/home/chris/Zotero/storage/I86CSCK7/9341354.html:text/html
      \endverb
      \keyw{Neural networks,Deep learning,Cameras,Pipelines,Rocks,Structure from motion,Surface treatment}
    \endentry
    \entry{cities_alliance_dynamics_2022}{report}{}
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        {{hash=d7cc31cd93189c0a1afcedd9d7dc59b8}{%
           family={{Cities Alliance}},
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        {{hash=9c12dff4ece46cbb8163d465db09931f}{%
           family={{AfDB}},
           familyi={A\bibinitperiod}}}%
      }
      \list{location}{1}{%
        {Brussels}%
      }
      \strng{namehash}{3b196bbadb71b2849906ceb2f680380b}
      \strng{fullhash}{3b196bbadb71b2849906ceb2f680380b}
      \strng{bibnamehash}{3b196bbadb71b2849906ceb2f680380b}
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      \strng{authornamehash}{3b196bbadb71b2849906ceb2f680380b}
      \strng{authorfullhash}{3b196bbadb71b2849906ceb2f680380b}
      \field{sortinit}{C}
      \field{sortinithash}{4d103a86280481745c9c897c925753c0}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{abstract}{Secondary systems of cities in Africa ({SSCA}) have become the subject of renewed interest by scholars and international development organisations. This book explores the role played by secondary cities in the development of African countries and regions. It includes a review and discusses trends, influences, and challenges, including the forces of New Economic Geography, facing the development of secondary cities in Africa. Using a systems approach, it examines urban governance, economic, development, social, and environmental factors that have shaped the development of secondary cities. Eight country and city case studies illustrate how they have approached urbanisation, decentralisation, and other processes supporting secondary city development. Case studies include Cape Coast in Ghana, Dire Dawa in Ethiopia, Gabès in Tunisia, Huambo in Angola, Ibadan in Nigeria, Mombasa in Kenya, Gqeberha (formerly Port Elizabeth) in South Africa, and Touba‑Mbacké in Senegal. These case studies provide insights and knowledge about challenges facing the development of secondary cities within the selected countries. Learning outcomes are presented for each country case study, followed by an outline of opportunities open to secondary cities in Africa to become more competitive, dynamic, and liveable. The roles that international development assistance agencies and organisations can play to support the development of secondary systems of cities are discussed. The book concludes with a call for a new urban age agenda for the management and development of African secondary cities. This is focused on the need for improved urban governance, management, planning and economic development, and for enhancing connectivity and logistic systems to build collaborative partnerships between secondary cities and create a strong network of national systems of cities across the continent.}
      \field{title}{Dynamics of Systems of Secondary Cities in Africa}
      \field{year}{2022}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{congalton_assessing_2019}{book}{}
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        {{hash=3e4c0a37573a9d32c703ee993624fcfb}{%
           family={Congalton},
           familyi={C\bibinitperiod},
           given={Russell\bibnamedelima G},
           giveni={R\bibinitperiod\bibinitdelim G\bibinitperiod}}}%
        {{hash=45ac9c0f455fac173a64d78b13fbce7c}{%
           family={Green},
           familyi={G\bibinitperiod},
           given={Kass},
           giveni={K\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {{CRC} press}%
      }
      \strng{namehash}{0c6efd73c3a6ddb42466e581052ac922}
      \strng{fullhash}{0c6efd73c3a6ddb42466e581052ac922}
      \strng{bibnamehash}{0c6efd73c3a6ddb42466e581052ac922}
      \strng{authorbibnamehash}{0c6efd73c3a6ddb42466e581052ac922}
      \strng{authornamehash}{0c6efd73c3a6ddb42466e581052ac922}
      \strng{authorfullhash}{0c6efd73c3a6ddb42466e581052ac922}
      \field{sortinit}{C}
      \field{sortinithash}{4d103a86280481745c9c897c925753c0}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{edition}{3rd}
      \field{isbn}{0-429-05272-3}
      \field{title}{Assessing the accuracy of remotely sensed data: principles and practices}
      \field{year}{2019}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{noauthor_cs231n_nodate}{online}{}
      \field{sortinit}{C}
      \field{sortinithash}{4d103a86280481745c9c897c925753c0}
      \field{labeltitlesource}{title}
      \field{title}{{CS}231n Convolutional Neural Networks for Visual Recognition}
      \field{urlday}{4}
      \field{urlmonth}{7}
      \field{urlyear}{2022}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{file}
      \verb CS231n Convolutional Neural Networks for Visual Recognition:/home/chris/Zotero/storage/A94Z3AN3/convolutional-networks.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://cs231n.github.io/convolutional-networks/
      \endverb
      \verb{url}
      \verb https://cs231n.github.io/convolutional-networks/
      \endverb
    \endentry
    \entry{deng_imagenet_2009}{inproceedings}{}
      \name{author}{6}{}{%
        {{hash=0ae7fdc13773f928525f673b05f37149}{%
           family={Deng},
           familyi={D\bibinitperiod},
           given={Jia},
           giveni={J\bibinitperiod}}}%
        {{hash=7d87c5957b07153c7f18918b92830bf8}{%
           family={Dong},
           familyi={D\bibinitperiod},
           given={Wei},
           giveni={W\bibinitperiod}}}%
        {{hash=d5670b2600fea169724521e252d9d09d}{%
           family={Socher},
           familyi={S\bibinitperiod},
           given={Richard},
           giveni={R\bibinitperiod}}}%
        {{hash=2afdae52015b97674d81efea449edce2}{%
           family={Li},
           familyi={L\bibinitperiod},
           given={Li-Jia},
           giveni={L\bibinithyphendelim J\bibinitperiod}}}%
        {{hash=4838f7fdd28d5cefb28f3b3c734976d4}{%
           family={Li},
           familyi={L\bibinitperiod},
           given={Kai},
           giveni={K\bibinitperiod}}}%
        {{hash=cd00ce5bc45f687c432e52e0fa1a7aa6}{%
           family={Fei-Fei},
           familyi={F\bibinithyphendelim F\bibinitperiod},
           given={Li},
           giveni={L\bibinitperiod}}}%
      }
      \strng{namehash}{990420f755e01028377fcad1464c9706}
      \strng{fullhash}{a16fdd05c52c264b99fe98f4a5e24c60}
      \strng{bibnamehash}{990420f755e01028377fcad1464c9706}
      \strng{authorbibnamehash}{990420f755e01028377fcad1464c9706}
      \strng{authornamehash}{990420f755e01028377fcad1464c9706}
      \strng{authorfullhash}{a16fdd05c52c264b99fe98f4a5e24c60}
      \field{sortinit}{D}
      \field{sortinithash}{6f385f66841fb5e82009dc833c761848}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{shorttitle}
      \field{abstract}{The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “{ImageNet}”, a large-scale ontology of images built upon the backbone of the {WordNet} structure. {ImageNet} aims to populate the majority of the 80,000 synsets of {WordNet} with an average of 500–1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of {WordNet}. This paper offers a detailed analysis of {ImageNet} in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that {ImageNet} is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of {ImageNet} through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of {ImageNet} can offer unparalleled opportunities to researchers in the computer vision community and beyond.}
      \field{booktitle}{2009 {IEEE} Conference on Computer Vision and Pattern Recognition}
      \field{eventtitle}{2009 {IEEE} Conference on Computer Vision and Pattern Recognition}
      \field{month}{6}
      \field{note}{{ISSN}: 1063-6919}
      \field{shorttitle}{{ImageNet}}
      \field{title}{{ImageNet}: A large-scale hierarchical image database}
      \field{year}{2009}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{248\bibrangedash 255}
      \range{pages}{8}
      \verb{doi}
      \verb 10.1109/CVPR.2009.5206848
      \endverb
      \verb{file}
      \verb IEEE Xplore Abstract Record:/home/chris/Zotero/storage/Q9Q8IQ78/5206848.html:text/html
      \endverb
      \keyw{Explosions,Image databases,Image retrieval,Information retrieval,Internet,Large-scale systems,Multimedia databases,Ontologies,Robustness,Spine}
    \endentry
    \entry{drivendata_open_nodate}{online}{}
      \name{author}{1}{}{%
        {{hash=af457902593ce4ea84f13959756d3f24}{%
           family={{DrivenData}},
           familyi={D\bibinitperiod}}}%
      }
      \strng{namehash}{af457902593ce4ea84f13959756d3f24}
      \strng{fullhash}{af457902593ce4ea84f13959756d3f24}
      \strng{bibnamehash}{af457902593ce4ea84f13959756d3f24}
      \strng{authorbibnamehash}{af457902593ce4ea84f13959756d3f24}
      \strng{authornamehash}{af457902593ce4ea84f13959756d3f24}
      \strng{authorfullhash}{af457902593ce4ea84f13959756d3f24}
      \field{sortinit}{D}
      \field{sortinithash}{6f385f66841fb5e82009dc833c761848}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{shorttitle}
      \field{abstract}{Can you map building footprints from drone imagery? This semantic segmentation challenge leverages computer vision and data from {OpenStreetMap} to support disaster risk management efforts in cities across Africa.}
      \field{langid}{english}
      \field{shorttitle}{Open Cities {AI} Challenge}
      \field{title}{Open Cities {AI} Challenge: Segmenting Buildings for Disaster Resilience}
      \field{titleaddon}{{DrivenData}}
      \field{urlday}{11}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{file}
      \verb Snapshot:/home/chris/Zotero/storage/RQP3YNPD/218.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/page/218/
      \endverb
      \verb{url}
      \verb https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/page/218/
      \endverb
    \endentry
    \entry{elsken_neural_2019}{article}{}
      \name{author}{3}{}{%
        {{hash=17831398c93648de0ce8c5bcfbbc9784}{%
           family={Elsken},
           familyi={E\bibinitperiod},
           given={Thomas},
           giveni={T\bibinitperiod}}}%
        {{hash=21923fb486ef4c63bad4c8fcac6cc124}{%
           family={Metzen},
           familyi={M\bibinitperiod},
           given={Jan\bibnamedelima Hendrik},
           giveni={J\bibinitperiod\bibinitdelim H\bibinitperiod}}}%
        {{hash=528d4af87fd2ecf5fb8a22db913ce088}{%
           family={Hutter},
           familyi={H\bibinitperiod},
           given={Frank},
           giveni={F\bibinitperiod}}}%
      }
      \strng{namehash}{ed1f11ecdd12a620b5f6316dbe235ed3}
      \strng{fullhash}{ed1f11ecdd12a620b5f6316dbe235ed3}
      \strng{bibnamehash}{ed1f11ecdd12a620b5f6316dbe235ed3}
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      \strng{authornamehash}{ed1f11ecdd12a620b5f6316dbe235ed3}
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      \field{sortinit}{E}
      \field{sortinithash}{8da8a182d344d5b9047633dfc0cc9131}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{issn}{1532-4435}
      \field{journaltitle}{The Journal of Machine Learning Research}
      \field{note}{Publisher: {JMLR}. org}
      \field{number}{1}
      \field{shortjournal}{The Journal of Machine Learning Research}
      \field{title}{Neural architecture search: A survey}
      \field{volume}{20}
      \field{year}{2019}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1997\bibrangedash 2017}
      \range{pages}{21}
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    \entry{gevaert_evaluating_2018}{article}{}
      \name{author}{4}{}{%
        {{hash=075e2fe0a19fdc5f44a8f2249b4e148e}{%
           family={Gevaert},
           familyi={G\bibinitperiod},
           given={Caroline\bibnamedelima M.},
           giveni={C\bibinitperiod\bibinitdelim M\bibinitperiod}}}%
        {{hash=0ab163e3e17c1b8a92f048e5c5a761ea}{%
           family={Sliuzas},
           familyi={S\bibinitperiod},
           given={Richard},
           giveni={R\bibinitperiod}}}%
        {{hash=bba94f32ed6d61df8dcd638e7c7e9214}{%
           family={Persello},
           familyi={P\bibinitperiod},
           given={Claudio},
           giveni={C\bibinitperiod}}}%
        {{hash=18adac25454fa9510bf776a597fd28c7}{%
           family={Vosselman},
           familyi={V\bibinitperiod},
           given={George},
           giveni={G\bibinitperiod}}}%
      }
      \strng{namehash}{884e71370ce5a20e2775cc5e896268d1}
      \strng{fullhash}{950de2ee930c2cee3567d7f8e7569aea}
      \strng{bibnamehash}{884e71370ce5a20e2775cc5e896268d1}
      \strng{authorbibnamehash}{884e71370ce5a20e2775cc5e896268d1}
      \strng{authornamehash}{884e71370ce5a20e2775cc5e896268d1}
      \strng{authorfullhash}{950de2ee930c2cee3567d7f8e7569aea}
      \field{sortinit}{G}
      \field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{abstract}{Unmanned Aerial Vehicles ({UAVs}), or drones, have been gaining enormous popularity for many applications including informal settlement upgrading. Although {UAVs} can be used to efficiently collect highly detailed geospatial information, there are concerns regarding the ethical implications of its usage and the potential misuse of data. The aim of this study is therefore to evaluate the societal impacts of using {UAVs} for informal settlement mapping through two case studies in Eastern Africa. We discuss how the geospatial information they provide is beneficial from a technical perspective and analyze how the use of {UAVs} can be aligned with the values of: participation, empowerment, accountability, transparency, and equity. The local concept of privacy is investigated by asking citizens of the informal settlements to identify objects appearing in {UAV} images which they consider to be sensitive or private. As such, our research is an explicit example of how to increase citizen participation in the discussion of geospatial data security and privacy issues over urban areas and provides a framework of strategies illustrating how such issues can be addressed.}
      \field{journaltitle}{{ISPRS} International Journal of Geo-Information}
      \field{langid}{english}
      \field{month}{3}
      \field{note}{Number: 3 Publisher: Multidisciplinary Digital Publishing Institute}
      \field{number}{3}
      \field{title}{Evaluating the Societal Impact of Using Drones to Support Urban Upgrading Projects}
      \field{urlday}{13}
      \field{urlmonth}{1}
      \field{urlyear}{2022}
      \field{volume}{7}
      \field{year}{2018}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{91}
      \range{pages}{1}
      \verb{doi}
      \verb 10.3390/ijgi7030091
      \endverb
      \verb{file}
      \verb Full Text PDF:/home/chris/Zotero/storage/LNYIYCYH/Gevaert et al. - 2018 - Evaluating the Societal Impact of Using Drones to .pdf:application/pdf;Snapshot:/home/chris/Zotero/storage/9PVJH2TV/91.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://www.mdpi.com/2220-9964/7/3/91
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      \verb{url}
      \verb https://www.mdpi.com/2220-9964/7/3/91
      \endverb
      \keyw{informal settlements,privacy,social impact,Unmanned Aerial Vehicles ({UAVs}),urban planning}
    \endentry
    \entry{gonzalez_igital_2002}{book}{}
      \name{author}{2}{}{%
        {{hash=3c7716d0678056de3cb1756f7b0a8b3b}{%
           family={Gonzalez},
           familyi={G\bibinitperiod},
           given={R},
           giveni={R\bibinitperiod}}}%
        {{hash=a906fd57d0ee1e83e6027050d6460e4f}{%
           family={Woods},
           familyi={W\bibinitperiod},
           given={R},
           giveni={R\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {Prentice Hall}%
      }
      \strng{namehash}{10d8df1258467ba258cf8db9a5a7de81}
      \strng{fullhash}{10d8df1258467ba258cf8db9a5a7de81}
      \strng{bibnamehash}{10d8df1258467ba258cf8db9a5a7de81}
      \strng{authorbibnamehash}{10d8df1258467ba258cf8db9a5a7de81}
      \strng{authornamehash}{10d8df1258467ba258cf8db9a5a7de81}
      \strng{authorfullhash}{10d8df1258467ba258cf8db9a5a7de81}
      \field{sortinit}{G}
      \field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{edition}{2}
      \field{isbn}{0-201-18075-8}
      \field{title}{igital Image {OProcessing}}
      \field{year}{2002}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{gross_dzaleka_nodate}{online}{}
      \name{author}{1}{}{%
        {{hash=91b0fbf4b755e020a4a03de50d868524}{%
           family={Gross},
           familyi={G\bibinitperiod},
           given={Gerhard},
           giveni={G\bibinitperiod}}}%
      }
      \strng{namehash}{91b0fbf4b755e020a4a03de50d868524}
      \strng{fullhash}{91b0fbf4b755e020a4a03de50d868524}
      \strng{bibnamehash}{91b0fbf4b755e020a4a03de50d868524}
      \strng{authorbibnamehash}{91b0fbf4b755e020a4a03de50d868524}
      \strng{authornamehash}{91b0fbf4b755e020a4a03de50d868524}
      \strng{authorfullhash}{91b0fbf4b755e020a4a03de50d868524}
      \field{sortinit}{G}
      \field{sortinithash}{32d67eca0634bf53703493fb1090a2e8}
      \field{labelnamesource}{author}
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      \field{langid}{german}
      \field{title}{Dzaleka Refugee Camp Extension – Werner Schellenberg}
      \field{urlday}{17}
      \field{urlmonth}{6}
      \field{urlyear}{2022}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{file}
      \verb Snapshot:/home/chris/Zotero/storage/DANQB897/dzaleka-refugee-camp-extension.html:text/html
      \endverb
      \verb{urlraw}
      \verb http://werner-schellenberg.de/dzaleka-refugee-camp-extension
      \endverb
      \verb{url}
      \verb http://werner-schellenberg.de/dzaleka-refugee-camp-extension
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    \entry{un-habitat_kakuma_2021}{report}{}
      \name{author}{1}{}{%
        {{hash=acbc918c837626e652b24fb105fd4437}{%
           family={{UN-HABITAT}},
           familyi={U\bibinitperiod}}}%
      }
      \list{institution}{1}{%
        {UN}-{HABITAT}%
      }
      \list{location}{1}{%
        {Nairobi}%
      }
      \strng{namehash}{acbc918c837626e652b24fb105fd4437}
      \strng{fullhash}{acbc918c837626e652b24fb105fd4437}
      \strng{bibnamehash}{acbc918c837626e652b24fb105fd4437}
      \strng{authorbibnamehash}{acbc918c837626e652b24fb105fd4437}
      \strng{authornamehash}{acbc918c837626e652b24fb105fd4437}
      \strng{authorfullhash}{acbc918c837626e652b24fb105fd4437}
      \field{sortinit}{H}
      \field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{month}{6}
      \field{title}{Kakuma and Kalobeyei Spatial Profile}
      \field{year}{2021}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{he_control_2019}{inproceedings}{}
      \name{author}{3}{}{%
        {{hash=36d2ca8671a7bfe92b62deb66bcf45c8}{%
           family={He},
           familyi={H\bibinitperiod},
           given={Fengxiang},
           giveni={F\bibinitperiod}}}%
        {{hash=63fd394717cccfabe1cc081a643b09ea}{%
           family={Liu},
           familyi={L\bibinitperiod},
           given={Tongliang},
           giveni={T\bibinitperiod}}}%
        {{hash=f15b34a2bc944a9f055fd6e2e3a7c460}{%
           family={Tao},
           familyi={T\bibinitperiod},
           given={Dacheng},
           giveni={D\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {Curran Associates, Inc.}%
      }
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      \field{booktitle}{Advances in Neural Information Processing Systems}
      \field{shorttitle}{Control Batch Size and Learning Rate to Generalize Well}
      \field{title}{Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence}
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      \field{urlyear}{2022}
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      \field{year}{2019}
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      \verb https://proceedings.neurips.cc/paper/2019/hash/dc6a70712a252123c40d2adba6a11d84-Abstract.html
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      \verb{url}
      \verb https://proceedings.neurips.cc/paper/2019/hash/dc6a70712a252123c40d2adba6a11d84-Abstract.html
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      \field{abstract}{We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-{CNN}, extends Faster R-{CNN} by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-{CNN} is simple to train and adds only a small overhead to Faster R-{CNN}, running at 5 fps. Moreover, Mask R-{CNN} is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the {COCO} suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-{CNN} outperforms all existing, single-model entries on every task, including the {COCO} 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code has been made available at: https://github.com/facebookresearch/Detectron}
      \field{day}{24}
      \field{eprinttype}{arxiv}
      \field{journaltitle}{{arXiv}:1703.06870 [cs]}
      \field{month}{1}
      \field{title}{Mask R-{CNN}}
      \field{urlday}{27}
      \field{urlmonth}{11}
      \field{urlyear}{2020}
      \field{year}{2018}
      \field{dateera}{ce}
      \field{urldateera}{ce}
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      \verb{eprint}
      \verb 1703.06870
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      \verb{file}
      \verb arXiv.org Snapshot:/home/chris/Zotero/storage/IYFC9AN8/1703.html:text/html;arXiv Fulltext PDF:/home/chris/Zotero/storage/AJ5G2QB9/He et al. - 2018 - Mask R-CNN.pdf:application/pdf
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      \verb{urlraw}
      \verb http://arxiv.org/abs/1703.06870
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      \verb{url}
      \verb http://arxiv.org/abs/1703.06870
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      \keyw{Computer Science - Computer Vision and Pattern Recognition}
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    \entry{herfort_mapping_2019}{article}{}
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        {{hash=91b91d97f70f5f0eff6b2e0b705592aa}{%
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        {{hash=308dda5782513ea81e85e19f65978829}{%
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      \field{abstract}{Reliable techniques to generate accurate data sets of human built-up areas at national, regional, and global scales are a key factor to monitor the implementation progress of the Sustainable Development Goals as defined by the United Nations. However, the scarce availability of accurate and up-to-date human settlement data remains a major challenge, e.g., for humanitarian organizations. In this paper, we investigated the complementary value of crowdsourcing and deep learning to fill the data gaps of existing earth observation-based ({EO}) products. To this end, we propose a novel workflow to combine deep learning ({DeepVGI}) and crowdsourcing ({MapSwipe}). Our strategy for allocating classification tasks to deep learning or crowdsourcing is based on confidence of the derived binary classification. We conducted case studies in three different sites located in Guatemala, Laos, and Malawi to evaluate the proposed workflow. Our study reveals that crowdsourcing and deep learning outperform existing {EO}-based approaches and products such as the Global Urban Footprint. Compared to a crowdsourcing-only approach, the combination increased the quality (measured by Matthew’s correlation coefficient) of the generated human settlement maps by 3 to 5 percentage points. At the same time, it reduced the volunteer efforts needed by at least 80 percentage points for all study sites. The study suggests that for the efficient creation of human settlement maps, we should rely on human skills when needed and rely on automated approaches when possible.}
      \field{issn}{2072-4292}
      \field{journaltitle}{Remote Sensing}
      \field{langid}{english}
      \field{month}{1}
      \field{note}{Number: 15 Publisher: Multidisciplinary Digital Publishing Institute}
      \field{number}{15}
      \field{title}{Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning}
      \field{urlday}{6}
      \field{urlmonth}{6}
      \field{urlyear}{2022}
      \field{volume}{11}
      \field{year}{2019}
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      \field{pages}{1799}
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      \verb{file}
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      \endverb
      \verb{urlraw}
      \verb https://www.mdpi.com/2072-4292/11/15/1799
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      \verb{url}
      \verb https://www.mdpi.com/2072-4292/11/15/1799
      \endverb
      \keyw{deep learning,human settlements,building detection,crowdsourcing,humanitarian mapping,volunteered geographic information}
    \endentry
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        {{hash=91b91d97f70f5f0eff6b2e0b705592aa}{%
           family={Lautenbach},
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        {{hash=e5b3936967203dd8a7984cf4ad08954d}{%
           family={Albuquerque},
           familyi={A\bibinitperiod},
           given={João\bibnamedelima Porto},
           giveni={J\bibinitperiod\bibinitdelim P\bibinitperiod},
           prefix={de},
           prefixi={d\bibinitperiod}}}%
        {{hash=ec90a0788517cd8f83956d9e5c536b85}{%
           family={Anderson},
           familyi={A\bibinitperiod},
           given={Jennings},
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        {{hash=308dda5782513ea81e85e19f65978829}{%
           family={Zipf},
           familyi={Z\bibinitperiod},
           given={Alexander},
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      }
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      \field{issn}{2045-2322}
      \field{journaltitle}{Scientific reports}
      \field{note}{Publisher: Nature Publishing Group}
      \field{number}{1}
      \field{shortjournal}{Scientific reports}
      \field{title}{The evolution of humanitarian mapping within the {OpenStreetMap} community}
      \field{volume}{11}
      \field{year}{2021}
      \field{dateera}{ce}
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      \field{pages}{1\bibrangedash 15}
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           given={Thorsten},
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        {{hash=61b3720be6312d7db0f23f6c49a8e3fa}{%
           family={Bachofer},
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           given={Felix},
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        {{hash=215f37483a6db1e62f1ae27dcb385172}{%
           family={Kuenzer},
           familyi={K\bibinitperiod},
           given={Claudia},
           giveni={C\bibinitperiod}}}%
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      \field{journaltitle}{Remote Sensing}
      \field{note}{Publisher: Multidisciplinary Digital Publishing Institute}
      \field{number}{18}
      \field{shorttitle}{Object detection and image segmentation with deep learning on Earth observation data}
      \field{title}{Object detection and image segmentation with deep learning on Earth observation data: A review—Part {II}: Applications}
      \field{volume}{12}
      \field{year}{2020}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{3053}
      \range{pages}{1}
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    \entry{hoeser_object_2020-1}{article}{}
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        {{hash=27b42ec522604c6d4ebfbd1d7e506194}{%
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           given={Thorsten},
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        {{hash=215f37483a6db1e62f1ae27dcb385172}{%
           family={Kuenzer},
           familyi={K\bibinitperiod},
           given={Claudia},
           giveni={C\bibinitperiod}}}%
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      \strng{namehash}{7de7ee1c79ac120d5b6694293ac6cf1e}
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      \field{journaltitle}{Remote Sensing}
      \field{note}{Publisher: Multidisciplinary Digital Publishing Institute}
      \field{number}{10}
      \field{shorttitle}{Object detection and image segmentation with deep learning on earth observation data}
      \field{title}{Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends}
      \field{volume}{12}
      \field{year}{2020}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1667}
      \range{pages}{1}
    \endentry
    \entry{hofmann_monitoring_2015}{inproceedings}{}
      \name{author}{3}{}{%
        {{hash=890a3054378dfc068ee78e2f5002fa5c}{%
           family={Hofmann},
           familyi={H\bibinitperiod},
           given={Peter},
           giveni={P\bibinitperiod}}}%
        {{hash=83f8976204b907d38b06e2acffa27c2b}{%
           family={Taubenböck},
           familyi={T\bibinitperiod},
           given={Hannes},
           giveni={H\bibinitperiod}}}%
        {{hash=8f370da3557491f1b3ba98ef385461a0}{%
           family={Werthmann},
           familyi={W\bibinitperiod},
           given={Christian},
           giveni={C\bibinitperiod}}}%
      }
      \strng{namehash}{8ca79e7e3208193f74328eb6697ca192}
      \strng{fullhash}{8ca79e7e3208193f74328eb6697ca192}
      \strng{bibnamehash}{8ca79e7e3208193f74328eb6697ca192}
      \strng{authorbibnamehash}{8ca79e7e3208193f74328eb6697ca192}
      \strng{authornamehash}{8ca79e7e3208193f74328eb6697ca192}
      \strng{authorfullhash}{8ca79e7e3208193f74328eb6697ca192}
      \field{sortinit}{H}
      \field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{abstract}{The increasing development of informal settlements and slums in the years to come is a humanitarian challenge we are facing worldwide. The reasons for this development are mainly given by an urbanization of poverty and the lack of affordable housing in urban areas. Consequently, the majority of slum dwellers are forced to live in housing conditions which are simply unacceptable. In order to fight urban poverty and develop strategies to reduce the number of slum dwellers it is essential to inventory and monitor slums and to understand the underlying mechanisms of slum genesis. However, systematic spatial knowledge about these mechanisms is still absent. This paper addresses the capabilities of mapping and structural analysis of informal settlements using remote sensing data and modelling their genesis based on agent based approaches.}
      \field{booktitle}{2015 Joint Urban Remote Sensing Event ({JURSE})}
      \field{eventtitle}{2015 Joint Urban Remote Sensing Event ({JURSE})}
      \field{month}{3}
      \field{note}{{ISSN}: 2334-0932}
      \field{title}{Monitoring and modelling of informal settlements - A review on recent developments and challenges}
      \field{year}{2015}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1\bibrangedash 4}
      \range{pages}{4}
      \verb{doi}
      \verb 10.1109/JURSE.2015.7120513
      \endverb
      \verb{file}
      \verb IEEE Xplore Abstract Record:/home/chris/Zotero/storage/ZAHRMF3A/7120513.html:text/html
      \endverb
      \keyw{Vegetation mapping,Monitoring,Analytical models,Bayes methods,Biological system modeling,Information science,Sensors}
    \endentry
    \entry{hopfield_neural_1982}{article}{}
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        {{hash=9358686fc958d08cb3cffdebe832f513}{%
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           familyi={H\bibinitperiod},
           given={J.\bibnamedelimi J.},
           giveni={J\bibinitperiod\bibinitdelim J\bibinitperiod}}}%
      }
      \strng{namehash}{9358686fc958d08cb3cffdebe832f513}
      \strng{fullhash}{9358686fc958d08cb3cffdebe832f513}
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      \strng{authorfullhash}{9358686fc958d08cb3cffdebe832f513}
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      \field{sortinithash}{23a3aa7c24e56cfa16945d55545109b5}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{abstract}{Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.}
      \field{day}{1}
      \field{issn}{0027-8424, 1091-6490}
      \field{journaltitle}{Proceedings of the National Academy of Sciences}
      \field{langid}{english}
      \field{month}{4}
      \field{number}{8}
      \field{shortjournal}{{PNAS}}
      \field{title}{Neural networks and physical systems with emergent collective computational abilities}
      \field{urlday}{1}
      \field{urlmonth}{2}
      \field{urlyear}{2020}
      \field{volume}{79}
      \field{year}{1982}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{2554\bibrangedash 2558}
      \range{pages}{5}
      \verb{doi}
      \verb 10.1073/pnas.79.8.2554
      \endverb
      \verb{file}
      \verb Snapshot:/home/chris/Zotero/storage/RGDUGIFX/2554.html:text/html;Full Text PDF:/home/chris/Zotero/storage/GL5LCKDL/Hopfield - 1982 - Neural networks and physical systems with emergent.pdf:application/pdf
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      \verb{urlraw}
      \verb https://www.pnas.org/content/79/8/2554
      \endverb
      \verb{url}
      \verb https://www.pnas.org/content/79/8/2554
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    \endentry
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      \list{institution}{1}{%
        {HOTOSM}%
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      \list{location}{1}{%
        {Washington D. C.}%
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      \field{month}{6}
      \field{title}{Annual Report}
      \field{year}{2021}
      \field{dateera}{ce}
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      \verb{url}
      \verb https://stories.hotosm.org/humanitarian-openstreetmap-team/#section-Annual-Report-ILOtXcst0a
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        {{hash=3199c0799a0576dc2e8ff87efd9b838c}{%
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           given={Nicholas},
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      \field{abstract}{Local integration has long been seen as the “forgotten” durable solution to refugee displacement1 evidenced by the reluctance of governments across the world to accord refugees a new citizenship. This article goes further. It argues that local integration as a durable solution has not been merely forgotten, but deliberately avoided at a national, regional and international level. As a result, its veracity as a realistic durable solution for the majority of refugees is now in question.The article examines ways in which states seek to evade local integration. It begins by investigating the multiple tactics used by wealthier governments to elude responsibility both at a national level and through the influence they exert over global refugee responses. It then explores how countries hosting the greatest numbers of refugees, with a specific focus on Africa, have allowed significant numbers of refugees into their territory but have then maintained a short-term approach that has, in practice, blocked local integration as a durable solution. We argue that a mix of global, national, and local processes and forces have effectively conspired to diminish local integration as a durable solution to the point that it has all but vanished from the political arena. The implications for refugee populations of these processes and forces – talked of collectively as the politics of evasion – are profound.While refugees continue to find ways to negotiate their own access to communities and labour markets, this is often done against national, regional, and international policies rather than with them. Ultimately, by highlighting its value as a durable solution, while showing that there is almost uniform acceptance by states and international organisations working on protection concerns that it is no longer politically viable, this article hopes to restart an urgent conversation about the value of local integration and how it can be reinvigorated.}
      \field{day}{1}
      \field{issn}{1020-4067}
      \field{journaltitle}{Refugee Survey Quarterly}
      \field{month}{6}
      \field{number}{2}
      \field{shortjournal}{Refugee Survey Quarterly}
      \field{shorttitle}{Local Integration}
      \field{title}{Local Integration: A Durable Solution in need of Restoration?}
      \field{urlday}{6}
      \field{urlmonth}{6}
      \field{urlyear}{2022}
      \field{volume}{41}
      \field{year}{2022}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{238\bibrangedash 266}
      \range{pages}{29}
      \verb{doi}
      \verb 10.1093/rsq/hdac008
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      \verb{file}
      \verb Full Text PDF:/home/chris/Zotero/storage/K7VHZ8RA/Hovil and Maple - 2022 - Local Integration A Durable Solution in need of R.pdf:application/pdf;Snapshot:/home/chris/Zotero/storage/8PVWNK2C/6564683.html:text/html
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      \verb{url}
      \verb https://doi.org/10.1093/rsq/hdac008
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    \endentry
    \entry{howard_deep_2020}{book}{}
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           family={Howard},
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           given={Jeremy},
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        {{hash=e8a58125af15e56d1db521647af99086}{%
           family={Gugger},
           familyi={G\bibinitperiod},
           given={Sylvain},
           giveni={S\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {O'Reilly Media}%
      }
      \strng{namehash}{2a7259908a2cbe9913ec19b7633e3136}
      \strng{fullhash}{2a7259908a2cbe9913ec19b7633e3136}
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      \field{labeltitlesource}{title}
      \field{isbn}{1-4920-4549-7}
      \field{title}{Deep Learning for Coders with fastai and {PyTorch}}
      \field{year}{2020}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{ifc_kakuma_2018}{report}{}
      \name{author}{1}{}{%
        {{hash=ed8f585f62cf3201342c060dac642ce3}{%
           family={{IFC}},
           familyi={I\bibinitperiod}}}%
      }
      \list{institution}{1}{%
        {World Bank}%
      }
      \list{location}{1}{%
        {Washington D. C.}%
      }
      \strng{namehash}{ed8f585f62cf3201342c060dac642ce3}
      \strng{fullhash}{ed8f585f62cf3201342c060dac642ce3}
      \strng{bibnamehash}{ed8f585f62cf3201342c060dac642ce3}
      \strng{authorbibnamehash}{ed8f585f62cf3201342c060dac642ce3}
      \strng{authornamehash}{ed8f585f62cf3201342c060dac642ce3}
      \strng{authorfullhash}{ed8f585f62cf3201342c060dac642ce3}
      \field{sortinit}{I}
      \field{sortinithash}{8d291c51ee89b6cd86bf5379f0b151d8}
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      \field{day}{1}
      \field{month}{4}
      \field{number}{125918}
      \field{title}{Kakuma as a marketplace: a consumer and market study of a refugee camp and town in northwest Kenya}
      \field{year}{2018}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{jean_combining_2016}{article}{}
      \name{author}{6}{}{%
        {{hash=3ea148b8bd8e9560ec6781fe36d3baec}{%
           family={Jean},
           familyi={J\bibinitperiod},
           given={N.},
           giveni={N\bibinitperiod}}}%
        {{hash=31d4f92abc49e335bd0880284dee11a2}{%
           family={Burke},
           familyi={B\bibinitperiod},
           given={M.},
           giveni={M\bibinitperiod}}}%
        {{hash=cd95222338172c4d844983a51838755a}{%
           family={Xie},
           familyi={X\bibinitperiod},
           given={M.},
           giveni={M\bibinitperiod}}}%
        {{hash=0131eda282be8fefe17598e3ed15e5ec}{%
           family={Davis},
           familyi={D\bibinitperiod},
           given={W.\bibnamedelimi M.},
           giveni={W\bibinitperiod\bibinitdelim M\bibinitperiod}}}%
        {{hash=3d02cc98851288294ae55b7c5c9ea4e4}{%
           family={Lobell},
           familyi={L\bibinitperiod},
           given={D.\bibnamedelimi B.},
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        {{hash=e33030733b84541bfe83b7317769b79a}{%
           family={Ermon},
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           given={S.},
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      \strng{namehash}{5e9d9683075c01299959ca66926b1f5d}
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      \field{day}{19}
      \field{issn}{0036-8075, 1095-9203}
      \field{journaltitle}{Science}
      \field{langid}{english}
      \field{month}{8}
      \field{number}{6301}
      \field{shortjournal}{Science}
      \field{title}{Combining satellite imagery and machine learning to predict poverty}
      \field{urlday}{8}
      \field{urlmonth}{2}
      \field{urlyear}{2020}
      \field{volume}{353}
      \field{year}{2016}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{790\bibrangedash 794}
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      \verb{doi}
      \verb 10.1126/science.aaf7894
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      \verb{urlraw}
      \verb https://www.sciencemag.org/lookup/doi/10.1126/science.aaf7894
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      \verb{url}
      \verb https://www.sciencemag.org/lookup/doi/10.1126/science.aaf7894
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    \endentry
    \entry{kavalo_environmental_2016}{thesis}{}
      \name{author}{1}{}{%
        {{hash=d968e9417c00a3e254a4161fa37d35be}{%
           family={Kavalo},
           familyi={K\bibinitperiod},
           given={Eddie\bibnamedelima Bright},
           giveni={E\bibinitperiod\bibinitdelim B\bibinitperiod}}}%
      }
      \list{institution}{1}{%
        {University of South Africa}%
      }
      \list{location}{1}{%
        {Pretoria}%
      }
      \strng{namehash}{d968e9417c00a3e254a4161fa37d35be}
      \strng{fullhash}{d968e9417c00a3e254a4161fa37d35be}
      \strng{bibnamehash}{d968e9417c00a3e254a4161fa37d35be}
      \strng{authorbibnamehash}{d968e9417c00a3e254a4161fa37d35be}
      \strng{authornamehash}{d968e9417c00a3e254a4161fa37d35be}
      \strng{authorfullhash}{d968e9417c00a3e254a4161fa37d35be}
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      \field{sortinithash}{c02bf6bff1c488450c352b40f5d853ab}
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      \field{labeltitlesource}{shorttitle}
      \field{abstract}{The opening of the refugee camp in Dowa by the Malawi Government, with support from {UNHCR} meant that the population of that area was increased abruptly. This led to an increase in socio- economic activities resulting into high demand of energy, food and other amenities from the natural environment. The impact of the refugees on the host community and their relationship was central in this research. The main aim of the study was to assess the environmental and socio-economic impacts for hosting refugees at the Dzaleka Refugee Camp in Dowa. The study used both quantitative and qualitative methods in data collection. A structured questionnaire, focus group discussions and key informant interviews were used to collect data and analysed using the Statistical Package for the Social Sciences ({SPSS}) Version 16.0. In total, 237 household heads and 6 key informants were interviewed. In addition, 4 focus group discussions were conducted. Qualitative data, collected through focus group discussions helped in explaining and understanding the results from the questionnaire. The most evident environmental impacts reported by respondents were: deforestation and firewood depletion; land degradation and water pollution. It is important to note that such environmental impacts can affect the long-term livelihood opportunities of both refugees and host population. The camp establishment has had socio-economic impacts regarded as positive by the majority of the respondents. Although most hosts still struggle to survive, the camp has created a larger market for generating income and better opportunities to provide basic needs such as food and water. The majority of host respondents use the refugee camp for providing livelihoods. Most respondents reported that refugees are regularly benefitting from privileged access to resources unavailable to the local host population. In this respect, refugees at Dzaleka were offered opportunities for education, literacy, vocational training, health and basic livelihood. The most reported negative social impacts are exposure to more conflicts and increased insecurity. Both of these impacts relate to the relationship between the host community and refugee population.}
      \field{langid}{english}
      \field{month}{11}
      \field{note}{Accepted: 2017-03-17T08:09:37Z}
      \field{shorttitle}{Environmental and socio-economic impact of hosting refugees}
      \field{title}{Environmental and socio-economic impact of hosting refugees : a case study of villages around the Dzaleka refugee camp in Dowa district, Malawi}
      \field{type}{Master of Science}
      \field{urlday}{10}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{year}{2016}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{file}
      \verb Snapshot:/home/chris/Zotero/storage/ZQLC4I9R/22174.html:text/html;Full Text PDF:/home/chris/Zotero/storage/29K4X857/Kavalo - 2016 - Environmental and socio-economic impact of hosting.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb https://uir.unisa.ac.za/handle/10500/22174
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      \verb{url}
      \verb https://uir.unisa.ac.za/handle/10500/22174
      \endverb
    \endentry
    \entry{kingma_adam_2017}{article}{}
      \name{author}{2}{}{%
        {{hash=b6fbd171848aad4edf3925543f1f1522}{%
           family={Kingma},
           familyi={K\bibinitperiod},
           given={Diederik\bibnamedelima P.},
           giveni={D\bibinitperiod\bibinitdelim P\bibinitperiod}}}%
        {{hash=8aa66e8231cc2fdbe67aa4f18ca970c6}{%
           family={Ba},
           familyi={B\bibinitperiod},
           given={Jimmy},
           giveni={J\bibinitperiod}}}%
      }
      \strng{namehash}{a09df9f123146b8e2c7f1134c9496932}
      \strng{fullhash}{a09df9f123146b8e2c7f1134c9496932}
      \strng{bibnamehash}{a09df9f123146b8e2c7f1134c9496932}
      \strng{authorbibnamehash}{a09df9f123146b8e2c7f1134c9496932}
      \strng{authornamehash}{a09df9f123146b8e2c7f1134c9496932}
      \strng{authorfullhash}{a09df9f123146b8e2c7f1134c9496932}
      \field{sortinit}{K}
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      \field{abstract}{We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss {AdaMax}, a variant of Adam based on the infinity norm.}
      \field{day}{29}
      \field{eprinttype}{arxiv}
      \field{journaltitle}{{arXiv}:1412.6980 [cs]}
      \field{langid}{english}
      \field{month}{1}
      \field{shorttitle}{Adam}
      \field{title}{Adam: A Method for Stochastic Optimization}
      \field{urlday}{12}
      \field{urlmonth}{2}
      \field{urlyear}{2020}
      \field{year}{2017}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{eprint}
      \verb 1412.6980
      \endverb
      \verb{file}
      \verb Kingma and Ba - 2017 - Adam A Method for Stochastic Optimization.pdf:/home/chris/Zotero/storage/IJP7Z9UE/Kingma and Ba - 2017 - Adam A Method for Stochastic Optimization.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb http://arxiv.org/abs/1412.6980
      \endverb
      \verb{url}
      \verb http://arxiv.org/abs/1412.6980
      \endverb
      \keyw{Computer Science - Machine Learning}
    \endentry
    \entry{kinsley_neural_2020}{book}{}
      \name{author}{2}{}{%
        {{hash=b899dd84e284163139a7782ff51b7bac}{%
           family={Kinsley},
           familyi={K\bibinitperiod},
           given={Harrison},
           giveni={H\bibinitperiod}}}%
        {{hash=3e1f1f3cc24aa1875a2e43b171787b7b}{%
           family={Kukieła},
           familyi={K\bibinitperiod},
           given={Daniel},
           giveni={D\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {Harrison Kinsley}%
      }
      \strng{namehash}{e0e9f3878f43bb6b45eccb59fdba0372}
      \strng{fullhash}{e0e9f3878f43bb6b45eccb59fdba0372}
      \strng{bibnamehash}{e0e9f3878f43bb6b45eccb59fdba0372}
      \strng{authorbibnamehash}{e0e9f3878f43bb6b45eccb59fdba0372}
      \strng{authornamehash}{e0e9f3878f43bb6b45eccb59fdba0372}
      \strng{authorfullhash}{e0e9f3878f43bb6b45eccb59fdba0372}
      \field{sortinit}{K}
      \field{sortinithash}{c02bf6bff1c488450c352b40f5d853ab}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{title}{Neural Networks from Scratch in Python}
      \field{year}{2020}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{krizhevsky_imagenet_2012}{inproceedings}{}
      \name{author}{3}{}{%
        {{hash=c5e3a676e2ac1164b3afcd539c131fc9}{%
           family={Krizhevsky},
           familyi={K\bibinitperiod},
           given={Alex},
           giveni={A\bibinitperiod}}}%
        {{hash=8d569d1d5b8b5a7836017a98b430f959}{%
           family={Sutskever},
           familyi={S\bibinitperiod},
           given={Ilya},
           giveni={I\bibinitperiod}}}%
        {{hash=813bd95fe553e6079cd53a567b238287}{%
           family={Hinton},
           familyi={H\bibinitperiod},
           given={Geoffrey\bibnamedelima E},
           giveni={G\bibinitperiod\bibinitdelim E\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {Curran Associates, Inc.}%
      }
      \strng{namehash}{1a23c09aa65b3c2ade45ed18d8127375}
      \strng{fullhash}{1a23c09aa65b3c2ade45ed18d8127375}
      \strng{bibnamehash}{1a23c09aa65b3c2ade45ed18d8127375}
      \strng{authorbibnamehash}{1a23c09aa65b3c2ade45ed18d8127375}
      \strng{authornamehash}{1a23c09aa65b3c2ade45ed18d8127375}
      \strng{authorfullhash}{1a23c09aa65b3c2ade45ed18d8127375}
      \field{sortinit}{K}
      \field{sortinithash}{c02bf6bff1c488450c352b40f5d853ab}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{abstract}{We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the {LSVRC}-2010 {ImageNet} training set into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 39.7{\textbackslash}\% and 18.9{\textbackslash}\% which is considerably better than the previous state-of-the-art results. The neural network, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient {GPU} implementation of convolutional nets. To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective.}
      \field{booktitle}{Advances in Neural Information Processing Systems}
      \field{title}{{ImageNet} Classification with Deep Convolutional Neural Networks}
      \field{urlday}{29}
      \field{urlmonth}{8}
      \field{urlyear}{2022}
      \field{volume}{25}
      \field{year}{2012}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{file}
      \verb Full Text PDF:/home/chris/Zotero/storage/D44Q686T/Krizhevsky et al. - 2012 - ImageNet Classification with Deep Convolutional Ne.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
      \endverb
      \verb{url}
      \verb https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
      \endverb
    \endentry
    \entry{kuffer_development_2014}{article}{}
      \name{author}{3}{}{%
        {{hash=2e31d45cb8f4df38b638b412fc143f2c}{%
           family={Kuffer},
           familyi={K\bibinitperiod},
           given={Monika},
           giveni={M\bibinitperiod}}}%
        {{hash=b49dd6b973175138531cc3f138df83dd}{%
           family={Barros},
           familyi={B\bibinitperiod},
           given={Joana},
           giveni={J\bibinitperiod}}}%
        {{hash=9351270794d9772cb4aff329058f7f49}{%
           family={Sliuzas},
           familyi={S\bibinitperiod},
           given={Richard\bibnamedelima V.},
           giveni={R\bibinitperiod\bibinitdelim V\bibinitperiod}}}%
      }
      \strng{namehash}{a768a189474cea453da4cf49513c69d1}
      \strng{fullhash}{a768a189474cea453da4cf49513c69d1}
      \strng{bibnamehash}{a768a189474cea453da4cf49513c69d1}
      \strng{authorbibnamehash}{a768a189474cea453da4cf49513c69d1}
      \strng{authornamehash}{a768a189474cea453da4cf49513c69d1}
      \strng{authorfullhash}{a768a189474cea453da4cf49513c69d1}
      \field{sortinit}{K}
      \field{sortinithash}{c02bf6bff1c488450c352b40f5d853ab}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{abstract}{Spatial metrics combined with spectral information extracted from very-high-resolution ({VHR}) imagery allow quantification of the general spatial characteristics of urban areas, as well as specific morphological features (i.e., density, size, and pattern) of unplanned settlements. Such morphological features are visible in {VHR} imagery, but they are challenging to quantify. Still, quantification of the morphological differences between planned and unplanned areas is an important step towards automatic extraction of unplanned areas from {VHR} imagery. In this work, we discuss how image segmentation assists in the extraction of homogenous urban patches ({HUPs}), and use spatial metrics to quantify the morphological differences between planned and unplanned {HUPs}. A set of spatial metrics meaningful to describe morphological features of unplanned areas is selected and combined into an unplanned settlement index ({USI}) using a multi-criteria evaluation approach. Two case study areas are used to test the {USI}, i.e., Dar es Salaam, Tanzania, and New Delhi, India. The ability of the developed {USI} to extract unplanned areas is confirmed via visual comparison with existing land use data, and a quantitative accuracy assessment shows that areas of high {USI} coincide well with unplanned areas in the reference data. The quantitative accuracy assessment presents an accuracy of greater than 70\% for five selected test areas in both cities.}
      \field{day}{1}
      \field{issn}{0198-9715}
      \field{journaltitle}{Computers, Environment and Urban Systems}
      \field{langid}{english}
      \field{month}{11}
      \field{shortjournal}{Computers, Environment and Urban Systems}
      \field{title}{The development of a morphological unplanned settlement index using very-high-resolution ({VHR}) imagery}
      \field{urlday}{5}
      \field{urlmonth}{7}
      \field{urlyear}{2022}
      \field{volume}{48}
      \field{year}{2014}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{138\bibrangedash 152}
      \range{pages}{15}
      \verb{doi}
      \verb 10.1016/j.compenvurbsys.2014.07.012
      \endverb
      \verb{file}
      \verb Accepted Version:/home/chris/Zotero/storage/27HNSRM2/Kuffer et al. - 2014 - The development of a morphological unplanned settl.pdf:application/pdf;ScienceDirect Snapshot:/home/chris/Zotero/storage/L8Y6EU5P/S0198971514000933.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://www.sciencedirect.com/science/article/pii/S0198971514000933
      \endverb
      \verb{url}
      \verb https://www.sciencedirect.com/science/article/pii/S0198971514000933
      \endverb
      \keyw{Image segmentation,{VHR} imagery,Spatial metrics,Unplanned settlement index,Urban morphology}
    \endentry
    \entry{kuffer_slums_2016}{article}{}
      \name{author}{3}{}{%
        {{hash=2e31d45cb8f4df38b638b412fc143f2c}{%
           family={Kuffer},
           familyi={K\bibinitperiod},
           given={Monika},
           giveni={M\bibinitperiod}}}%
        {{hash=9ec612eaae3e0d5215b89f9b6c373ce0}{%
           family={Pfeffer},
           familyi={P\bibinitperiod},
           given={Karin},
           giveni={K\bibinitperiod}}}%
        {{hash=0ab163e3e17c1b8a92f048e5c5a761ea}{%
           family={Sliuzas},
           familyi={S\bibinitperiod},
           given={Richard},
           giveni={R\bibinitperiod}}}%
      }
      \strng{namehash}{cb671c82058b32c8154375a628e721bb}
      \strng{fullhash}{cb671c82058b32c8154375a628e721bb}
      \strng{bibnamehash}{cb671c82058b32c8154375a628e721bb}
      \strng{authorbibnamehash}{cb671c82058b32c8154375a628e721bb}
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      \strng{authorfullhash}{cb671c82058b32c8154375a628e721bb}
      \field{sortinit}{K}
      \field{sortinithash}{c02bf6bff1c488450c352b40f5d853ab}
      \field{labelnamesource}{author}
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      \field{abstract}{The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution ({VHR}) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000–2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical slum characteristics is required for the development of robust image-based proxies. Third, although the latest commercial sensor technologies provide image data of less than 0.5 m spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity in terms of extracted information levels (area or object based), implemented indicator sets (single or large sets) and methods employed (e.g., object-based image analysis ({OBIA}) or machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, {OBIA} approaches show good capabilities of extracting both area- and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.}
      \field{issn}{2072-4292}
      \field{journaltitle}{Remote Sensing}
      \field{langid}{english}
      \field{month}{6}
      \field{number}{6}
      \field{title}{Slums from Space—15 Years of Slum Mapping Using Remote Sensing}
      \field{urlday}{5}
      \field{urlmonth}{7}
      \field{urlyear}{2022}
      \field{volume}{8}
      \field{year}{2016}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{455}
      \range{pages}{1}
      \verb{doi}
      \verb 10.3390/rs8060455
      \endverb
      \verb{file}
      \verb Full Text PDF:/home/chris/Zotero/storage/CD4BZ4CG/Kuffer et al. - 2016 - Slums from Space—15 Years of Slum Mapping Using Re.pdf:application/pdf;Snapshot:/home/chris/Zotero/storage/5XDFAJCD/455.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://www.mdpi.com/2072-4292/8/6/455
      \endverb
      \verb{url}
      \verb https://www.mdpi.com/2072-4292/8/6/455
      \endverb
      \keyw{Global South,informal areas,slums,urban remote sensing,{VHR} imagery}
    \endentry
    \entry{kuffer_extraction_2016}{article}{}
      \name{author}{4}{}{%
        {{hash=2e31d45cb8f4df38b638b412fc143f2c}{%
           family={Kuffer},
           familyi={K\bibinitperiod},
           given={Monika},
           giveni={M\bibinitperiod}}}%
        {{hash=9ec612eaae3e0d5215b89f9b6c373ce0}{%
           family={Pfeffer},
           familyi={P\bibinitperiod},
           given={Karin},
           giveni={K\bibinitperiod}}}%
        {{hash=0ab163e3e17c1b8a92f048e5c5a761ea}{%
           family={Sliuzas},
           familyi={S\bibinitperiod},
           given={Richard},
           giveni={R\bibinitperiod}}}%
        {{hash=21bf3149d8e30040bc3a3d51d71b31e2}{%
           family={Baud},
           familyi={B\bibinitperiod},
           given={Isa},
           giveni={I\bibinitperiod}}}%
      }
      \strng{namehash}{4baec47c60781657601a56e1e8d2afa1}
      \strng{fullhash}{8fb0e3b5d5346e92cac7b374475b8494}
      \strng{bibnamehash}{4baec47c60781657601a56e1e8d2afa1}
      \strng{authorbibnamehash}{4baec47c60781657601a56e1e8d2afa1}
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      \field{sortinit}{K}
      \field{sortinithash}{c02bf6bff1c488450c352b40f5d853ab}
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      \field{labeltitlesource}{title}
      \field{abstract}{Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix ({GLCM}) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as {WorldView}-2, {OrbView}, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the {GLCM} shows that the variance of the {GLCM} combined with the normalized difference vegetation index ({NDVI}) is able to separate slums and formal areas. The overall accuracy achieved is 84\% in Kigali, 87\% in Mumbai, and 88\% in Ahmedabad. Furthermore, combining spectral information with the {GLCM} variance within a random forest classifier results in a pixel-based classification accuracy of 90\%. The final slum map, aggregated to homogenous urban patches ({HUPs}), shows an accuracy of 88\%-95\% for slum locations depending on the scale parameter.}
      \field{issn}{2151-1535}
      \field{journaltitle}{{IEEE} Journal of Selected Topics in Applied Earth Observations and Remote Sensing}
      \field{month}{5}
      \field{number}{5}
      \field{title}{Extraction of Slum Areas From {VHR} Imagery Using {GLCM} Variance}
      \field{volume}{9}
      \field{year}{2016}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1830\bibrangedash 1840}
      \range{pages}{11}
      \verb{doi}
      \verb 10.1109/JSTARS.2016.2538563
      \endverb
      \verb{file}
      \verb IEEE Xplore Abstract Record:/home/chris/Zotero/storage/3ZMVXWZ5/7447704.html:text/html
      \endverb
      \keyw{Buildings,Data mining,Earth,Feature extraction,Global South,gray-level co-occurrence matrix ({GLCM}),homogenous urban patches,image segmentation,Indexes,informal settlements,random forest classification,Roads,slums,texture,Urban areas,urban remote sensing,variance}
    \endentry
    \entry{kuffer_mapping_2021}{incollection}{}
      \name{author}{6}{}{%
        {{hash=2e31d45cb8f4df38b638b412fc143f2c}{%
           family={Kuffer},
           familyi={K\bibinitperiod},
           given={Monika},
           giveni={M\bibinitperiod}}}%
        {{hash=84916299ae549420d9251117abc278ac}{%
           family={Grippa},
           familyi={G\bibinitperiod},
           given={Taïs},
           giveni={T\bibinitperiod}}}%
        {{hash=bba94f32ed6d61df8dcd638e7c7e9214}{%
           family={Persello},
           familyi={P\bibinitperiod},
           given={Claudio},
           giveni={C\bibinitperiod}}}%
        {{hash=83f8976204b907d38b06e2acffa27c2b}{%
           family={Taubenböck},
           familyi={T\bibinitperiod},
           given={Hannes},
           giveni={H\bibinitperiod}}}%
        {{hash=9ec612eaae3e0d5215b89f9b6c373ce0}{%
           family={Pfeffer},
           familyi={P\bibinitperiod},
           given={Karin},
           giveni={K\bibinitperiod}}}%
        {{hash=0ab163e3e17c1b8a92f048e5c5a761ea}{%
           family={Sliuzas},
           familyi={S\bibinitperiod},
           given={Richard},
           giveni={R\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {John Wiley \& Sons, Ltd}%
      }
      \strng{namehash}{4baec47c60781657601a56e1e8d2afa1}
      \strng{fullhash}{95428cce1afc5c78a1335bb0da0e5241}
      \strng{bibnamehash}{4baec47c60781657601a56e1e8d2afa1}
      \strng{authorbibnamehash}{4baec47c60781657601a56e1e8d2afa1}
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      \field{sortinit}{K}
      \field{sortinithash}{c02bf6bff1c488450c352b40f5d853ab}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{abstract}{Summary Globally, about one billion urban dwellers live in deprived areas (commonly referred as slums). However, this figure may be highly uncertain due to large data gaps. For example, in many cities, systematic underreporting occurs, which hampers the monitoring of Sustainable Development Goal ({SDG}) indicators. Earth observation ({EO}) data can be used to extract consistent spatial information on important aspects of the physical domain of deprivation and can offer essential proxies to not well-covered (e.g. social and economic) domains. However, for the development of a global data repository on deprived areas, several conceptual and methodological issues need to be solved. First, the relationship between concepts of a slum household and a deprived area needs to be defined in the context of information available in {EO} images. Second, the costs and benefits of different types of {EO}-data need to be established. Third, at different scales (ranging from communities, city to global scales), meaningful spatial aggregation units need to be established that are suitable to deal with uncertainties, privacy, ethics, and user needs. Fourth, computationally feasible, scalable, and transferable methods are required to produce a global data repository on deprived areas. This chapter provides an overview of methodological advances to address the above four major challenges .}
      \field{booktitle}{Urban Remote Sensing}
      \field{isbn}{978-1-119-62586-5}
      \field{title}{Mapping the Morphology of Urban Deprivation}
      \field{year}{2021}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{305\bibrangedash 323}
      \range{pages}{19}
      \verb{urlraw}
      \verb https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119625865.ch14
      \endverb
      \verb{url}
      \verb https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119625865.ch14
      \endverb
      \keyw{{CNNs},deep-learning,deprived area,machine learning,slum}
    \endentry
    \entry{kuhn_structure_1962}{book}{}
      \name{author}{1}{}{%
        {{hash=d0261ae5373d7a2ba373aa72a2ddac43}{%
           family={Kuhn},
           familyi={K\bibinitperiod},
           given={Thomas\bibnamedelima S},
           giveni={T\bibinitperiod\bibinitdelim S\bibinitperiod}}}%
      }
      \list{location}{1}{%
        {London}%
      }
      \list{publisher}{1}{%
        {The University of Chicago Press}%
      }
      \strng{namehash}{d0261ae5373d7a2ba373aa72a2ddac43}
      \strng{fullhash}{d0261ae5373d7a2ba373aa72a2ddac43}
      \strng{bibnamehash}{d0261ae5373d7a2ba373aa72a2ddac43}
      \strng{authorbibnamehash}{d0261ae5373d7a2ba373aa72a2ddac43}
      \strng{authornamehash}{d0261ae5373d7a2ba373aa72a2ddac43}
      \strng{authorfullhash}{d0261ae5373d7a2ba373aa72a2ddac43}
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      \field{sortinithash}{c02bf6bff1c488450c352b40f5d853ab}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{edition}{2}
      \field{isbn}{0-226-45803-2}
      \field{series}{International International Encyclopedia of Unified Science}
      \field{title}{The Structure of Scientific Revolutions}
      \field{volume}{{II}}
      \field{year}{1962}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{lai_deep_2021}{incollection}{}
      \name{author}{4}{}{%
        {{hash=73a610265d03c7368578944ab915fefd}{%
           family={Lai},
           familyi={L\bibinitperiod},
           given={Feilin},
           giveni={F\bibinitperiod}}}%
        {{hash=aa37c80e1cb1f21cbd884dcded122824}{%
           family={Sharma},
           familyi={S\bibinitperiod},
           given={Atharva},
           giveni={A\bibinitperiod}}}%
        {{hash=7cd732e09b590cf509783c3255d60fb4}{%
           family={Liu},
           familyi={L\bibinitperiod},
           given={Xiuwen},
           giveni={X\bibinitperiod}}}%
        {{hash=42f7c89c04e37a74d5faf10ae9cf58f4}{%
           family={Yang},
           familyi={Y\bibinitperiod},
           given={Xiaojun},
           giveni={X\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {John Wiley \& Sons, Ltd}%
      }
      \strng{namehash}{65de6dffda6e40cad4c45a6ebf5f8561}
      \strng{fullhash}{c17e291905da2adf49f2c95aadea63f9}
      \strng{bibnamehash}{65de6dffda6e40cad4c45a6ebf5f8561}
      \strng{authorbibnamehash}{65de6dffda6e40cad4c45a6ebf5f8561}
      \strng{authornamehash}{65de6dffda6e40cad4c45a6ebf5f8561}
      \strng{authorfullhash}{c17e291905da2adf49f2c95aadea63f9}
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      \field{sortinithash}{7c47d417cecb1f4bd38d1825c427a61a}
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      \field{abstract}{Deep learning has recently become a trending research topic in remote sensing because of its potential in generating high performance for pattern classification. Some deep learning models can make effective use of spectral, spatial, and temporal information from remotely sensed data, resulting in improved mapping solutions, especially over complex environments, such as urban areas. This chapter provides an overview of deep learning models for urban and landscape mapping from remotely sensed data focusing on the potential of deep neural networks to address current challenges in land classification. It begins with a brief discussion of the evolution of artificial neural networks and the basic architecture of multi-layer neural networks deemed as the foundation for developing deep learning models, which is followed by a summary of some major advantages of deep learning. Then, several deep learning models commonly used in remote sensing are introduced, along with a close look at the two most popular models: convolution neural networks ({CNNs}) and recurrent neural networks ({RNNs}). Two case studies using {CNNs} and {RNNs} for landscape mapping over a complex urbanized coastal area are further presented to demonstrate how deep learning models can be used to generate improved performance in remote sensing. It is believed that these case studies can encourage further thinking over some potential issues (e.g. hyperparameter optimization) challenging the performance of deep learning applications in remote sensing .}
      \field{booktitle}{Urban Remote Sensing}
      \field{isbn}{978-1-119-62586-5}
      \field{langid}{english}
      \field{title}{Deep Learning for Urban and Landscape Mapping from Remotely Sensed Imagery}
      \field{urlday}{10}
      \field{urlmonth}{3}
      \field{urlyear}{2022}
      \field{year}{2021}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{153\bibrangedash 174}
      \range{pages}{22}
      \verb{file}
      \verb Snapshot:/home/chris/Zotero/storage/QBJBQKAL/9781119625865.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119625865.ch8
      \endverb
      \verb{url}
      \verb https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119625865.ch8
      \endverb
      \keyw{{CNNs},deep learning,remote sensing,{RNNs},urban mapping}
    \endentry
    \entry{lang_geobia_2019}{article}{}
      \name{author}{5}{}{%
        {{hash=2979075266629fa1d790fb7c7f4a940a}{%
           family={Lang},
           familyi={L\bibinitperiod},
           given={Stefan},
           giveni={S\bibinitperiod}}}%
        {{hash=cb4d727248c50247f2b7654ec3629ebf}{%
           family={Hay},
           familyi={H\bibinitperiod},
           given={Geoffrey\bibnamedelima J.},
           giveni={G\bibinitperiod\bibinitdelim J\bibinitperiod}}}%
        {{hash=41c92bf76b517004a02a72af8ff7929e}{%
           family={Baraldi},
           familyi={B\bibinitperiod},
           given={Andrea},
           giveni={A\bibinitperiod}}}%
        {{hash=c0d8be1b2acc3581cc448b7d58e3a4a8}{%
           family={Tiede},
           familyi={T\bibinitperiod},
           given={Dirk},
           giveni={D\bibinitperiod}}}%
        {{hash=e52e987849436cae55c3929da2203ba4}{%
           family={Blaschke},
           familyi={B\bibinitperiod},
           given={Thomas},
           giveni={T\bibinitperiod}}}%
      }
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      \field{abstract}{The primary goal of collecting Earth observation ({EO}) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science ({GIScience}), the term object-based image analysis ({OBIA}) was tentatively introduced in 2006. When it was re-formulated in 2008 as geographic object-based image analysis ({GEOBIA}), the primary focus was on integrating multiscale {EO} data with {GIScience} and computer vision ({CV}) solutions to cope with the increasing spatial and temporal resolution of {EO} imagery. Building on recent trends in the context of big {EO} data analytics as well as major achievements in {CV}, the objective of this article is to review the role of spatial concepts in the understanding of image objects as the primary analytical units in semantic {EO} image analysis, and to identify opportunities where {GEOBIA} may support multi-source remote sensing analysis in the era of big {EO} data analytics. We (re-)emphasize the spatial paradigm as a key requisite for an image understanding system capable to deal with and exploit the massive data streams we are currently facing; a system which encompasses a combined physical and statistical model-based inference engine, a well-structured {CV} system design based on a convergence of spatial and colour evidence, semantic content-based image retrieval capacities, and the full integration of spatio-temporal aspects of the studied geographical phenomena.}
      \field{journaltitle}{{ISPRS} International Journal of Geo-Information}
      \field{langid}{english}
      \field{month}{11}
      \field{note}{Number: 11 Publisher: Multidisciplinary Digital Publishing Institute}
      \field{number}{11}
      \field{title}{{GEOBIA} Achievements and Spatial Opportunities in the Era of Big Earth Observation Data}
      \field{urlday}{20}
      \field{urlmonth}{1}
      \field{urlyear}{2022}
      \field{volume}{8}
      \field{year}{2019}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{474}
      \range{pages}{1}
      \verb{doi}
      \verb 10.3390/ijgi8110474
      \endverb
      \verb{file}
      \verb Full Text PDF:/home/chris/Zotero/storage/JNRW4D6Y/Lang et al. - 2019 - GEOBIA Achievements and Spatial Opportunities in t.pdf:application/pdf;Snapshot:/home/chris/Zotero/storage/Q2C45V9E/474.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://www.mdpi.com/2220-9964/8/11/474
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      \verb{url}
      \verb https://www.mdpi.com/2220-9964/8/11/474
      \endverb
      \keyw{big data analytics,computer vision,geographic object-based image analysis ({GEOBIA}),geographic space,{GIScience},spatial autocorrelation}
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      \field{abstract}{Humanitarian action has rapidly adopted Earth observation ({EO}) and geospatial technologies shaping them according to their needs. Protracted crises and large-scale population displacements require up-to-date information in many facets of humanitarian action support, from mission planning, resource deployment and monitoring, to nutrition and vaccination campaigns, camp plotting, damage assessment, etc. Even though nearly all assets of remote sensing apply in such demanding scenarios, it remains a challenge to fully implement and sustain a trustful and reliable information service. This paper discusses achievements and open issues in the use and uptake of {EO} technology, from a technical and organisational point of view, motivated by an information service for Médecins Sans Frontières ({MSF}) and its extension to other {NGO}’s information needs in the humanitarian sector. With a focus on {EO}-based population estimation based on (semi-)automated dwelling counting from very high-resolution optical satellite imagery as well as the exploitation of data integration (including radar sensors), the paper also covers potential service elements with respect to environmental and ground- or surface water monitoring. It investigates workflow elements in relation to information extraction and delivery by illustrating a broad range of application scenarios, and discusses first operational solutions of a customized service portfolio.}
      \field{day}{13}
      \field{issn}{null}
      \field{issue}{sup2}
      \field{journaltitle}{European Journal of Remote Sensing}
      \field{month}{7}
      \field{title}{Earth observation tools and services to increase the effectiveness of humanitarian assistance}
      \field{urlday}{31}
      \field{urlmonth}{10}
      \field{urlyear}{2021}
      \field{volume}{53}
      \field{year}{2020}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{67\bibrangedash 85}
      \range{pages}{19}
      \verb{doi}
      \verb 10.1080/22797254.2019.1684208
      \endverb
      \verb{file}
      \verb Snapshot:/home/chris/Zotero/storage/RFHWJ8Y2/22797254.2019.html:text/html;Full Text PDF:/home/chris/Zotero/storage/ZAATX3JM/Lang et al. - 2020 - Earth observation tools and services to increase t.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb https://doi.org/10.1080/22797254.2019.1684208
      \endverb
      \verb{url}
      \verb https://doi.org/10.1080/22797254.2019.1684208
      \endverb
      \keyw{Earth observation,Geohumanitarian action,geospatial tools,humanitarian assistance,population monitoring,{VHR}/{HR}/{SAR} satellite data}
    \endentry
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           family={Lang},
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           family={Adebayo},
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        {{hash=cc7257c150cd1fb45bc9f425353c84de}{%
           family={Schwendemann},
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        {{hash=74df920de86c77d91a60ef6e19f20793}{%
           family={Jeremias},
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           given={Peter},
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      }
      \strng{namehash}{7876ee643689a6df5440f34cf0e4682d}
      \strng{fullhash}{a2a95caca19a9770217a282ab61e8fb5}
      \strng{bibnamehash}{7876ee643689a6df5440f34cf0e4682d}
      \strng{authorbibnamehash}{7876ee643689a6df5440f34cf0e4682d}
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      \strng{authorfullhash}{a2a95caca19a9770217a282ab61e8fb5}
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      \field{issn}{2308-1708}
      \field{journaltitle}{{GI}\_Forum 2021,}
      \field{note}{Publisher: Verlag der Österreichischen Akademie der Wissenschaften}
      \field{shortjournal}{{GI}\_Forum 2021,}
      \field{title}{Multi-Feature Sample Database for Enhancing Deep Learning Tasks in Operational Humanitarian Applications}
      \field{volume}{9}
      \field{year}{2021}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{209\bibrangedash 219}
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        {{hash=40a8e4774982146adc2688546f54efb2}{%
           family={Bengio},
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           given={Yoshua},
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        {{hash=9a8750ccdb2a4cf14d2655face1ce016}{%
           family={Hinton},
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           given={Geoffrey},
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      \field{issn}{0028-0836, 1476-4687}
      \field{journaltitle}{Nature}
      \field{langid}{english}
      \field{month}{5}
      \field{number}{7553}
      \field{title}{Deep learning}
      \field{urlday}{1}
      \field{urlmonth}{2}
      \field{urlyear}{2020}
      \field{volume}{521}
      \field{year}{2015}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{436\bibrangedash 444}
      \range{pages}{9}
      \verb{doi}
      \verb 10.1038/nature14539
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      \verb{file}
      \verb LeCun et al. - 2015 - Deep learning.pdf:/home/chris/Zotero/storage/MS3L43KJ/LeCun et al. - 2015 - Deep learning.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb http://www.nature.com/articles/nature14539
      \endverb
      \verb{url}
      \verb http://www.nature.com/articles/nature14539
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    \endentry
    \entry{leonita_machine_2018}{article}{}
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        {{hash=cb952cc9eb02c1f291744027abb742b6}{%
           family={Leonita},
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           given={Gina},
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        {{hash=2e31d45cb8f4df38b638b412fc143f2c}{%
           family={Kuffer},
           familyi={K\bibinitperiod},
           given={Monika},
           giveni={M\bibinitperiod}}}%
        {{hash=0ab163e3e17c1b8a92f048e5c5a761ea}{%
           family={Sliuzas},
           familyi={S\bibinitperiod},
           given={Richard},
           giveni={R\bibinitperiod}}}%
        {{hash=bba94f32ed6d61df8dcd638e7c7e9214}{%
           family={Persello},
           familyi={P\bibinitperiod},
           given={Claudio},
           giveni={C\bibinitperiod}}}%
      }
      \strng{namehash}{c028d38db5983e75995b9a020cd933c5}
      \strng{fullhash}{8db54a2a4c9e0916ef8599b5a727ae50}
      \strng{bibnamehash}{c028d38db5983e75995b9a020cd933c5}
      \strng{authorbibnamehash}{c028d38db5983e75995b9a020cd933c5}
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      \strng{authorfullhash}{8db54a2a4c9e0916ef8599b5a727ae50}
      \field{sortinit}{L}
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      \field{abstract}{The survey-based slum mapping ({SBSM}) program conducted by the Indonesian government to reach the national target of \&ldquo;cities without slums\&rdquo; by 2019 shows mapping inconsistencies due to several reasons, e.g., the dependency on the surveyor\&rsquo;s experiences and the complexity of the slum indicators set. By relying on such inconsistent maps, it will be difficult to monitor the national slum upgrading program\&rsquo;s progress. Remote sensing imagery combined with machine learning algorithms could support the reduction of these inconsistencies. This study evaluates the performance of two machine learning algorithms, i.e., support vector machine ({SVM}) and random forest ({RF}), for slum mapping in support of the slum mapping campaign in Bandung, Indonesia. Recognizing the complexity in differentiating slum and formal areas in Indonesia, the study used a combination of spectral, contextual, and morphological features. In addition, sequential feature selection ({SFS}) combined with the Hilbert\&ndash;Schmidt independence criterion ({HSIC}) was used to select significant features for classifying slums. Overall, the highest accuracy (88.5\%) was achieved by the {SVM} with {SFS} using contextual, morphological, and spectral features, which is higher than the estimated accuracy of the {SBSM}. To evaluate the potential of machine learning-based slum mapping ({MLBSM}) in support of slum upgrading programs, interviews were conducted with several local and national stakeholders. Results show that local acceptance for a remote sensing-based slum mapping approach varies among stakeholder groups. Therefore, a locally adapted framework is required to combine ground surveys with robust and consistent machine learning methods, for being able to deal with big data, and to allow the rapid extraction of consistent information on the dynamics of slums at a large scale.}
      \field{journaltitle}{Remote Sensing}
      \field{langid}{english}
      \field{month}{10}
      \field{number}{10}
      \field{shorttitle}{Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs}
      \field{title}{Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia}
      \field{urlday}{22}
      \field{urlmonth}{1}
      \field{urlyear}{2022}
      \field{volume}{10}
      \field{year}{2018}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{1522}
      \range{pages}{1}
      \verb{doi}
      \verb 10.3390/rs10101522
      \endverb
      \verb{file}
      \verb Full Text PDF:/home/chris/Zotero/storage/MQJI5PXV/Leonita et al. - 2018 - Machine Learning-Based Slum Mapping in Support of .pdf:application/pdf;Snapshot:/home/chris/Zotero/storage/8XEQ8ZEI/htm.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://www.mdpi.com/2072-4292/10/10/1522
      \endverb
      \verb{url}
      \verb https://www.mdpi.com/2072-4292/10/10/1522
      \endverb
      \keyw{Bandung,Indonesia,machine learning,slum upgrading programs,slums}
    \endentry
    \entry{ma_deep_2019}{article}{}
      \name{author}{6}{}{%
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           family={Ma},
           familyi={M\bibinitperiod},
           given={Lei},
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        {{hash=93c82128aca25c57cfb17e90d02136d5}{%
           family={Liu},
           familyi={L\bibinitperiod},
           given={Yu},
           giveni={Y\bibinitperiod}}}%
        {{hash=8e1c7b0f8eaaec4074660f432646e819}{%
           family={Zhang},
           familyi={Z\bibinitperiod},
           given={Xueliang},
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        {{hash=cd8ce48b6e11eda916d6af47a2ae4bb7}{%
           family={Ye},
           familyi={Y\bibinitperiod},
           given={Yuanxin},
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        {{hash=24a192736e296abce95eb27d890dbd6a}{%
           family={Yin},
           familyi={Y\bibinitperiod},
           given={Gaofei},
           giveni={G\bibinitperiod}}}%
        {{hash=e4d22a31bd64cb197a5bbb7041fde59d}{%
           family={Johnson},
           familyi={J\bibinitperiod},
           given={Brian\bibnamedelima Alan},
           giveni={B\bibinitperiod\bibinitdelim A\bibinitperiod}}}%
      }
      \strng{namehash}{dfd95404f12354c6a96019259bd9ed0d}
      \strng{fullhash}{208e56e221aebfdba662ab9f023796fc}
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      \field{issn}{0924-2716}
      \field{journaltitle}{{ISPRS} journal of photogrammetry and remote sensing}
      \field{note}{Publisher: Elsevier}
      \field{shortjournal}{{ISPRS} journal of photogrammetry and remote sensing}
      \field{title}{Deep learning in remote sensing applications: A meta-analysis and review}
      \field{volume}{152}
      \field{year}{2019}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{166\bibrangedash 177}
      \range{pages}{12}
    \endentry
    \entry{marmanis_deep_2016}{article}{}
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        {{hash=bf554d0f5455ca12c7c1990593f0ee76}{%
           family={Marmanis},
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           given={Dimitrios},
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        {{hash=5ce04dd0b9604019878e13703f117bfc}{%
           family={Datcu},
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           given={Mihai},
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        {{hash=45d3bbb75e3e3be33a6cafb00499daf7}{%
           family={Esch},
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           given={Thomas},
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        {{hash=6cc3148836af5c71eb2f91478b4de5cc}{%
           family={Stilla},
           familyi={S\bibinitperiod},
           given={Uwe},
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      \field{abstract}{Deep learning methods such as convolutional neural networks ({CNNs}) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using {CNNs} along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained {CNN} designed for tackling an entirely different classification problem, namely, the {ImageNet} challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised {CNN} classifier, along with their class labels, effectively training the system. Through this two-stage framework, we successfully deal with the limited-data problem in an end-to-end processing scheme. Comparative results over the {UC} Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1\% up to 92.4\%. Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach.}
      \field{issn}{1558-0571}
      \field{journaltitle}{{IEEE} Geoscience and Remote Sensing Letters}
      \field{month}{1}
      \field{note}{Conference Name: {IEEE} Geoscience and Remote Sensing Letters}
      \field{number}{1}
      \field{title}{Deep Learning Earth Observation Classification Using {ImageNet} Pretrained Networks}
      \field{volume}{13}
      \field{year}{2016}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{105\bibrangedash 109}
      \range{pages}{5}
      \verb{doi}
      \verb 10.1109/LGRS.2015.2499239
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      \verb{file}
      \verb Accepted Version:/home/chris/Zotero/storage/VID48AU6/Marmanis et al. - 2016 - Deep Learning Earth Observation Classification Usi.pdf:application/pdf;IEEE Xplore Abstract Record:/home/chris/Zotero/storage/TRLMDGMK/7342907.html:text/html
      \endverb
      \keyw{Remote sensing,Convolutional neural networks ({CNNs}),Training,feature extraction,Feature extraction,Arrays,Neural networks,Data models,Adaptation models,deep learning ({DL}),land-use classification,pretrained network,remote sensing ({RS})}
    \endentry
    \entry{minghini_proceedings_2021}{book}{}
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        {{hash=a9869674cf520ffbd0120c85579a77e5}{%
           family={Ludwig},
           familyi={L\bibinitperiod},
           given={Christina},
           giveni={C\bibinitperiod}}}%
        {{hash=ec90a0788517cd8f83956d9e5c536b85}{%
           family={Anderson},
           familyi={A\bibinitperiod},
           given={Jennings},
           giveni={J\bibinitperiod}}}%
        {{hash=118ce8006d9537341b85fd1eb39fd928}{%
           family={Mooney},
           familyi={M\bibinitperiod},
           given={Peter},
           giveni={P\bibinitperiod}}}%
        {{hash=d7b77b6363d5ab39e4ae2db5261c73ce}{%
           family={Grinberger},
           familyi={G\bibinitperiod},
           given={A.\bibnamedelimi Yair},
           giveni={A\bibinitperiod\bibinitdelim Y\bibinitperiod}}}%
      }
      \list{location}{1}{%
        {online}%
      }
      \list{publisher}{1}{%
        {Zenodo}%
      }
      \strng{namehash}{fb7da4a1ad8651d6e6eb723127682600}
      \strng{fullhash}{592ae58b6978b92fea387499c857fae5}
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      \field{abstract}{Proceedings of the Academic Track at the State of the Map 2021 Online Conference, July 09-11 2021. Editors Marco Minghini – European Commission, Joint Research Centre ({JRC}), Ispra, Italy Christina Ludwig – {GIScience} Research Group, Institute of Geography, Heidelberg University, Germany Jennings Anderson – {YetiGeoLabs}, Montana, {USA} Peter Mooney – Department of Computer Science, Maynooth University, Maynooth, Ireland A. Yair Grinberger – Department of Geography, The Hebrew University of Jerusalem, Israel}
      \field{day}{20}
      \field{month}{7}
      \field{title}{Proceedings of the Academic Track at State of the Map 2021}
      \field{urlday}{12}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{year}{2021}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{doi}
      \verb 10.5281/zenodo.5116434
      \endverb
      \verb{file}
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      \verb{urlraw}
      \verb https://zenodo.org/record/5116434
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      \verb{url}
      \verb https://zenodo.org/record/5116434
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      \keyw{mapping,open data,geographic information science,geospatial,giscience,openstreetmap,proceedings,state of the map}
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           family={{National Research Council}},
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      \list{publisher}{1}{%
        {National Academies Press}%
      }
      \strng{namehash}{542dbf807c6825455f75670116436ebd}
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      \strng{bibnamehash}{542dbf807c6825455f75670116436ebd}
      \strng{authorbibnamehash}{542dbf807c6825455f75670116436ebd}
      \strng{authornamehash}{542dbf807c6825455f75670116436ebd}
      \strng{authorfullhash}{542dbf807c6825455f75670116436ebd}
      \field{sortinit}{N}
      \field{sortinithash}{22369a73d5f88983a108b63f07f37084}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{isbn}{0-309-06408-2}
      \field{title}{People and pixels: Linking remote sensing and social science}
      \field{year}{1998}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{united_nations_global_2018}{misc}{}
      \name{author}{1}{}{%
        {{hash=2d661e9167bd3a268ea03a3ee152926c}{%
           family={Nations},
           familyi={N\bibinitperiod},
           given={United},
           giveni={U\bibinitperiod}}}%
      }
      \list{publisher}{1}{%
        {United Nations}%
      }
      \strng{namehash}{2d661e9167bd3a268ea03a3ee152926c}
      \strng{fullhash}{2d661e9167bd3a268ea03a3ee152926c}
      \strng{bibnamehash}{2d661e9167bd3a268ea03a3ee152926c}
      \strng{authorbibnamehash}{2d661e9167bd3a268ea03a3ee152926c}
      \strng{authornamehash}{2d661e9167bd3a268ea03a3ee152926c}
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      \field{sortinit}{N}
      \field{sortinithash}{22369a73d5f88983a108b63f07f37084}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{title}{Global Compact on Refugees}
      \field{year}{2018}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{ng_machine_2018}{book}{}
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      \field{title}{Machine Learning Yearning}
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      \field{langid}{english}
      \field{note}{Publisher: Determination Press}
      \field{title}{Neural Networks and Deep Learning}
      \field{urlday}{24}
      \field{urlmonth}{8}
      \field{urlyear}{2020}
      \field{year}{2015}
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      \verb http://neuralnetworksanddeeplearning.com
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      \verb{url}
      \verb http://neuralnetworksanddeeplearning.com
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        {{hash=61e0799e76f64369168599f31bfecf34}{%
           family={Pal},
           familyi={P\bibinitperiod},
           given={Sankar\bibnamedelima K},
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      \strng{bibnamehash}{3e721d3992668efe6e025929847203b8}
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      \field{abstract}{Many image segmentation techniques are available in the literature. Some of these techniques use only the gray level histogram, some use spatial details while others use fuzzy set theoretic approaches. Most of these techniques are not suitable for noisy environments. Some works have been done using the Markov Random Field ({MRF}) model which is robust to noise, but is computationally involved. Neural network architectures which help to get the output in real time because of their parallel processing ability, have also been used for segmentation and they work fine even when the noise level is very high. The literature on color image segmentation is not that rich as it is for gray tone images. This paper critically reviews and summarizes some of these techniques. Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches. Adequate attention is paid to segmentation of range images and magnetic resonance images. It also addresses the issue of quantitative evaluation of segmentation results.}
      \field{day}{1}
      \field{issn}{0031-3203}
      \field{journaltitle}{Pattern Recognition}
      \field{langid}{english}
      \field{month}{9}
      \field{number}{9}
      \field{shortjournal}{Pattern Recognition}
      \field{title}{A review on image segmentation techniques}
      \field{urlday}{10}
      \field{urlmonth}{6}
      \field{urlyear}{2022}
      \field{volume}{26}
      \field{year}{1993}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{1277\bibrangedash 1294}
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      \verb 10.1016/0031-3203(93)90135-J
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      \verb{url}
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      \keyw{Image segmentation,Clustering,Edge detection,Fuzzy sets,Markov Random Field,Relaxation,Thresholding}
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    \entry{perez_effectiveness_2017}{article}{}
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      \field{abstract}{In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification. Previous work has demonstrated the effectiveness of data augmentation through simple techniques, such as cropping, rotating, and flipping input images. We artificially constrain our access to data to a small subset of the {ImageNet} dataset, and compare each data augmentation technique in turn. One of the more successful data augmentations strategies is the traditional transformations mentioned above. We also experiment with {GANs} to generate images of different styles. Finally, we propose a method to allow a neural net to learn augmentations that best improve the classifier, which we call neural augmentation. We discuss the successes and shortcomings of this method on various datasets.}
      \field{day}{13}
      \field{eprinttype}{arxiv}
      \field{journaltitle}{{arXiv}:1712.04621 [cs]}
      \field{month}{12}
      \field{title}{The Effectiveness of Data Augmentation in Image Classification using Deep Learning}
      \field{urlday}{8}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{year}{2017}
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      \verb{eprint}
      \verb 1712.04621
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      \verb arXiv Fulltext PDF:/home/chris/Zotero/storage/N6PERTFN/Perez and Wang - 2017 - The Effectiveness of Data Augmentation in Image Cl.pdf:application/pdf;arXiv.org Snapshot:/home/chris/Zotero/storage/XPFVWQGQ/1712.html:text/html
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      \keyw{Computer Science - Computer Vision and Pattern Recognition}
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    \entry{quinn_humanitarian_2018}{article}{}
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           family={Quinn},
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        {{hash=a35f05051e555e689c1c1f734bdc42b9}{%
           family={Nyhan},
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        {{hash=fefc42f9ad14adcbf7f9a9d38ab6b382}{%
           family={Navarro},
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        {{hash=74bd7cf7c1008fbdd44ac76a55819650}{%
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           family={Bromley},
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           given={Lars},
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        {{hash=8e4c93aa099374278a3f13eece6580de}{%
           family={Luengo-Oroz},
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           given={Miguel},
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      \field{abstract}{The coordination of humanitarian relief, e.g. in a natural disaster or a conflict situation, is often complicated by a scarcity of data to inform planning. Remote sensing imagery, from satellites or drones, can give important insights into conditions on the ground, including in areas which are difficult to access. Applications include situation awareness after natural disasters, structural damage assessment in conflict, monitoring human rights violations or population estimation in settlements. We review machine learning approaches for automating these problems, and discuss their potential and limitations. We also provide a case study of experiments using deep learning methods to count the numbers of structures in multiple refugee settlements in Africa and the Middle East. We find that while high levels of accuracy are possible, there is considerable variation in the characteristics of imagery collected from different sensors and regions. In this, as in the other applications discussed in the paper, critical inferences must be made from a relatively small amount of pixel data. We, therefore, consider that using machine learning systems as an augmentation of human analysts is a reasonable strategy to transition from current fully manual operational pipelines to ones which are both more efficient and have the necessary levels of quality control. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.}
      \field{day}{13}
      \field{journaltitle}{Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}
      \field{month}{9}
      \field{note}{Publisher: Royal Society}
      \field{number}{2128}
      \field{shorttitle}{Humanitarian applications of machine learning with remote-sensing data}
      \field{title}{Humanitarian applications of machine learning with remote-sensing data: review and case study in refugee settlement mapping}
      \field{urlday}{10}
      \field{urlmonth}{9}
      \field{urlyear}{2021}
      \field{volume}{376}
      \field{year}{2018}
      \field{dateera}{ce}
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      \field{pages}{20170363}
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      \verb{doi}
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      \verb{url}
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      \keyw{humanitarian aid,object detection,remote sensing,satellite imaging}
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           family={Sun},
           familyi={S\bibinitperiod},
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      \field{abstract}{State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like {SPPnet} and Fast R-{CNN} have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network ({RPN}) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An {RPN} is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The {RPN} is trained end-to-end to generate high-quality region proposals, which are used by Fast R-{CNN} for detection. We further merge {RPN} and Fast R-{CNN} into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the {RPN} component tells the unified network where to look. For the very deep {VGG}-16 model, our detection system has a frame rate of 5fps (including all steps) on a {GPU}, while achieving state-of-the-art object detection accuracy on {PASCAL} {VOC} 2007, 2012, and {MS} {COCO} datasets with only 300 proposals per image. In {ILSVRC} and {COCO} 2015 competitions, Faster R-{CNN} and {RPN} are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.}
      \field{day}{6}
      \field{eprinttype}{arxiv}
      \field{journaltitle}{{arXiv}:1506.01497 [cs]}
      \field{month}{1}
      \field{shorttitle}{Faster R-{CNN}}
      \field{title}{Faster R-{CNN}: Towards Real-Time Object Detection with Region Proposal Networks}
      \field{urlday}{8}
      \field{urlmonth}{1}
      \field{urlyear}{2022}
      \field{year}{2016}
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      \verb arXiv Fulltext PDF:/home/chris/Zotero/storage/367XCXQW/Ren et al. - 2016 - Faster R-CNN Towards Real-Time Object Detection w.pdf:application/pdf;arXiv.org Snapshot:/home/chris/Zotero/storage/6MDSGCYK/1506.html:text/html
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      \keyw{Computer Science - Computer Vision and Pattern Recognition}
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    \entry{ronneberger_u-net_2015}{report}{}
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      \field{abstract}{There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the {ISBI} challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and {DIC}) we won the {ISBI} cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent {GPU}. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .}
      \field{day}{18}
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      \field{month}{5}
      \field{note}{type: article}
      \field{number}{{arXiv}:1505.04597}
      \field{shorttitle}{U-Net}
      \field{title}{U-Net: Convolutional Networks for Biomedical Image Segmentation}
      \field{urlday}{26}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{year}{2015}
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      \field{issn}{1939-1471, 0033-295X}
      \field{journaltitle}{Psychological Review}
      \field{langid}{english}
      \field{number}{6}
      \field{shortjournal}{Psychological Review}
      \field{shorttitle}{The perceptron}
      \field{title}{The perceptron: A probabilistic model for information storage and organization in the brain.}
      \field{urlday}{3}
      \field{urlmonth}{2}
      \field{urlyear}{2020}
      \field{volume}{65}
      \field{year}{1958}
      \field{dateera}{ce}
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      \field{pages}{386\bibrangedash 408}
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      \field{abstract}{The author provides a general introduction to computer vision. He discusses basic techniques and computer implementations, and also indicates areas in which further research is needed. He focuses on two-dimensional object recognition, i.e. recognition of an object whose spatial orientation, relative to the viewing direction is known.{<}{>}}
      \field{issn}{1558-2256}
      \field{journaltitle}{Proceedings of the {IEEE}}
      \field{month}{8}
      \field{note}{Conference Name: Proceedings of the {IEEE}}
      \field{number}{8}
      \field{shorttitle}{Computer vision}
      \field{title}{Computer vision: basic principles}
      \field{volume}{76}
      \field{year}{1988}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{863\bibrangedash 868}
      \range{pages}{6}
      \verb{doi}
      \verb 10.1109/5.5961
      \endverb
      \verb{file}
      \verb IEEE Xplore Full Text PDF:/home/chris/Zotero/storage/DPDQDC3Y/Rosenfeld - 1988 - Computer vision basic principles.pdf:application/pdf
      \endverb
      \keyw{Computer vision,Object recognition}
    \endentry
    \entry{rumelhart_learning_1986}{article}{}
      \name{author}{3}{}{%
        {{hash=55cd5380bb5b15032a6d5a2015f56e3f}{%
           family={Rumelhart},
           familyi={R\bibinitperiod},
           given={David\bibnamedelima E.},
           giveni={D\bibinitperiod\bibinitdelim E\bibinitperiod}}}%
        {{hash=813bd95fe553e6079cd53a567b238287}{%
           family={Hinton},
           familyi={H\bibinitperiod},
           given={Geoffrey\bibnamedelima E.},
           giveni={G\bibinitperiod\bibinitdelim E\bibinitperiod}}}%
        {{hash=6cbc29ad7fd57ffdb9ed4728418fd988}{%
           family={Williams},
           familyi={W\bibinitperiod},
           given={Ronald\bibnamedelima J.},
           giveni={R\bibinitperiod\bibinitdelim J\bibinitperiod}}}%
      }
      \strng{namehash}{fd75cea68d6982b503a0cd84f7cc7b51}
      \strng{fullhash}{fd75cea68d6982b503a0cd84f7cc7b51}
      \strng{bibnamehash}{fd75cea68d6982b503a0cd84f7cc7b51}
      \strng{authorbibnamehash}{fd75cea68d6982b503a0cd84f7cc7b51}
      \strng{authornamehash}{fd75cea68d6982b503a0cd84f7cc7b51}
      \strng{authorfullhash}{fd75cea68d6982b503a0cd84f7cc7b51}
      \field{sortinit}{R}
      \field{sortinithash}{5e1c39a9d46ffb6bebd8f801023a9486}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{title}
      \field{abstract}{We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.}
      \field{issn}{1476-4687}
      \field{journaltitle}{Nature}
      \field{langid}{english}
      \field{month}{10}
      \field{number}{6088}
      \field{shortjournal}{Nature}
      \field{title}{Learning representations by back-propagating errors}
      \field{urlday}{12}
      \field{urlmonth}{2}
      \field{urlyear}{2020}
      \field{volume}{323}
      \field{year}{1986}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{533\bibrangedash 536}
      \range{pages}{4}
      \verb{doi}
      \verb 10.1038/323533a0
      \endverb
      \verb{file}
      \verb Snapshot:/home/chris/Zotero/storage/IP8MS3LS/323533a0.html:text/html;Full Text PDF:/home/chris/Zotero/storage/ZEEDAQKZ/Rumelhart et al. - 1986 - Learning representations by back-propagating error.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb https://www.nature.com/articles/323533a0
      \endverb
      \verb{url}
      \verb https://www.nature.com/articles/323533a0
      \endverb
    \endentry
    \entry{rummery_why_nodate}{online}{}
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        {{hash=b8f3af0c6edd34dd8dc662f91b92fe6e}{%
           family={Rummery},
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           given={A.},
           giveni={A\bibinitperiod}}}%
      }
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      \strng{authorbibnamehash}{b8f3af0c6edd34dd8dc662f91b92fe6e}
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      \field{sortinit}{R}
      \field{sortinithash}{5e1c39a9d46ffb6bebd8f801023a9486}
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      \field{labeltitlesource}{title}
      \field{abstract}{{UNHCR}'s new Director of the Africa Bureau, Raouf Mazou, says the socioeconomic inclusion of refugees is vital to the agency's efforts to protect and assist them.}
      \field{langid}{english}
      \field{title}{Why including refugees makes economic sense}
      \field{titleaddon}{{UNHCR}}
      \field{urlday}{4}
      \field{urlmonth}{6}
      \field{urlyear}{2022}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{file}
      \verb Snapshot:/home/chris/Zotero/storage/U2FLFUR5/including-refugees-makes-economic-sense.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://www.unhcr.org/news/stories/2019/4/5c9caee84/including-refugees-makes-economic-sense.html
      \endverb
      \verb{url}
      \verb https://www.unhcr.org/news/stories/2019/4/5c9caee84/including-refugees-makes-economic-sense.html
      \endverb
    \endentry
    \entry{schmidhuber_deep_2015}{article}{}
      \name{author}{1}{}{%
        {{hash=288bdbcfe1b91ad7484d7a24f74f99ed}{%
           family={Schmidhuber},
           familyi={S\bibinitperiod},
           given={Jürgen},
           giveni={J\bibinitperiod}}}%
      }
      \strng{namehash}{288bdbcfe1b91ad7484d7a24f74f99ed}
      \strng{fullhash}{288bdbcfe1b91ad7484d7a24f74f99ed}
      \strng{bibnamehash}{288bdbcfe1b91ad7484d7a24f74f99ed}
      \strng{authorbibnamehash}{288bdbcfe1b91ad7484d7a24f74f99ed}
      \strng{authornamehash}{288bdbcfe1b91ad7484d7a24f74f99ed}
      \strng{authorfullhash}{288bdbcfe1b91ad7484d7a24f74f99ed}
      \field{sortinit}{S}
      \field{sortinithash}{b164b07b29984b41daf1e85279fbc5ab}
      \field{labelnamesource}{author}
      \field{labeltitlesource}{shorttitle}
      \field{abstract}{In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning \& evolutionary computation, and indirect search for short programs encoding deep and large networks.}
      \field{day}{1}
      \field{issn}{0893-6080}
      \field{journaltitle}{Neural Networks}
      \field{langid}{english}
      \field{month}{1}
      \field{shortjournal}{Neural Networks}
      \field{shorttitle}{Deep learning in neural networks}
      \field{title}{Deep learning in neural networks: An overview}
      \field{urlday}{9}
      \field{urlmonth}{9}
      \field{urlyear}{2022}
      \field{volume}{61}
      \field{year}{2015}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{85\bibrangedash 117}
      \range{pages}{33}
      \verb{doi}
      \verb 10.1016/j.neunet.2014.09.003
      \endverb
      \verb{file}
      \verb Submitted Version:/home/chris/Zotero/storage/37ERZ2GF/Schmidhuber - 2015 - Deep learning in neural networks An overview.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb https://www.sciencedirect.com/science/article/pii/S0893608014002135
      \endverb
      \verb{url}
      \verb https://www.sciencedirect.com/science/article/pii/S0893608014002135
      \endverb
      \keyw{Deep learning,Evolutionary computation,Reinforcement learning,Supervised learning,Unsupervised learning}
    \endentry
    \entry{seale_coastline_2022}{article}{}
      \name{author}{5}{}{%
        {{hash=22d35c82ced4c1fcae94cc54ec5624ce}{%
           family={Seale},
           familyi={S\bibinitperiod},
           given={Catherine},
           giveni={C\bibinitperiod}}}%
        {{hash=0c5c5d82bb072172915e6302230559ae}{%
           family={Redfern},
           familyi={R\bibinitperiod},
           given={Thomas},
           giveni={T\bibinitperiod}}}%
        {{hash=ff8a1fecf58f7dd6bbb4ef680a8edcb7}{%
           family={Chatfield},
           familyi={C\bibinitperiod},
           given={Paul},
           giveni={P\bibinitperiod}}}%
        {{hash=0c9b14cfcfd8fc56ea90e9a2c7ed0c32}{%
           family={Luo},
           familyi={L\bibinitperiod},
           given={Chunbo},
           giveni={C\bibinitperiod}}}%
        {{hash=aa8444a0f4b333a1e61ba205b6a4dc23}{%
           family={Dempsey},
           familyi={D\bibinitperiod},
           given={Kari},
           giveni={K\bibinitperiod}}}%
      }
      \strng{namehash}{a80f7c1635732f671216783687d6e761}
      \strng{fullhash}{fe6830c4f5930a56fe04f968c8801bee}
      \strng{bibnamehash}{a80f7c1635732f671216783687d6e761}
      \strng{authorbibnamehash}{a80f7c1635732f671216783687d6e761}
      \strng{authornamehash}{a80f7c1635732f671216783687d6e761}
      \strng{authorfullhash}{fe6830c4f5930a56fe04f968c8801bee}
      \field{sortinit}{S}
      \field{sortinithash}{b164b07b29984b41daf1e85279fbc5ab}
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      \field{labeltitlesource}{shorttitle}
      \field{abstract}{Detailed and up-to-date coastline morphology data underpins our understanding of coastline change over time. The development of an automated and scalable coastline extraction methodology from satellite imagery is currently limited by the low availability of open, globally distributed and diverse labelled data with which to develop and benchmark techniques. Therefore, in this study we present the Sentinel-2 Water Edges Dataset ({SWED}), a new and bespoke labelled image dataset for the development and bench-marking of techniques for the automated extraction of coastline morphology data from Sentinel-2 images. Composed of 16 labelled training Sentinel-2 scenes, and 98 test label-image pairs, {SWED} is globally distributed and contains examples of many different coastline types and natural and anthropogenic coastline features. To provide a baseline of model performance against {SWED} we train and test four convolutional neural network models, based on the U-Net model architecture. Models are optimised using Categorical Cross-entropy Loss, Sørensen–Dice Loss and two novel loss functions we present for the focusing of model training attention to the boundary between land and water. Through a hybrid quantitative and qualitative model assessment process we demonstrate that the model trained using our novel Sobel-edge loss function has greater sensitivity to fine-scale, narrow coastline features whilst possessing near top quantitative performance demonstrated by Categorical Cross-entropy. The {SWED} dataset is published openly for use by the remote sensing and machine learning communities, whilst the Sobel-edge loss is available for use in machine learning applications where sensitivity to boundary features is important.}
      \field{day}{1}
      \field{issn}{0034-4257}
      \field{journaltitle}{Remote Sensing of Environment}
      \field{langid}{english}
      \field{month}{9}
      \field{shortjournal}{Remote Sensing of Environment}
      \field{shorttitle}{Coastline detection in satellite imagery}
      \field{title}{Coastline detection in satellite imagery: A deep learning approach on new benchmark data}
      \field{urlday}{11}
      \field{urlmonth}{9}
      \field{urlyear}{2022}
      \field{volume}{278}
      \field{year}{2022}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{113044}
      \range{pages}{1}
      \verb{doi}
      \verb 10.1016/j.rse.2022.113044
      \endverb
      \verb{file}
      \verb ScienceDirect Snapshot:/home/chris/Zotero/storage/2WQI4AWV/S0034425722001584.html:text/html
      \endverb
      \verb{urlraw}
      \verb https://www.sciencedirect.com/science/article/pii/S0034425722001584
      \endverb
      \verb{url}
      \verb https://www.sciencedirect.com/science/article/pii/S0034425722001584
      \endverb
      \keyw{Automated coastline extraction,Deep learning,Labelled data,Loss function,Machine learning,Sentinel-2 satellite imagery}
    \endentry
    \entry{shorten_survey_2019}{article}{}
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        {{hash=d49d1c5cbce775054678e29a462ca723}{%
           family={Shorten},
           familyi={S\bibinitperiod},
           given={Connor},
           giveni={C\bibinitperiod}}}%
        {{hash=c7e0de384e7d1be2ca29a338e7f5c047}{%
           family={Khoshgoftaar},
           familyi={K\bibinitperiod},
           given={Taghi\bibnamedelima M.},
           giveni={T\bibinitperiod\bibinitdelim M\bibinitperiod}}}%
      }
      \strng{namehash}{dae7c2fd5449b2cabfe6d8cea2eebe96}
      \strng{fullhash}{dae7c2fd5449b2cabfe6d8cea2eebe96}
      \strng{bibnamehash}{dae7c2fd5449b2cabfe6d8cea2eebe96}
      \strng{authorbibnamehash}{dae7c2fd5449b2cabfe6d8cea2eebe96}
      \strng{authornamehash}{dae7c2fd5449b2cabfe6d8cea2eebe96}
      \strng{authorfullhash}{dae7c2fd5449b2cabfe6d8cea2eebe96}
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      \field{abstract}{Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on {GANs} are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.}
      \field{day}{6}
      \field{issn}{2196-1115}
      \field{journaltitle}{Journal of Big Data}
      \field{langid}{english}
      \field{month}{7}
      \field{number}{1}
      \field{shortjournal}{J Big Data}
      \field{title}{A survey on Image Data Augmentation for Deep Learning}
      \field{urlday}{7}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{volume}{6}
      \field{year}{2019}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{60}
      \range{pages}{1}
      \verb{doi}
      \verb 10.1186/s40537-019-0197-0
      \endverb
      \verb{file}
      \verb Full Text PDF:/home/chris/Zotero/storage/DGSUG47U/Shorten and Khoshgoftaar - 2019 - A survey on Image Data Augmentation for Deep Learn.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb https://doi.org/10.1186/s40537-019-0197-0
      \endverb
      \verb{url}
      \verb https://doi.org/10.1186/s40537-019-0197-0
      \endverb
      \keyw{Data Augmentation,Big data,Deep Learning,{GANs},Image data}
    \endentry
    \entry{simonyan_deep_2014}{article}{}
      \name{author}{3}{}{%
        {{hash=9d16b7284df92c9adaee86c37ab992df}{%
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        {{hash=85ab53268da1006b51b453d57d3566f2}{%
           family={Vedaldi},
           familyi={V\bibinitperiod},
           given={Andrea},
           giveni={A\bibinitperiod}}}%
        {{hash=c72fc39e94030f67717052309266a44d}{%
           family={Zisserman},
           familyi={Z\bibinitperiod},
           given={Andrew},
           giveni={A\bibinitperiod}}}%
      }
      \strng{namehash}{400aa4a24354d63b49a6b7b6275bf2a4}
      \strng{fullhash}{400aa4a24354d63b49a6b7b6275bf2a4}
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      \field{sortinithash}{b164b07b29984b41daf1e85279fbc5ab}
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      \field{labeltitlesource}{shorttitle}
      \field{abstract}{This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks ({ConvNets}). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [Erhan et al., 2009], thus visualising the notion of the class, captured by a {ConvNet}. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification {ConvNets}. Finally, we establish the connection between the gradient-based {ConvNet} visualisation methods and deconvolutional networks [Zeiler et al., 2013].}
      \field{day}{19}
      \field{eprinttype}{arxiv}
      \field{journaltitle}{{arXiv}:1312.6034 [cs]}
      \field{month}{4}
      \field{shorttitle}{Deep Inside Convolutional Networks}
      \field{title}{Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps}
      \field{urlday}{3}
      \field{urlmonth}{2}
      \field{urlyear}{2022}
      \field{year}{2014}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{eprint}
      \verb 1312.6034
      \endverb
      \verb{file}
      \verb arXiv Fulltext PDF:/home/chris/Zotero/storage/GRKAIJIL/Simonyan et al. - 2014 - Deep Inside Convolutional Networks Visualising Im.pdf:application/pdf;arXiv.org Snapshot:/home/chris/Zotero/storage/5DMDR2SB/1312.html:text/html
      \endverb
      \verb{urlraw}
      \verb http://arxiv.org/abs/1312.6034
      \endverb
      \verb{url}
      \verb http://arxiv.org/abs/1312.6034
      \endverb
      \keyw{Computer Science - Computer Vision and Pattern Recognition}
    \endentry
    \entry{sirko_continental-scale_2021}{article}{}
      \name{author}{10}{}{%
        {{hash=2627f5ce7705e1e73df77daa8da43621}{%
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           given={Wojciech},
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        {{hash=e43d5132b90885fe0e69f58288449f98}{%
           family={Kashubin},
           familyi={K\bibinitperiod},
           given={Sergii},
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        {{hash=02ca56291c902c02cf20caa730c79895}{%
           family={Ritter},
           familyi={R\bibinitperiod},
           given={Marvin},
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        {{hash=63c95604d017a6780919d2e95afb3438}{%
           family={Annkah},
           familyi={A\bibinitperiod},
           given={Abigail},
           giveni={A\bibinitperiod}}}%
        {{hash=e10ed950a8691fb2ec10d63e58f75a41}{%
           family={Bouchareb},
           familyi={B\bibinitperiod},
           given={Yasser\bibnamedelimb Salah\bibnamedelima Eddine},
           giveni={Y\bibinitperiod\bibinitdelim S\bibinitperiod\bibinitdelim E\bibinitperiod}}}%
        {{hash=0bf1646a7a00f3a39fb857f25326c32c}{%
           family={Dauphin},
           familyi={D\bibinitperiod},
           given={Yann},
           giveni={Y\bibinitperiod}}}%
        {{hash=f1c8b9825fb787670cd8f32a95bf0158}{%
           family={Keysers},
           familyi={K\bibinitperiod},
           given={Daniel},
           giveni={D\bibinitperiod}}}%
        {{hash=cb0e8b168eb2c285ccada26468f51772}{%
           family={Neumann},
           familyi={N\bibinitperiod},
           given={Maxim},
           giveni={M\bibinitperiod}}}%
        {{hash=c55edd870afd5174d08d32e8560235f0}{%
           family={Cisse},
           familyi={C\bibinitperiod},
           given={Moustapha},
           giveni={M\bibinitperiod}}}%
        {{hash=957b0bc4aa5bfbe03d396e3808f4b324}{%
           family={Quinn},
           familyi={Q\bibinitperiod},
           given={John},
           giveni={J\bibinitperiod}}}%
      }
      \strng{namehash}{b956bcd0042d0ca6954ca014f422ffbb}
      \strng{fullhash}{ce7fd1fd34bde5162c2bbb89ef22cba6}
      \strng{bibnamehash}{b956bcd0042d0ca6954ca014f422ffbb}
      \strng{authorbibnamehash}{b956bcd0042d0ca6954ca014f422ffbb}
      \strng{authornamehash}{b956bcd0042d0ca6954ca014f422ffbb}
      \strng{authorfullhash}{ce7fd1fd34bde5162c2bbb89ef22cba6}
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      \field{abstract}{Identifying the locations and footprints of buildings is vital for many practical and scientific purposes. Such information can be particularly useful in developing regions where alternative data sources may be scarce. In this work, we describe a model training pipeline for detecting buildings across the entire continent of Africa, using 50 cm satellite imagery. Starting with the U-Net model, widely used in satellite image analysis, we study variations in architecture, loss functions, regularization, pre-training, self-training and post-processing that increase instance segmentation performance. Experiments were carried out using a dataset of 100k satellite images across Africa containing 1.75M manually labelled building instances, and further datasets for pre-training and self-training. We report novel methods for improving performance of building detection with this type of model, including the use of mixup ({mAP} +0.12) and self-training with soft {KL} loss ({mAP} +0.06). The resulting pipeline obtains good results even on a wide variety of challenging rural and urban contexts, and was used to create the Open Buildings dataset of 516M Africa-wide detected footprints.}
      \field{day}{29}
      \field{eprinttype}{arxiv}
      \field{journaltitle}{{arXiv}:2107.12283 [cs]}
      \field{month}{7}
      \field{title}{Continental-Scale Building Detection from High Resolution Satellite Imagery}
      \field{urlday}{8}
      \field{urlmonth}{9}
      \field{urlyear}{2021}
      \field{year}{2021}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \verb{eprint}
      \verb 2107.12283
      \endverb
      \verb{file}
      \verb arXiv.org Snapshot:/home/chris/Zotero/storage/IRKVLYMR/2107.html:text/html;arXiv Fulltext PDF:/home/chris/Zotero/storage/CLBHB775/Sirko et al. - 2021 - Continental-Scale Building Detection from High Res.pdf:application/pdf
      \endverb
      \verb{urlraw}
      \verb http://arxiv.org/abs/2107.12283
      \endverb
      \verb{url}
      \verb http://arxiv.org/abs/2107.12283
      \endverb
      \keyw{Computer Science - Computer Vision and Pattern Recognition}
    \endentry
    \entry{sliuzas_slum_2017}{inproceedings}{}
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      \field{abstract}{Very high resolution image data are required for detailed mapping and analysis in urban environments. Their widespread availability has driven attention for urban remote sensing applications in general and slum mapping in particular. Recently the use of unmanned aerial vehicles for urban/slum mapping has become reality. We provide a brief overview of slum mapping requirements and how these two data sources and related data extraction technologies are now being developed and used. Some examples of current practice and future research and application directions are highlighted.}
      \field{booktitle}{2017 Joint Urban Remote Sensing Event ({JURSE})}
      \field{eventtitle}{2017 Joint Urban Remote Sensing Event ({JURSE})}
      \field{month}{3}
      \field{title}{Slum mapping}
      \field{year}{2017}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1\bibrangedash 4}
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      \verb{file}
      \verb IEEE Xplore Abstract Record:/home/chris/Zotero/storage/FHXPDAY8/7924589.html:text/html
      \endverb
      \keyw{Image resolution,slum mapping,Urban areas,Satellites,Monitoring,Buildings,Context,data extraction,{UAV},Unmanned aerial vehicles,{VHR} images}
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    \entry{smith_disciplined_2018}{article}{}
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      \field{abstract}{Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-parameters, requiring unnecessarily long training times. Setting the hyper-parameters remains a black art that requires years of experience to acquire. This report proposes several efficient ways to set the hyper-parameters that significantly reduce training time and improves performance. Specifically, this report shows how to examine the training validation/test loss function for subtle clues of underfitting and overfitting and suggests guidelines for moving toward the optimal balance point. Then it discusses how to increase/decrease the learning rate/momentum to speed up training. Our experiments show that it is crucial to balance every manner of regularization for each dataset and architecture. Weight decay is used as a sample regularizer to show how its optimal value is tightly coupled with the learning rates and momentums. Files to help replicate the results reported here are available.}
      \field{day}{24}
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      \field{journaltitle}{{arXiv}:1803.09820 [cs, stat]}
      \field{month}{4}
      \field{shorttitle}{A disciplined approach to neural network hyper-parameters}
      \field{title}{A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay}
      \field{urlday}{15}
      \field{urlmonth}{3}
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      \field{year}{2018}
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      \keyw{Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Statistics - Machine Learning,Computer Science - Neural and Evolutionary Computing}
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      \field{abstract}{Accurate, precise and rapid acquisition of topographic data is fundamental to many sub-disciplines of physical geography. Technological developments over the past few decades have made fully distributed data sets of centimetric resolution and accuracy commonplace, yet the emergence of Structure from Motion ({SfM}) with Multi-View Stereo ({MVS}) in recent years has revolutionised three-dimensional topographic surveys in physical geography by democratising data collection and processing. {SfM}-{MVS} originates from the fields of computer vision and photogrammetry, requires minimal expensive equipment or specialist expertise and, under certain conditions, can produce point clouds of comparable quality to existing survey methods (e.g. Terrestrial Laser Scanning). Consequently, applications of {SfM}-{MVS} in physical geography have multiplied rapidly. There are many practical options available to physical geographers when planning a {SfM}-{MVS} survey (e.g. platforms, cameras, software), yet, many {SfM}-{MVS} end-users are uncertain as to the errors associated with each choice and, perhaps most fundamentally, the processes actually taking place as part of the {SfM}-{MVS} workflow. This paper details the typical workflow applied by {SfM}-{MVS} software packages, reviews practical details of implementing {SfM}-{MVS}, combines existing validation studies to assess practically achievable data quality and reviews the range of applications of {SfM}-{MVS} in physical geography. The flexibility of the {SfM}-{MVS} approach complicates attempts to validate {SfM}-{MVS} robustly as each individual validation study will use a different approach (e.g. platform, camera, georeferencing method, etc.). We highlight the need for greater transparency in {SfM}-{MVS} processing and enhanced ability to adjust parameters that determine survey quality. Looking forwards, future prospects of {SfM}-{MVS} in physical geography are identified through discussion of more recent developments in the fields of image analysis and computer vision.}
      \field{day}{1}
      \field{issn}{0309-1333}
      \field{journaltitle}{Progress in Physical Geography: Earth and Environment}
      \field{langid}{english}
      \field{month}{4}
      \field{note}{Publisher: {SAGE} Publications Ltd}
      \field{number}{2}
      \field{shortjournal}{Progress in Physical Geography: Earth and Environment}
      \field{title}{Structure from motion photogrammetry in physical geography}
      \field{urlday}{27}
      \field{urlmonth}{7}
      \field{urlyear}{2022}
      \field{volume}{40}
      \field{year}{2016}
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      \field{pages}{247\bibrangedash 275}
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      \verb https://doi.org/10.1177/0309133315615805
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      \verb{url}
      \verb https://doi.org/10.1177/0309133315615805
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      \keyw{Structure from motion,Multi-View Stereo,point cloud,topographic survey}
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    \entry{stevens_deep_2020}{book}{}
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      \field{isbn}{1-61729-526-4}
      \field{title}{Deep learning with {PyTorch}}
      \field{year}{2020}
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        {Springer Science \& Business Media}%
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      \field{isbn}{1-84882-935-3}
      \field{title}{Computer vision: algorithms and applications}
      \field{year}{2010}
      \field{dateera}{ce}
      \true{nocite}
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    \entry{tan_survey_2018}{inproceedings}{}
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        {{hash=59eaf27c32447b7323877072f8533d7c}{%
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        {{hash=a3e4cf51f9408eb5fff8461e5ab7caca}{%
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        {Cham}%
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        {Springer International Publishing}%
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      \field{abstract}{As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.}
      \field{booktitle}{Artificial Neural Networks and Machine Learning – {ICANN} 2018}
      \field{isbn}{978-3-030-01424-7}
      \field{langid}{english}
      \field{series}{Lecture Notes in Computer Science}
      \field{title}{A Survey on Deep Transfer Learning}
      \field{year}{2018}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{270\bibrangedash 279}
      \range{pages}{10}
      \verb{doi}
      \verb 10.1007/978-3-030-01424-7_27
      \endverb
      \verb{file}
      \verb Submitted Version:/home/chris/Zotero/storage/784HUIEX/Tan et al. - 2018 - A Survey on Deep Transfer Learning.pdf:application/pdf
      \endverb
      \keyw{Deep transfer learning,Survey,Transfer learning}
    \endentry
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      \field{abstract}{Convolutional Neural Networks ({ConvNets}) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up {MobileNets} and {ResNet}. To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called {EfficientNets}, which achieve much better accuracy and efficiency than previous {ConvNets}. In particular, our {EfficientNet}-B7 achieves state-of-the-art 84.3\% top-1 accuracy on {ImageNet}, while being 8.4x smaller and 6.1x faster on inference than the best existing {ConvNet}. Our {EfficientNets} also transfer well and achieve state-of-the-art accuracy on {CIFAR}-100 (91.7\%), Flowers (98.8\%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. Source code is at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet.}
      \field{day}{11}
      \field{eprinttype}{arxiv}
      \field{journaltitle}{{arXiv}:1905.11946 [cs, stat]}
      \field{month}{9}
      \field{shorttitle}{{EfficientNet}}
      \field{title}{{EfficientNet}: Rethinking Model Scaling for Convolutional Neural Networks}
      \field{urlday}{2}
      \field{urlmonth}{9}
      \field{urlyear}{2021}
      \field{year}{2020}
      \field{dateera}{ce}
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      \verb{eprint}
      \verb 1905.11946
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      \verb{file}
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      \keyw{Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,Statistics - Machine Learning}
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      \field{abstract}{The alignment, small-scale transitions and characteristics of buildings, streets and open spaces constitute a heterogeneous urban morphology. The urban morphology is the physical reflection of a society that created it, influenced by historical, social, cultural, economic, political, demographic and natural conditions as well as their developments. Within the complex urban environment homogeneous physical patterns and sectors of similar building types, structural alignments or similar built-up densities can be localized and classified. Accordingly, it is assumed that urban societies also feature a distinctive socio-economic urban morphology that is strongly correlated with the characteristics of a city's physical morphology: Social groups settle spatially with one's peer more or less segregated from other social groups according to, amongst other things, their economic status. This study focuses on the analysis, whether the static physical urban morphology correlates with socioeconomic parameters of its inhabitants here with the example indicators income and value of property. Therefore, the study explores on the capabilities of high resolution optical satellite data (Ikonos) to classify patterns of urban morphology based on physical parameters. In addition a household questionnaire was developed to investigate on the cities socioeconomic morphology.}
      \field{booktitle}{2009 Joint Urban Remote Sensing Event}
      \field{eventtitle}{2009 Joint Urban Remote Sensing Event}
      \field{month}{5}
      \field{title}{Integrating remote sensing and social science}
      \field{year}{2009}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1\bibrangedash 7}
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      \verb{doi}
      \verb 10.1109/URS.2009.5137506
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      \keyw{social science,Remote sensing,remote sensing,Optical sensors,Satellites,Optical reflection,Environmental economics,Buildings,buildings,Cultural differences,Demography,Economic indicators,Morphology,open spaces,social groups,social sciences,socio-economic effects,socioeconomic parameters,streets,urban morphology}
    \endentry
    \entry{taubenbock_morphology_2018}{article}{}
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        {{hash=d958ea4ee13dd3f0c3cd0a54e06ee9ef}{%
           family={Kraff},
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        {{hash=57f83b6db4f9395d6c3b28e37bb29a4a}{%
           family={Wurm},
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      \field{abstract}{When we think about living environments of the urban poor, slums might be the most immediate association. These slums evoke a more or less stereotype impression of built environments: complex, high dense alignments of small makeshift or run-down shelters. However, this perceived characteristic morphology is neither globally homogeneous nor is this perception covering morphologic appearances of urban poverty in a comprehensive way. This research provides an empirical baseline study of existing morphologies, their similarities and differences across the globe. To do so, we conceptually approach urban poverty as places which provide relatively cheap living spaces serving as possible access to the city, to its society and to its functions – so called Arrival Cities. Based on a systematic literature survey we select a sample of 44 Arrival Cities across the globe. Using very high resolution optical satellite data in combination with street view images and field work we derive level of detail-1 3D-building models for all study areas. We measure the spatial structure of these settlements by the spatial pattern (by three features – building density, building orientation and heterogeneity of the pattern) and the morphology of individual buildings (by two features – building size and height). We develop a morphologic settlement type index based on all five features allowing categorization of Arrival Cities. We find a large morphologic variety for built environments of the urban poor, from slum and slum-like structures to formal and planned structures. This variability is found on all continents, within countries and even within a single city. At the same time detected categories (such as slums) are found to have very similar physical features across the globe.}
      \field{day}{1}
      \field{issn}{0143-6228}
      \field{journaltitle}{Applied Geography}
      \field{langid}{english}
      \field{month}{3}
      \field{shortjournal}{Applied Geography}
      \field{title}{The morphology of the Arrival City - A global categorization based on literature surveys and remotely sensed data}
      \field{urlday}{20}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{volume}{92}
      \field{year}{2018}
      \field{dateera}{ce}
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      \true{nocite}
      \field{pages}{150\bibrangedash 167}
      \range{pages}{18}
      \verb{doi}
      \verb 10.1016/j.apgeog.2018.02.002
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      \verb{file}
      \verb Accepted Version:/home/chris/Zotero/storage/9XSIC3J7/Taubenböck et al. - 2018 - The morphology of the Arrival City - A global cate.pdf:application/pdf;ScienceDirect Snapshot:/home/chris/Zotero/storage/2XPFDEI6/S0143622817309955.html:text/html
      \endverb
      \verb{urlraw}
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      \verb{url}
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      \keyw{Remote sensing,Informal settlements,Slums,Building morphologies,Urban pattern,Urban poverty}
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    \entry{turner_what_2016}{article}{}
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      \field{labeltitlesource}{shorttitle}
      \field{abstract}{On a global scale, millions of refugees are contained in camps of one sort or another. This special issue and this introductory article explore what characterizes a camp and how camps affect the lives of those who are placed in them. It argues that the camp is an exceptional space that is put in place to deal with populations that disturb the national order of things. While being exceptional, the camp does not, however, produce bare life in an Agambenian sense. Life goes on in camps—albeit a life that is affected by the camp. Camps are defined along two dimensions: spatially and temporally. Spatially, camps always have boundaries, while in practice refugees and locals cross these boundaries for trade, employment, etc. Temporally, refugee camps are meant to be temporary, while in practice this temporariness may become permanent. The article proposes that camps may be explored along three dimensions. First, analyses of refugee camps must be attentive to the fact that a camp is at once a place of social dissolution and a place of new beginnings where sociality is remoulded in new ways. Second, we must explore the precarity of life in the camp by exploring relations to the future in this temporary space. Finally, the depoliticization of life that takes place in refugee camps due to humanitarian government, paradoxically also produces a hyper-politicized space where nothing is taken for granted and everything is contested.}
      \field{day}{1}
      \field{issn}{0951-6328}
      \field{journaltitle}{Journal of Refugee Studies}
      \field{month}{6}
      \field{number}{2}
      \field{shortjournal}{Journal of Refugee Studies}
      \field{shorttitle}{What Is a Refugee Camp?}
      \field{title}{What Is a Refugee Camp? Explorations of the Limits and Effects of the Camp}
      \field{urlday}{15}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{volume}{29}
      \field{year}{2016}
      \field{dateera}{ce}
      \field{urldateera}{ce}
      \true{nocite}
      \field{pages}{139\bibrangedash 148}
      \range{pages}{10}
      \verb{doi}
      \verb 10.1093/jrs/fev024
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      \verb{file}
      \verb Full Text PDF:/home/chris/Zotero/storage/BGFR32ZE/Turner - 2016 - What Is a Refugee Camp Explorations of the Limits.pdf:application/pdf;Snapshot:/home/chris/Zotero/storage/HZ6G3N2H/2362940.html:text/html
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      \verb https://doi.org/10.1093/jrs/fev024
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        {United Nations}%
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      \field{labeltitlesource}{title}
      \field{number}{A/{RES}/70/1}
      \field{title}{Transforming Our World: {THe} 2030 Agenda for Sustainable Development}
      \field{year}{2015}
      \field{dateera}{ce}
      \true{nocite}
    \endentry
    \entry{un_desa_world_2019}{report}{}
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      \list{institution}{1}{%
        {{UN} {DESA}}%
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      \list{location}{1}{%
        {New York}%
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      \strng{namehash}{e09dc26c2dba7fc84f90e1b22f5d3bd4}
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      \field{labeltitlesource}{title}
      \field{number}{{ST}/{ESA}/{SER}.A/420}
      \field{title}{World Urbanization Prospect: The 2018 Revision}
      \field{year}{2019}
      \field{dateera}{ce}
      \true{nocite}
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    \entry{unds_africa_2021}{report}{}
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      \list{institution}{1}{%
        {The Africa Regional United Nations Development System}%
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      \field{month}{5}
      \field{title}{The Africa Regional {UN} Development System Report 2020}
      \field{year}{2021}
      \field{dateera}{ce}
      \true{nocite}
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    \entry{unhcr_malawi_2014}{report}{}
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      \list{institution}{1}{%
        {UNHCR}%
      }
      \list{location}{1}{%
        {Lilongwe, Malawi}%
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      \field{langid}{english}
      \field{month}{11}
      \field{number}{{WFP}/{UNHCR}/{GOM}/{PRDO}/{JRS}/{PLAN}}
      \field{shorttitle}{Malawi}
      \field{title}{Malawi: Joint Assessment Mission Report. Dzaleka Refugee Camp (November 2014)}
      \field{urlday}{10}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{year}{2014}
      \field{dateera}{ce}
      \field{urldateera}{ce}
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      \verb{urlraw}
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      \verb{url}
      \verb https://www.unhcr.org/protection/health/5680f7d09/malawi-joint-assessment-mission-report-dzaleka-refugee-camp-november-2014.html
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      \field{title}{Global Compact on Refugees Indicator Report 2021}
      \field{year}{2021}
      \field{dateera}{ce}
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      \field{title}{{UNHCR} Global Report 2021}
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        {{hash=398e83438fa8e84105a76835481a9215}{%
           family={{DANIDA}},
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        {UNHCR}, {DANIDA}%
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        {Copenhagen}%
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      \field{month}{10}
      \field{number}{978-87-93760-22-6}
      \field{title}{The Integrated Solutions Model in and Around Kalobeyei, Turkana, Kenya}
      \field{urlday}{10}
      \field{urlmonth}{5}
      \field{urlyear}{2022}
      \field{year}{2019}
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    \entry{vakalopoulou_building_2015}{inproceedings}{}
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      \field{labeltitlesource}{title}
      \field{abstract}{The automated man-made object detection and building extraction from single satellite images is, still, one of the most challenging tasks for various urban planning and monitoring engineering applications. To this end, in this paper we propose an automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks. The core of the developed method is based on a supervised classification procedure employing a very large training dataset. An {MRF} model is then responsible for obtaining the optimal labels regarding the detection of scene buildings. The experimental results and the performed quantitative validation indicate the quite promising potentials of the developed approach.}
      \field{booktitle}{2015 {IEEE} International Geoscience and Remote Sensing Symposium ({IGARSS})}
      \field{eventtitle}{2015 {IEEE} International Geoscience and Remote Sensing Symposium ({IGARSS})}
      \field{month}{7}
      \field{note}{{ISSN}: 2153-7003}
      \field{title}{Building detection in very high resolution multispectral data with deep learning features}
      \field{year}{2015}
      \field{dateera}{ce}
      \true{nocite}
      \field{pages}{1873\bibrangedash 1876}
      \range{pages}{4}
      \verb{doi}
      \verb 10.1109/IGARSS.2015.7326158
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      \verb{file}
      \verb Submitted Version:/home/chris/Zotero/storage/DD8MSS3Z/Vakalopoulou et al. - 2015 - Building detection in very high resolution multisp.pdf:application/pdf;IEEE Xplore Abstract Record:/home/chris/Zotero/storage/X7V9SQR7/7326158.html:text/html
      \endverb
      \keyw{Remote sensing,Image resolution,object detection,remote sensing,Training,Satellites,Feature extraction,image resolution,image classification,Buildings,buildings (structures),Machine learning,building extraction,automated building detection framework,automated man-made object detection,deep convolutional networks,deep convolutional neural networks,deep learning features,extraction,{ImageNet},man made objects,{MRF} model,neural nets,optimal labels,quantitative validation,satellite images,scene building detection,supervised classification procedure,Support vector machines,town and country planning,training dataset,urban monitoring engineering applications,urban planning applications,very high resolution multispectral data,very high resolution remote sensing data}
    \endentry
    \entry{venables_urbanisation_2018}{article}{}
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           given={Anthony\bibnamedelima J.},
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      \field{abstract}{This article presents results from an investigation of the relationships between welfare and geographic metrics from over 14,000 villages in Assam, India. Geographic metrics accounted for 61\% of the variation in the lowest welfare quintile and 57\% in the highest welfare quintile. Travel time to market towns, percentage of a village covered with woodland, and percentage of a village covered with winter crop were significantly related to welfare. These results support findings in the literature across a range of different developing countries. Model accuracy is unprecedented considering that the majority of geographic metrics were derived from remotely sensed data.}
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      \field{journaltitle}{World Development}
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      \field{month}{2}
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      \field{shorttitle}{Understanding the Evidence Base for Poverty–Environment Relationships using Remotely Sensed Satellite Data}
      \field{title}{Understanding the Evidence Base for Poverty–Environment Relationships using Remotely Sensed Satellite Data: An Example from Assam, India}
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      \field{urlmonth}{9}
      \field{urlyear}{2022}
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      \keyw{environment,India,livelihoods,poverty,remote sensing}
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      \field{title}{Socioecologically informed use of remote sensing data to predict rural household poverty}
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      \field{title}{Remote sensing and {GIS} for ecologists: using open source software}
      \field{year}{2016}
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      \field{abstract}{In this paper we use image texture and morphological profiles for mapping slums in Sentinel-2A imagery. Varying sizes of the respective spatial descriptors ({GLCM}, differential morphological profiles) are tested for classification using a random forest classifier. Results are interpreted based on pixel-based and patch-based accuracy assessment. Best classification results have been reached at the pixel-based level with a kappa of 81.65 for the combined feature set with both {GLCM} and {DMP}. At the patch level, the analyses show that higher accuracies are reached with large kernel sizes and detection is better for large slum areas.}
      \field{booktitle}{2017 Joint Urban Remote Sensing Event ({JURSE})}
      \field{eventtitle}{2017 Joint Urban Remote Sensing Event ({JURSE})}
      \field{month}{3}
      \field{title}{Exploitation of textural and morphological image features in Sentinel-2A data for slum mapping}
      \field{year}{2017}
      \field{dateera}{ce}
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      \field{pages}{1\bibrangedash 4}
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      \keyw{{GLCM} texture,Image reconstruction,Image resolution,Image texture,Kernel,morphologic profiles,random forest,Sensitivity,Sentinel-2A,Size measurement,slum mapping,Urban areas}
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        {{hash=e5e6aebfefcbe0d7c5db556334617b20}{%
           family={Zhu},
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           given={Xiao\bibnamedelima Xiang},
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           family={Taubenböck},
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      \field{abstract}{Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolutional networks ({FCN}) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of {FCNs} to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from {QuickBird} is transferred to Sentinel-2 and {TerraSAR}-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. {TerraSAR}-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of {SAR} compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped slums in the optical data: {QuickBird} image obtains 86–88\% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55\% and from 79 to 85\% for {PPV} and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution.}
      \field{day}{1}
      \field{issn}{0924-2716}
      \field{journaltitle}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
      \field{langid}{english}
      \field{month}{4}
      \field{shortjournal}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
      \field{title}{Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks}
      \field{urlday}{17}
      \field{urlmonth}{7}
      \field{urlyear}{2022}
      \field{volume}{150}
      \field{year}{2019}
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      \keyw{Slums,Transfer learning,Convolutional neural networks,Deep learning,{FCN}}
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      \field{abstract}{Very high resolution ({VHR}) remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the {VHR} remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.}
      \field{journaltitle}{Remote Sensing}
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      \field{number}{1}
      \field{title}{Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters}
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      \field{urlmonth}{2}
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      \field{year}{2018}
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      \keyw{deep learning,building extraction,guided filter,very high resolution}
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      \field{title}{Welcome to Segmentation Models’s documentation! — Segmentation Models documentation}
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      \field{abstract}{Chan, C.Y., Weigand, M., Alnajar, E.A., \& Taubenböck, H. (2022). Investigating the capability of {UAV} imagery in {AI}-assisted mapping of Refugee Camps in East Africa. In: Minghini, M., Liu, P., Li, H., Grinberger, A.Y., \& Juhász, L. (Eds.). Proceedings of the Academic Track at State of the Map 2022, Florence, Italy, 19-21 August 2022. Available at https://zenodo.org/communities/sotm-22}
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      \field{abstract}{Contribution inequality widely exists in {OpenStreetMap} ({OSM}), which means that most data come from a minority of the contributors, while the majority only accounts for a small percentage of data. This phenomenon is of great importance to understanding from where the data come and how the project evolves. The investigation in this paper is dedicated to answering the following questions: How does contribution inequality change over time in {OSM}? Which group of contributors plays a more important role in influencing trends in contribution inequality: the “vocal minority” or the “silent majority”? To answer the first question, we provide overall measurements for contribution inequality using the Lorenz curve and the Gini coefficient. To answer the second question, we use quantile-based classifying strategy to analyze structural changes in the community, and use the Mann-Whitney-Wilcoxon test to analyze productivity changes. Our case study shows that in countries without significant imports, contributions become more unequal over time. This trend is consistent with the rapid expansion of the silent majority, even though other classes of contributors also grow at a slower pace. On the other hand, contribution inequality fluctuates a lot in countries with huge imports, and agrees well with the productivity changes in the vocal minority.}
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      \field{abstract}{Since deep neural networks were developed, they have made huge contributions to everyday lives. Machine learning provides more rational advice than humans are capable of in almost every aspect of daily life. However, despite this achievement, the design and training of neural networks are still challenging and unpredictable procedures. To lower the technical thresholds for common users, automated hyper-parameter optimization ({HPO}) has become a popular topic in both academic and industrial areas. This paper provides a review of the most essential topics on {HPO}. The first section introduces the key hyper-parameters related to model training and structure, and discusses their importance and methods to define the value range. Then, the research focuses on major optimization algorithms and their applicability, covering their efficiency and accuracy especially for deep learning networks. This study next reviews major services and toolkits for {HPO}, comparing their support for state-of-the-art searching algorithms, feasibility with major deep learning frameworks, and extensibility for new modules designed by users. The paper concludes with problems that exist when {HPO} is applied to deep learning, a comparison between optimization algorithms, and prominent approaches for model evaluation with limited computational resources.}
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      \field{title}{Building Outline Delineation: From Very High Resolution Remote Sensing Imagery to Polygons with an Improved End-to-End Learning Framework}
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      \field{abstract}{Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage remote-sensing scientists to bring their expertise into deep learning and use it as an implicit general model to tackle unprecedented, large-scale, influential challenges, such as climate change and urbanization.}
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      \field{journaltitle}{{IEEE} Geoscience and Remote Sensing Magazine}
      \field{month}{12}
      \field{number}{4}
      \field{shorttitle}{Deep Learning in Remote Sensing}
      \field{title}{Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources}
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      \field{year}{2017}
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      \keyw{Remote sensing,remote sensing,Feature extraction,climate change,Tutorials,Computer vision,data-intensive science,Hyperspectral imaging,looming paradigm shift,Machine learning,machine-learning techniques,remote-sensing data analysis}
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      \field{abstract}{Much research on object detection focuses on building better model architectures and detection algorithms. Changing the model architecture, however, comes at the cost of adding more complexity to inference, making models slower. Data augmentation, on the other hand, doesn’t add any inference complexity, but is insufficiently studied in object detection for two reasons. First it is more difficult to design plausible augmentation strategies for object detection than for classification, because one must handle the complexity of bounding boxes if geometric transformations are applied. Secondly, data augmentation attracts less research attention perhaps because it is believed to add less value and to transfer poorly compared to advances in network architectures.}
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