datasets.json

{  
    "UC1": [
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-04-21",
                "author": "Esther Vera, Paula Osés, Oriol Arroyo, Aldo Sollazzo, Chirag Rangholia",
                "date": "2023-04-21",
                "link": "https://zenodo.org/records/15114347",
                "description": "Crop monitoring dataset generated with DJI Mavic 3 Classic UAV and Metashape Agisoft that contains RGB images, pointcloud data, orthomosaic images and the DEM.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM.", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-04-28",
                "author": "Esther Vera, Paula Osés, Oriol Arroyo, Aldo Sollazzo, Chirag Rangholia",
                "date": "2023-04-28",
                "link": "https://zenodo.org/records/15124471",
                "description": "Crop monitoring dataset generated with DJI Mavic 3 Classic UAV and Metashape Agisoft that contains RGB images, pointcloud data, orthomosaic images and the DEM.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM.", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-05-05",
                "author": "Esther Vera, Oriol Arroyo, Aldo Sollazzo, Chirag Rangholia, Paula Osés",
                "date": "2023-05-05",
                "link": "https://zenodo.org/records/15124564",
                "description": "Crop monitoring dataset generated with DJI Mavic 3 Classic UAV and Metashape Agisoft that contains RGB images, pointcloud data, orthomosaic images and the DEM.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM.", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-06-09",
                "author": "Esther Vera, Oriol Arroyo, Aldo Sollazzo, Chirag Rangholia, Paula Osés",
                "date": "2023-06-09",
                "link": "https://zenodo.org/records/15064455",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM.", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UC1 DATA FOLDER GITHUB",
                "author": "Esther Vera, Aldo Sollazzo, Chirag Rangholia",
                "date": "2024-05-15",
                "link": "https://zenodo.org/records/8123870",
                "description": "This dataset contains the necessary data to execute the UC1_Crop_Monitoring codes in GitHub.",
                "parameters": {
                            "location": "Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "Features files with rows and plants positions for orthomosaic, health status, images for UC1 models and images generated.", 
                            "labels": "Yes"
                }  
            },
            {       
                "name": "VINEYARD PLANT LEAVES IMAGES (SUMMER 2024)",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2024-10-17",
                "link": "https://zenodo.org/records/13944498",
                "description": "Crop monitoring dataset generated with DJI Mavic Classic 3 UAV and Metashape Agisoft that contains vineyard images and RGB pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "Plant leaves images of summer 2024", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2024-08-30",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-05-08",
                "link": "https://zenodo.org/records/15173656",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2024-08-13",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-05-08",
                "link": "https://zenodo.org/records/15172492",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-08-31",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-03-24",
                "link": "https://zenodo.org/records/15075341",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR)", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-07-28",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-03-24",
                "link": "https://zenodo.org/records/15075338",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, G, RE)", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-06-09",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-03-21",
                "link": "https://zenodo.org/records/15064455",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, G, RE)", 
                            "labels": "No"
                }  
            },
            {       
                "name": "VINEYARD PLANTS LABELLED FOR DETECTION (HEALTHY, MILDEW, LOW-IRON)",
                "author": "Esther Vera, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-03-26",
                "link": "https://zenodo.org/records/15090766",
                "description": "This dataset consists of 508 images labeled for detecting plant health in vineyards. Each image is annotated with bounding boxes and has a resolution of 640 × 640 pixels. The dataset is formatted for YOLOv12 and was manually labeled using Roboflow. It is divided into 356 images for training, 102 images for validation, and 50 images for testing, ensuring a balanced distribution for model development and evaluation.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "508 labeled images of vineyard plant health: Healthy, Mildew, and Low-Iron Deficiency, for YOLOv12.", 
                            "labels": "Yes"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2024-06-18",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-03-19",
                "link": "https://zenodo.org/records/15046758",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2024-07-17",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-28",
                "link": "https://zenodo.org/records/15149667",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2024-07-03",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-28",
                "link": "https://zenodo.org/records/15147343",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }  
            },
            {   
                "name": "UAV Canyelles Vineyard Dataset 2024-05-30",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-28",
                "link": "https://zenodo.org/records/15124374",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-05-26",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-04",
                "link": "https://zenodo.org/records/15130400",
               "description": "Crop monitoring dataset generated with DJI Mavic 3 Classic UAV and Metashape Agisoft that contains RGB images, pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM.", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-05-18",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-04",
                "link": "https://zenodo.org/records/15120688",
                "description": "Crop monitoring dataset generated with DJI Mavic 3 Classic UAV and Metashape Agisoft that contains RGB images, pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2024-05-23",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-04",
                "link": "https://zenodo.org/records/15120063",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }   
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2023-04-28",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-04",
                "link": "https://zenodo.org/records/15124471",
                "description": "Crop monitoring dataset generated with DJI Mavic 3 Classic UAV and Metashape Agisoft that contains vineyard images and RGB pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM", 
                            "labels": "No"
                }  
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2024-08-13",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-08",
                "link": "https://zenodo.org/records/15172492",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }   
            },
            {       
                "name": "UAV Canyelles Vineyard Dataset 2024-07-31",
                "author": "Esther Vera, Albert Pedraza, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-04-08",
                "link": "https://zenodo.org/records/15166695",
                "description": "Crop monitoring dataset generated with DJI Mavic 3M UAV and Metashape Agisoft that contains vineyard images and RGB and multispectral pointcloud data, orthomosaic images and the DEM. This data is aligned with the rest of UAV Canyelles Vineyard Datasets uploaded by Noumena (UC1), so different orthomosaics / pointclouds / DEM of different dates can be analyzed jointly for cropped and uncropped files.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "RGB images, shapefile, pointclouds, orthomosaics, DEM in different bands (RGB, NIR, G, RE, R, NDVI)", 
                            "labels": "No"
                }   
            },
            {       
                "name": "UC1 DATA FOLDER - 3D POINT CLOUD ANALYSIS",
                "author": "Esther Vera, Aldo Sollazzo, Chirag Rangholia",
                "date": "2025-11-13",
                "link": "https://zenodo.org/records/17601303",
                "description": "Dataset containing rgb and ndvi downsampled and standardized point clouds, ground point clouds, plant point clouds, 2d grids, 3d grids and height, volume, density and color analysis data or 3d growth analysis over time. To be used with UC1 model 08.",
                "parameters": {
                            "location": "Canyelles, Barcelona, Spain",
                            "coordinates (wgs84)": "41.288687, 1.713078",
                            "sensor_type": ["RGB", "Multispectral"],
                            "contents": "Point clouds, 2D grid, 3D grids, analysis data", 
                            "labels": "No"
                }   
            }
          ],
    "UC2" :[
            {   
                "name": "UAV Spraying Parameters-Coverage in Vineyards",
                "author": "Vasilis Psiroukis",
                "date": "2023-12-06",
                "link": "https://zenodo.org/records/10276149",
                "description": "A set of UAV spraying data collected using WSPs, across different parameter configurations.",
                "parameters": {
                            "location": "Athens, Greece",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Water-Sensitive Paper (WSP)"],
                            "contents": "Spray coverage data, Volume Median Diameter (VMD) estimates", 
                            "labels": "No"
                }  
            }, 
            {   
                "name": "UAV Spraying Parameters-Coverage in Vineyards (2024 Experimental Year)",
                "author": "Vasilis Psiroukis",
                "date": "2024-12-05",
                "link": "https://zenodo.org/records/14280682",
                "description": "A set of UAV spraying data collected using WSPs, across different parameter configurations.",
                "parameters": {
                            "location": "Athens, Greece",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Water-Sensitive Paper (WSP)"],
                            "contents": "Spray coverage data, Volume Median Diameter (VMD) estimates", 
                            "labels": "No"
                }  
            },

            {
                 "name": "UAV Dataset containing RGB , Multispectral, Thermal images from Vineyard",
                 "author": "Vasilis Psiroukis",
                 "date": "03-06-2019",
                 "link": "https://zenodo.org/records/18678620",
                 "description": "This repository contains a processed image dataset acquired during drone-based surveys on Vineyard. The data were collected using RGB, Multispectral and Thermal cameras flown at 30m altitude over Vineyard. The images are from one field and differents on the capture days from seeding time. The resolution for the RGB is (4608x3456) and for Multispectral is (1280x960). ",
                 "parameters": {
                     "location": "Location description or coordinates: Nemea, Greece",
                     "coordinates (wgs84)": "37.8037739, 22.69408438888889",
                     "sensor type": ["RGB", "Multispectral", "Thermal"],
                     "contents": "UAV Data using RGB, Multispectral and Thermal cameras flown at 30m altitude over Vineyard",
                     "labels": "No"
                     }
             },
        
            {
                 "name": "UAV Dataset containing RGB, Multispectral and Thermal images fromTomato fields",
                 "author": "Vasilis Psiroukis",
                 "date": "07-06-2019",
                 "link": "https://zenodo.org/records/18679662",
                 "description": "This repository contains a processed image dataset acquired during drone-based surveys on Tomato fields. The data were collected using RGB, Multispectral and Thermal cameras flown at 30m altitude over Tomato fields. The images are from one field and differents on the capture days from seeding time. The resolution for the RGB is (4608x3456) and for Multispectral is (1280x960). ",
                 "parameters": {
                     "location": "Location description or coordinates: Thessaly, Greece",
                     "coordinates (wgs84)": "39.3653056, 22.303275",
                     "sensor type": ["RGB", "Multispectral", "Thermal"],
                     "contents": "UAV Data using RGB,Multispectral and Thermal cameras flown at 30m altitude over Tomato fields",
                     "labels": "No"
                 }
             },
            {
             "name": "UAV Dataset containing RGB , Multispectral images from cotton crops",
             "author": "Vasilis Psiroukis",
             "date": "03-07-2020",
             "link": "https://zenodo.org/records/18801595",
             "description": "This repository contains a processed image dataset acquired during drone-based surveys on cotton fields. The data were collected using RGB, and multispectral camera flown at 70m altitude over a cotton field. The images are from one field and differents on the capture days from seeding time. The resolution for the RGB is (4608x3456) and for Multispectral is (1280x960). ",
             "parameters": {
                 "location": "Location description or coordinates: Farsala, Greece",
                 "coordinates (wgs84)": "39.329872, 22.515747222222224",
                 "sensor type": ["RGB", "Multispectral"],
                 "contents": "UAV Data using RGB and Multispectral cameras flown at 70m altitude over Cotton fields",
                 "labels": "No"
                 }
            }
          ],

    "UC3": [
            {   
                "name": "Drone raw images of cattle in french grazing areas",
                "author": "Adrien Lebreton",
                "date": "2023-08-10",
                "link": "https://zenodo.org/records/8234156",
                "description": "The dataset encompasses around 900 raw .jpeg drone images of grazing areas where cattle graze collected between June and August 2023.",
                "parameters": {
                            "location": "France",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB", "Thermal"],
                            "contents": "Drone images, flight metadata",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Sheep videos taken from drone at low altitude",
                "author": "Adrien Lebreton, Louise Helary",
                "date": "2023-12-18",
                "link": "https://zenodo.org/records/10400302",
                "description": "The dataset encompasses 16 .MP4 videos from drone (DJI mavic 3 Enterprise or Thermal) of around 50 sheep crossing a gate.",
                "parameters": {
                            "location": "France",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB", "Thermal"],
                            "contents": "Drone videos",
                            "labels": "No"
                }  
            }, 

            {   
                "name": "Drone images and their annotations of grazing cows",
                "author": "Adrien Lebreton, Louise Helary",
                "date": "2024-04-24",
                "link": "https://zenodo.org/records/11048412",
                "description": "It contains 1385 images and 4941 bounding boxes of cows taken with a DJI Pilot 2 drone. This dataset is a major contribution for public and research stakeholders to develop cow detection and counting models.",
                "parameters": {
                            "location": "France",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB"],
                            "contents": "Drone images, flight metadata, bounding boxes annotations",
                            "labels": "Yes"
                }  
            }, 
            {   
                "name": "Drone videos and their annotations of passing sheep (for counting purpose)",
                "author": "Louise Helary, Kinsey Nnenna, Marta Kolodziejczyk, Jana Schewe, Léa Philip, Estelle Nicolas, Adrien Lebreton",
                "date": "2024-06-18",
                "link": "https://zenodo.org/records/12094356",
                "description": "The dataset encompasses 4 .MP4 videos from drone (DJI mavic 3 Enterprise or Thermal) of around 50 sheep crossing a gate.",
                "parameters": {
                            "location": "France",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB", "Thermal"],
                            "contents": "Drone videos, flight metadata, annotations in YOLO and MOTS", 
                            "labels": "Yes"
                }  
            },  
            {   
                "name": "Aerial videos and images of goats (for computer vision purpose)",
                "author": "Adrien Lebreton, Laurence Depuille, Estelle Nicolas, Louise Helary",
                "date": "2025-01-03",
                "link": "https://zenodo.org/records/14591324",
                "description": "The dataset contains drone-captured videos and images from two goat farms in France, featuring varied settings and resolutions.",
                "parameters": {
                            "location": "Ardèche, France",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB"],
                            "contents": "Drone videos, RGB images", 
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Drone images and their annotations of goats/small ruminants (for computer vision purpose)",
                "author": "Adrien Lebreton, Léane Duval, Laurence Depuille, Estelle Nicolas, Louise Helary",
                "date": "2025-02-26",
                "link": "https://zenodo.org/records/14929694",
                "description": "This dataset includes annotated drone videos and images of sheep and goats with YOLO-labeled bounding boxes for computer vision detection.",
                "parameters": {
                            "location": "Ardèche, France",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB"],
                            "contents": "Drone images, flight metadata, YOLO annotations", 
                            "labels": "Yes"
                }  
            }, 
            {   
                "name": "Drone videos and images of sheep in various conditions (for computer vision purpose)",
                "author": "Adrien Lebreton, Coline Morin, Estelle Nicolas, Louise Helary",
                "date": "2025-03-26",
                "link": "https://zenodo.org/records/14967219",
                "description": "Counting sheep and goats is a significant challenge for farmers managing flocks with hundreds of animals. Our objective is to develop a computer vision-based methodology to count sheep and goats as they pass through a corridor or gate. This approach utilizes low-altitude aerial videos (<15 m) recorded by drones.",
                "parameters": {
                            "location": "Alpes de Haute Provence and Limousin, France",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB"],
                            "contents": "Drone videos, RGB images", 
                            "labels": "No"
                }  
            },
        {
                "name": "Drone videos and images of sheep in various conditions (for computer vision purpose) - Part II",
                "author": "Adrien Lebreton, Laurent Helary, E. Nicolas, L. Goin, P.-G. Grisot, T. Jegorel",
                "date": "2026-03-06",
                "link": "https://zenodo.org/records/18889354",
                "description": "This dataset is the second part of 'Drone videos and images of sheep in various conditions (for computer vision purpose)' and offers a large diversity of sheep aerial images across farms and seasons. It includes 1679 images and 47 videos, featuring background diversity (grey rangelands and diverse green pastures), lighting diversity, and both dispersed and highly clustered groups of sheep. Additional objects such as dogs, humans, and farm equipment are also visible in some scenes.",
                "parameters": {
                    "location": "Alpes de Haute Provence and Limousin, France",
                    "coordinates (wgs84)": "",
                    "sensor_type": ["RGB"],
                    "contents": "RGB images, Drone videos, flight metadata",
                    "labels": "No"
                }
            },
            {
                "name": "Drone videos to test sheep counting computer vision pipeline",
                "author": "Adrien Lebreton, P.-G. Grisot, L. Depuille, L. Goin, E. Nicolas, Laurent Helary",
                "date": "2026-03-06",
                "link": "https://zenodo.org/records/18889878",
                "description": "This dataset provides 98 videos (79 post-processed and 19 raw) of small ruminant flocks funneled through passages, designed to test and evaluate a sheep detection, tracking, and counting pipeline using a virtual counting line. The videos are intended for use with a computer vision pipeline, with a Python GUI demonstrator available for simple testing.",
                "parameters": {
                    "location": "France",
                    "coordinates (wgs84)": "",
                    "sensor_type": ["RGB"],
                    "contents": "Drone videos, flight metadata",
                    "labels": "No"
                }
            },
            {
                "name": "Drone images and their annotations of sheep in various conditions (for computer vision purpose)",
                "author": "Adrien Lebreton, A. de Brito, E. Blaise, T. Jegorel, L. Goin, P.-G. Grisot, E. Nicolas, Laurent Helary",
                "date": "2026-03-06",
                "link": "https://zenodo.org/records/18889623",
                "description": "This dataset provides 809 annotated drone images of sheep in various conditions, with 18,018 sheep bounding boxes in Ultralytics YOLO Detection 1.0 format. It is designed for training or evaluating object detection models, and includes both nadir and oblique views, as well as evaluation subsets for model benchmarking.",
                "parameters": {
                    "location": "France",
                    "coordinates (wgs84)": "",
                    "sensor_type": ["RGB"],
                    "contents": "Drone images, annotations (YOLO format), flight metadata",
                    "labels": "Yes"
                }
            }       

           ],

    "UC4": [
            {   
                "name": "Forestry and Biodiversity monitoring in Lithuania with hyperspectral camera and UAV",
                "author": "BETAVIA",
                "date": "2023-09-11",
                "link": "https://zenodo.org/records/8333789",
                "description": "Hyperspectral imaging acquired with UAV on different dates of a pine tree forest.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "Drone hyperspectral images, calibration images, hyperspectral data cubes, flight metadata", 
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with thermal camera over Lithuanian forests in winter for wild boar detection",
                "author": "BETAVIA",
                "date": "2024-07-15",
                "link": "https://zenodo.org/records/12743334",
                "description": "The dataset consists of thermal images captured by DJI drones over Lithuanian forests in winter, used for wild boar detection",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Thermal"],
                            "contents": "Thermal videos, flight metadata", 
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #1",
                "author": "BETAVIA",
                "date": "2024-09-27",
                "link": "https://zenodo.org/records/13846686",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #12",
                "author": "BETAVIA",
                "date": "2024-09-27",
                "link": "https://zenodo.org/records/15063168",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #11",
                "author": "BETAVIA",
                "date": "2025-03-21",
                "link": "https://zenodo.org/records/15063160",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #10",
                "author": "BETAVIA",
                "date": "2025-03-21",
                "link": "https://zenodo.org/records/15063121",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #9",
                "author": "BETAVIA",
                "date": "2025-03-21",
                "link": "https://zenodo.org/records/15063049",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            },          
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #8",
                "author": "BETAVIA",
                "date": "2025-03-21",
                "link": "https://zenodo.org/records/15063013",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #7",
                "author": "BETAVIA",
                "date": "2025-03-21",
                "link": "https://zenodo.org/records/15062953",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #5",
                "author": "BETAVIA",
                "date": "2024-12-20",
                "link": "https://zenodo.org/records/14535422",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #6",
                "author": "BETAVIA",
                "date": "2024-12-20",
                "link": "https://zenodo.org/records/14535456",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #4",
                "author": "ART21",
                "date": "2024-12-20",
                "link": "https://zenodo.org/records/14535368",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            },
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #13",
                "author": "ART21",
                "date": "2025-09-17",
                "link": "https://zenodo.org/records/17142640",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            },
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #14",
                "author": "ART21",
                "date": "2025-09-17",
                "link": "https://zenodo.org/records/17142746",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            },
            {   
                "name": "Multiple DJI drone flights with hyperspectral camera over forests in Lithuania #15",
                "author": "ART21",
                "date": "2025-09-17",
                "link": "https://zenodo.org/records/17142780",
                "description": "The dataset contains processed hyperspectral data cubes in ENVI format, with both georectified and non-georectified files, along with generated RGB and NDVI images.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Hyperspectral"],
                            "contents": "RGB images, NDVI images, hyperspectral data cubes, georectified data",
                            "labels": "No"
                }  
            },
            {   
                "name": "DJI Mavic 3 Thermo flights over Lithuanian forests #1",
                "author": "UAB Beta via",
                "date": "2025-10-10",
                "link": "https://zenodo.org/records/17310544",
                "description": "Dataset of DJI Mavic 3 Thermal drone flights over Lithuanian forests, including thermal videos, visual and thermal images, and GPS metadata for wildlife monitoring.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Thermal", "RGB"],
                            "contents": "Thermal videos, metadata, thermal/RGB images",
                            "labels": "No"
                }  
            },
            {   
                "name": "DJI Mavic 3 Thermo flights over Lithuanian forests #2",
                "author": "UAB Beta via",
                "date": "2025-10-10",
                "link": "https://zenodo.org/records/17311014",
                "description": "Dataset of DJI Mavic 3 Thermal drone flights over Lithuanian forests, including thermal videos, visual and thermal images, and GPS metadata for wildlife monitoring.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Thermal", "RGB"],
                            "contents": "Thermal videos, metadata, thermal/RGB images",
                            "labels": "No"
                }  
            },
            {   
                "name": "DJI Mavic 3 Thermo flights over Lithuanian forests #3",
                "author": "UAB Beta via",
                "date": "2025-10-10",
                "link": "https://zenodo.org/records/17311189",
                "description": "Dataset of DJI Mavic 3 Thermal drone flights over Lithuanian forests, including thermal videos, visual and thermal images, and GPS metadata for wildlife monitoring.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Thermal", "RGB"],
                            "contents": "Thermal videos, metadata, thermal/RGB images",
                            "labels": "No"
                }  
            },      
            {   
                "name": "DJI Mavic 3 Thermo flights over Lithuanian forests #4",
                "author": "UAB Beta via",
                "date": "2025-10-10",
                "link": "https://zenodo.org/records/17311327",
                "description": "Dataset of DJI Mavic 3 Thermal drone flights over Lithuanian forests, including thermal videos, visual and thermal images, and GPS metadata for unhealthy forest monitoring.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Thermal", "RGB"],
                            "contents": "Thermal videos, metadata, thermal/RGB images",
                            "labels": "No"
                }  
            }, 
            {   
                "name": "DJI Mavic 3 Thermo flights over Lithuanian forests #4",
                "author": "UAB Beta via",
                "date": "2025-10-10",
                "link": "https://zenodo.org/records/17311890",
                "description": "Dataset of DJI Mavic 3 Thermal drone flights over Lithuanian forests, including thermal videos, visual and thermal images, and GPS metadata for unhealthy forest monitoring.",
                "parameters": {
                            "location": "Lithuania",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["Thermal", "RGB"],
                            "contents": "Thermal videos, metadata, thermal/RGB images",
                            "labels": "No"
                }  
            }
           ],

    "UC5" :[
            {   
                "name": "Photogrammetry Orthomosaic and Digital Surface Model (Strumica, North Macedonia)",
                "author": "GEOSENSE, AGFUTURA",
                "date": "2024-07-26",
                "link": "https://zenodo.org/records/12909590",
                "description": "A sample dataset from Strumica, North Macedonia, featuring photogrammetric orthomosaics and a Digital Surface Models (DSM).",
                "parameters": {
                            "location": "Strumica, North Macedonia",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, 3D pointcloud, DEM, orthomosaics",
                            "labels": "No"
                }  
            },   
            {   
                "name": "Photogrammetry Orthomosaic and Digital Surface Model 2 (Strumica, North Macedonia)",
                "author": "GEOSENSE",
                "date": "2024-10-24",
                "link": "https://zenodo.org/records/12772799",
                "description": "A sample dataset from Strumica, North Macedonia, featuring photogrammetric orthomosaics and a Digital Surface Models (DSM).",
                "parameters": {
                            "location": "Strumica, North Macedonia",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, 3D pointcloud, DEM, orthomosaics",
                            "labels": "No"
                }  
            }, 
            {   
               "name": "ICAERUS - UC5 - Enhanced Payload Data for Drone Logistics in Rural Areas",
                "author": "GEOSENSE",
                "date": "2024-12-11",
                "link": "https://zenodo.org/records/12909590",
                "description": "Dataset that supports the development and operation of the DaeDaLus (Drone Delivery Logistics Services) platform.",
                "parameters": {
                            "location": "Strumica, North Macedonia",
                            "coordinates (wgs84)": "",
                            "sensor_type": ["RGB"],
                            "contents": "RGB images, 3D pointcloud, DEM, orthomosaics",
                            "labels": "No"
                }  
            },
            {       
                "name": "Drone Processed Images for Coastal Litter Detection (RGB, Thermal, Multispectral)",
                "author": "GEOSENSE IKE, Harokopio University of Athens",
                "date": "2025",
                "link": "https://zenodo.org/records/17805043",
                "description": "Processed image dataset acquired during drone-based surveys for coastal litter pollution detection. Data collected using RGB, thermal, and multispectral camera payloads flown at multiple altitudes (30m, 60m, 90m, 120m) over coastal environments with varying shoreline morphology and litter density.",
                "parameters": {
                    "location": "",
                    "coordinates (wgs84)": "",
                    "sensor_type": ["RGB", "Thermal", "Multispectral"],
                    "contents": "Processed RGB images, thermal images, multispectral images at multiple altitudes (30m, 60m, 90m, 120m).", 
                    "labels": "No"
                }  
        }
           ],

    "OCT - Agrobit Srl": [
                          {   
                            "name": "UAV AgroTwin Vineyard Dataset 2024",
                            "author": "Simone Kartsiotis, Antonio Donnangelo",
                            "date": "2024-06-19",
                            "link": "https://zenodo.org/records/12168516",
                            "description": "The datasets contains RGB images and pointclouds of a vineyard field.",
                            "parameters": {
                                        "location": "Strumica, North Macedonia",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["RGB"],
                                        "contents": "RGB images, 3D pointclouds",
                                        "labels": "No"
                            }  
                        }
                      ],
    
    "OCT - TAAL": [
                    {   
                        "name": "3D cloud reconstruction and 2D NDVI index",
                        "author": "Taal S.r.l.",
                        "date": "2024-07-26",
                        "link": "https://zenodo.org/records/12954801",
                        "description": "The dataset contains 3D pointclouds and 2D NDVI maps of an olive grove.",
                        "parameters": {
                                    "location": "Rome, Italy",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": ["RGB", "Multispectral"],
                                    "contents": "3D pointclouds, 2D NDVI maps",
                                    "labels": "No"
                        }  
                    }, 
                    {   
                        "name": "raw drone sensing data - 1",
                        "author": "Taal S.r.l.",
                        "date": "2024-07-26",
                        "link": "https://zenodo.org/records/12943015",
                        "description": "It includes RGB and multispectral images collected with the DJI Mavic 3M in an olive grove, along with flight mission files and GIS information, organized by individual drone flights in separate folders.",
                        "parameters": {
                                    "location": "Rome, Italy",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": ["RGB", "Multispectral"],
                                    "contents": "RGB images, multispectral images (R, RE, G, NIR), flight metadata",
                                    "labels": "No"
                        }  
                    },  
                    {   
                        "name": "raw drone sensing data - 2",
                        "author": "Taal S.r.l.",
                        "date": "2024-07-26",
                        "link": "https://zenodo.org/records/12924461",
                        "description": "It includes RGB and multispectral images collected with the DJI Mavic 3M in an olive grove, along with flight mission files and GIS information, organized by individual drone flights in separate folders.",
                        "parameters": {
                                    "location": "Rome, Italy",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": ["RGB", "Multispectral"],
                                    "contents": "RGB images, multispectral images (R, RE, G, NIR), flight metadata",
                                    "labels": "No"
                        }  
                    }, 
                    {   
                        "name": "Ground Truth Data",
                        "author": "Taal S.r.l.",
                        "date": "2024-07-29",
                        "link": "https://zenodo.org/records/13120763",
                        "description": "The dataset features a dataset of labeled RGB and NDVI olive tree images for training classification models, with health statuses detailed in three CSV files.",
                        "parameters": {
                                    "location": "Rome, Italy",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": ["RGB"],
                                    "contents": "RGB images, augmented images, CSV label files",
                                    "labels": "Yes"
                        }   
                    }, 
                    {   
                        "name": "Tree detected",
                        "author": "Taal S.r.l.",
                        "date": "2024-07-29",
                        "link": "https://zenodo.org/records/13121962",
                        "description": "The repository contains a dataset of labeled olive trees with bounding boxes used for training detection models.",
                        "parameters": {
                                    "location": "Rome, Italy",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": ["RGB"],
                                    "contents": "RGB images, augmented images, CSV label files",
                                    "labels": "Yes"
                        }   
                    }, 
                    {   
                        "name": "Plant Health Status",
                        "author": "Taal S.r.l.",
                        "date": "2024-07-30",
                        "link": "https://zenodo.org/records/13132625",
                        "description": "This dataset contains the xylella classifier results to identify asymptomatic health status, effectively differentiating it from mild and evident symptomatic conditions in olive trees.",
                        "parameters": {
                                    "location": "Rome, Italy",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": [""],
                                    "contents": "Prediction results",
                                    "labels": "No"
                        }   
                    }
                  ],

    "OCT - SHIELD": [
                     {   
                        "name": "SHIELD Demonstrations Dataset",
                        "author": "Obsidian Innovation Institute - Associação",
                        "date": "2024-07-31",
                        "link": "https://zenodo.org/records/13146217",
                        "description": "This dataset contains the NMEA sentences generated during the Secure and High Integrity EGNSS enabled drone (SHIELD) demonstration campaing and concerning the different demonstration scenarios.",
                        "parameters": {
                                    "location": "Portugal",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": ["U-blox NEO-F9P receiver"],
                                    "contents": "NMEA sentences",
                                    "labels": "No"
                        }   
                    }
                   ],
    "OCT - DAEPHC": [
                     {   
                        "name": "Datasets from hop field 3 (orthomosaic of RGB, multispectral band and thermal sensor) from July 5 2024",
                        "author": "Matej Knapic",
                        "date": "2025-04-25",
                        "link": "https://zenodo.org/records/15280042",
                        "description": "The upload consists of 2 zip files. First one has orthomosaic of RGB, 4 multispectral bands and the second one the orhomosaic of thermal dataand RGB  prepared in PIX4D mapper software. The images were taken from 40 m height. The images are normalized with white reference plate. The thermal images were converted before mosaicing from sensor units to °C with DJI Image Processor Version v1.6.2 developed by Miro Rava. All data are in Slovenian coordinate system EPSG=3794.",
                        "parameters": {
                                    "location": "Slovenia",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": ["RGB", "Multispectral", "Thermal"],
                                    "contents": "RGB orthomosaics, multispectral orthomosaics, thermal data",
                                    "labels": "No"
                        }   
                     }, 
                     {   
                        "name": "Datasets from hop field 2 (orthomosaic of RGB, multispectral band and thermal sensor) from July 9 2024",
                        "author": "Matej Knapic",
                        "date": "2025-04-25",
                        "link": "https://zenodo.org/records/14761567",
                        "description": "The upload consists of 2 zip files. First one has orthomosaic of RGB, 4 multispectral bands and the second one the orhomosaic of thermal dataand RGB  prepared in PIX4D mapper software. The images were taken from 40 m height. The images are normalized with white reference plate. The thermal images were converted before mosaicing from sensor units to °C with DJI Image Processor Version v1.6.2 developed by Miro Rava. All data are in Slovenian coordinate system EPSG=3794.",
                        "parameters": {
                                    "location": "Slovenia",
                                    "coordinates (wgs84)": "",
                                    "sensor_type": ["RGB", "Multispectral", "Thermal"],
                                    "contents": "RGB orthomosaics, multispectral orthomosaics, thermal data",
                                    "labels": "No"
                        }   
                    }, 
                    {   
                        "name": "Datasets from hop field 1 (orthomosaic of RGB, multispectral band and thermal sensor) from June 28  2024",
                        "author": "Matej Knapic",
                        "date": "2025-04-25",
                        "link": "https://zenodo.org/records/14731003",
                        "description": "The upload consists of 2 zip files. First one has orthomosaic of RGB, 4 multispectral bands and the second one the orhomosaic of thermal dataand RGB  prepared in PIX4D mapper software. The images were taken from 40 m height. The images are normalized with white reference plate. The thermal images were converted before mosaicing from sensor units to °C with DJI Image Processor Version v1.6.2 developed by Miro Rava. All data are in Slovenian coordinate system EPSG=3794.",
                        "parameters": {
                                "location": "Slovenia",
                                "coordinates (wgs84)": "",
                                "sensor_type": ["RGB", "Multispectral", "Thermal"],
                                "contents": "RGB orthomosaics, multispectral orthomosaics, thermal data",
                                "labels": "No"
                            }   
                        } 
                      ],
    "OCT - LowAltitude": [
                          {   
                            "name": "Multispectral imaging of a UK Vineyard June 24, part 1",
                            "author": "Chris Crane",
                            "date": "2024-06-02",
                            "link": "https://zenodo.org/records/14328464",
                            "description": "A dataset of multipectral images and drone flight logs.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["RGB", "Multispectral"],
                                        "contents": "RGB images, multispectral images, drone flight logs",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "Multispectral data set of a UK Vineyard June 2024 part 2",
                            "author": "Chris Crane",
                            "date": "2024-06-02",
                            "link": "https://zenodo.org/records/14334273",
                            "description": "Part 2 of the multispectral dataset that contains images and drone flight logs.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["RGB", "Multispectral"],
                                        "contents": "RGB images, drone flight logs",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "Multispectral dataset of a Vineyard, UK, July 2024 Part 1 and 2",
                            "author": "Chris Crane",
                            "date": "2024-07-07",
                            "link": "https://zenodo.org/records/14281334",
                            "description": "Parts 1 and 2 of multispectral dataset, along with drone flight files.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["RGB", "Multispectral"],
                                        "contents": "RGB images, drone flight logs",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "Multispectral dataset of vineyard, July 24 Part 3",
                            "author": "Chris Crane",
                            "date": "2024-07-14",
                            "link": "https://zenodo.org/records/14284315",
                            "description": "Part 3 of multispectral dataset, along with drone flight logs.",                           
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["RGB", "Multispectral", "Thermal"],
                                        "contents": "RGB orthomosaics, multispectral orthomosaics, thermal data",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "Lidar files generated from UAV flight over vineyard trial block",
                            "author": "Chris Crane",
                            "date": "2024-07-14",
                            "link": "https://zenodo.org/records/14266547",
                            "description": "Lidar files collected using Zenmuse L2 lidar mounted on DJI Matrice 350 RTK drone.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["LiDAR"],
                                        "contents": "Lidar pointcloud",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "GeoTIFF for Vineyard, generated from Multispectral survey; Part 1",
                            "author": "Chris Crane",
                            "date": "2024-07-14",
                            "link": "https://zenodo.org/records/14355958",
                            "description": "The GeoTiffs created from each vegetation index, as an output of the multispectral surveys.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["Multispectral"],
                                        "contents": "Vegetation index GeoTIFFs",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "Lidar data set of a vineyard",
                            "author": "Chris Crane",
                            "date": "2024-08-16",
                            "link": "https://zenodo.org/records/14269622",
                            "description": "Lidar files for a full vineyard, that can be used to create a 3d model.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["LiDAR"],
                                        "contents": "Lidar pointcloud",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "Multispectral data set including images and drone logs, vineyard, September 2024: PART 1",
                            "author": "Chris Crane",
                            "date": "2024-09-01",
                            "link": "https://zenodo.org/records/14274523",
                            "description": "Multispectral data including drone log files of a UK vineyard.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["RGB"],
                                        "contents": "RGB images, drone flight logs",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "Lidar dataset including drone logs, Vineyard Sept 2024 parts 2 and 3",
                            "author": "Chris Crane",
                            "date": "2024-09-01",
                            "link": "https://zenodo.org/records/14278527",
                            "description": "Multispectral and RGB image datasets, parts 2 and 3.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["RGB", "Multispectral"],
                                        "contents": "RGB images, multispectral images, drone flight logs",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "GEOTiff of vegetation indexes from Multispectral Vineyard surveys PART 2",
                            "author": "Chris Crane",
                            "date": "2024-09-01",
                            "link": "https://zenodo.org/records/14360892",
                            "description": "The GeoTiffs created from each vegetation index, as an output of the multispectral surveys.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["Multispectral"],
                                        "contents": "GeoTIFFs of vegetation indexes",
                                        "labels": "No"
                            }   
                          }, 
                          {   
                            "name": "Vineyard block boundaries",
                            "author": "Chris Crane",
                            "date": "2024-12-10",
                            "link": "https://zenodo.org/records/14354921",
                            "description": "Zip file of vineyard block boundaries in various file types, including shape.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": ["LiDAR"],
                                        "contents": "Shapefile",
                                        "labels": "No"
                            }   
                         }, 
                         {   
                            "name": "PDF reports showing outcome of multispectral survey of Vineyard",
                            "author": "Chris Crane",
                            "date": "2024-12-10",
                            "link": "https://zenodo.org/records/14354749",
                            "description": "The pdf output reports generated in Pix4dFields of each multispectral vineyard surveys conducted.",
                            "parameters": {
                                        "location": "UK",
                                        "coordinates (wgs84)": "",
                                        "sensor_type": [""],
                                        "contents": "PDF report, vegetation index statistics",
                                        "labels": "No"
                            }   
                        }
                      ],

    "OCT - InnovPlantProtect": [
                                {   
                                    "name": "Raw DJI Mavic 3M RTK RGB and Multispectral drone imagery collected on 2024-05-28",
                                    "author": "iLaria Marengo, Nuno Faria",
                                    "date": "2024-01-06",
                                    "link": "https://zenodo.org/records/14604638",
                                    "description": "High-resolution RGB and multispectral drone imagery of six Alentejo vineyards, collected in 2024 and georeferenced manually.",
                                    "parameters": {
                                                "location": "Alentejo, Portugal",
                                                "coordinates (wgs84)": "",
                                                "sensor_type": ["RGB", "Multispectral"],
                                                "contents": "RGB images, multispectral images, flight metadata",
                                                "labels": "No"
                                    }   
                                }, 
                                {   
                                    "name": "RGB and Multispectral orthomosaics obtained by processing raw imagery (dated 20240528) in webODM software",
                                    "author": "iLaria Marengo, Nuno Faria",
                                    "date": "2024-01-06",
                                    "link": "https://zenodo.org/records/14605551",
                                    "description": "RGB and multispectral orthomosaics of six Alentejo vineyards, processed with webODM for stress and productivity analysis.",
                                    "parameters": {
                                            "location": "Alentejo, Portugal",
                                            "coordinates (wgs84)": "",
                                            "sensor_type": ["RGB", "Multispectral"],
                                            "contents": "RGB orthomosaics, multispectral orthomosaics, 3D pointclouds, DEM, drone flight logs",
                                            "labels": "No"
                                    }     
                                  },
                                  {   
                                    "name": "Input and output datasets for training the AI-based vine segmentation model (YOLOv9)",
                                    "author": "iLaria Marengo, Nuno Faria",
                                    "date": "2024-01-06",
                                    "link": "https://zenodo.org/records/14605849",
                                    "description": "It contains RGB orthomosaics and 5x5 m vector cells as inputs, with YOLOv9 segmentation outputs including vine polygons and vegetation index rasters.",
                                        "parameters": {
                                                "location": "Alentejo, Portugal",
                                                "coordinates (wgs84)": "",
                                                "sensor_type": ["RGB"],
                                                "contents": "RGB orthomosaics, segmented vines, shapefiles, vegetetation indices (NDVI, GNDVI, NDRE)",
                                                "labels": "No"
                                        }   
                                     }
                                    ],
     "OCT - Veles Sense": [
                                    {   
                                        "name": "DiVine project - Veles Sense",
                                        "author": "Veles Sense",
                                        "date": "2025-10-27",
                                        "link": "https://zenodo.org/records/17454029",
                                        "description": "Vineyards maps to distinguish disease from nutritional deficiencies. Includes orthomosaics, ground truth, sampling, vine extraction and vegetation indices.",
                                        "parameters": {
                                                    "location": "Belgrado, Serbia",
                                                    "coordinates (wgs84)": "",
                                                    "sensor_type": ["RGB", "Multispectral"],
                                                    "contents": "Orthomosaics, ground truth, sampling data, vine extraction results, vegetation indices",
                                                    "labels": "Yes"
                                        }   
                                    }
                                  ],
     "OCT - Institute for Security and Strategic Research": [
                                    {   
                                        "name": "IDSSF: Illegal logging usecase - DJI Zenmuse L2 lidar dataset",
                                        "author": "Institute for Security and Strategic Research",
                                        "date": "2025-10-13",
                                        "link": "https://zenodo.org/records/17339551",
                                        "description": "This dataset contains two classified point clouds (ground and non-ground) of a forest affected by illegal logging, one taken before and one after the logging.",
                                        "parameters": {
                                                    "location": "Ljubljana, Slovenia",
                                                    "coordinates (wgs84)": "",
                                                    "sensor_type": ["LiDAR"],
                                                    "contents": "Pointclouds",
                                                    "labels": "Yes"
                                        }   
                                    }
                                 ],

     "OCT - gprSense": [
                                    {   
                                        "name": "Example Soil Moisture Maps",
                                        "author": "gprSense",
                                        "date": "2025-10-29",
                                        "link": "https://zenodo.org/records/17476345",
                                        "description": "The dataset features gprSense drone-borne GPR maps demonstrating root-zone soil moisture mapping capability.",
                                        "parameters": {
                                                    "location": "Belgium",
                                                    "coordinates (wgs84)": "",
                                                    "sensor_type": ["GPR"],
                                                    "contents": "Soil moisture maps, radar data, QGIS project files",
                                                    "labels": "Yes"
                                        }   
                                    }
                                 ],
      "OCT - European Center for Training and Research in Earthquake Engineering": [
                                    {   
                                        "name": "U-AInSPECT Project: Bridge Use Case dataset",
                                        "author": "Institute for Security and Strategic Research",
                                        "date": "2025-10-30",
                                        "link": "https://zenodo.org/records/17477130",
                                        "description": "The dataset provides UAS and AI-based bridge inspection data, including imagery, damage detection, and 3D reconstruction for structural monitoring research.",
                                        "parameters": {
                                                    "location": "Pavia, Italy",
                                                    "coordinates (wgs84)": "",
                                                    "sensor_type": ["RGB"],
                                                    "contents": "Videos, images, damage detection outputs, 3D reconstruction, bridge data",
                                                    "labels": "Yes"
                                        }   
                                    }
                                 ],
    "OCT- HYGRI": [
                                    {
                                        "name": "HYGRI - UAV Hyperspectral and Thermal Data for Agriculture Monitoring - ICAERUS",
                                        "author": "AVT Airborne Sensing Italia Srl",
                                        "date": "2025-12-09",
                                        "link": "https://zenodo.org/records/17863526",
                                        "description": "Multi-modal airborne data acquired over a vineyard field containing hyperspectral imagery, thermal imagery, Digital Elevation Models (DTM/DSM), and RGB orthophotos collected across summer months for vegetation and environmental monitoring research.",
                                        "parameters": {
                                            "location": "Vineyard field, Terlano, Italy",
                                            "coordinates (wgs84)": "46°32'18.5\"N 11°15'09.1\"E",
                                            "sensor_type": ["Hyperspectral", "Thermal", "RGB"],
                                            "contents": "Hyperspectral mosaics, vegetation indices (NDVI, reNDVI), thermal imagery (32-bit), RGB orthophotos, DTM, DSM, flight plans (KML), area of interest boundaries",
                                            "labels": "No"
                                        }
                                    }
                                ],
    "OCT-FAMMS": [{ "name": "FAMMS Project Dataset",
                    "author": "Ordine & Sicurezza Srl",
                    "date": "2026-02-12", 
                    "link": "https://zenodo.org/records/18618424",
                    "description": "The dataset contains UAV images captured the forest area selected for the prototype, plus images of the equipment used for the flights. From a DJI Mavic 2 Pro",
                    "parameters": {
                        "location": "Malito, Italy",
                        "coordinates (wgs84)": "39.15, 16.25",
                        "sensor_type": ["RGB"],
                        "contents": "Geotagged UAV images, before and after harvest",
                        "labels": "No"
                        }
                    }
    ],
    
    "UARSF": [        
                {
                    "name": "Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles",
                    "author": "Chenglong Zhang, João Valente, Wensheng Wang, Pieter van Dalfsen, Peter Frans de Jong, Bert Rijk, Lammert Kooistra",
                    "date": "2023-08-01", 
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340923004754",
                    "description": "The dataset contains UAV images captured during the full blooming period in an apple orchard over three consecutive years (2018-2020). It is linked to research on quantifying flower intensity using UAV RGB imagery. This data can be utilized in the fields of remote sensing, machine vision, deep learning, and precision horticulture for flowering intensity estimation and prediction.",
                    "parameters": {
                        "location": "Randwijk, Overbetuwe, The Netherlands",
                        "coordinates (wgs84)": "51.938, 5.7068",
                        "sensor_type": ["RGB"],
                        "contents": "Geotagged UAV images, flower cluster counts, floridity ground truth",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "Dataset on UAV RGB videos acquired over a vineyard including bunch labels for object detection and tracking",
                    "author": "Mar Ariza-Sentís, Sergio Vélez, João Valente",
                    "date": "2023-02-01", 
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340922010514",
                    "description": "This dataset contains 40 RGB videos of a vineyard in northern Spain, acquired via UAV for object detection and tracking of grape bunches. The dataset is designed to facilitate grape bunch counting and assist winegrowers in assessing vineyard health. It includes mask labels of visible grape bunches, recorded throughout four UAV flights with the camera tilted at 60 degrees.",
                    "parameters": {
                        "location": "Bodegas Terras Gauda, Spain",
                        "coordinates (wgs84)": "41.937042, -8.79074",
                        "sensor_type": ["RGB"],
                        "contents": "RGB videos, grape annotations",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "Dataset on unmanned aerial vehicle multispectral images acquired over a vineyard affected by Botrytis cinerea in northern Spain",
                    "author": "Sergio Vélez, Mar Ariza-Sentís, João Valente",
                    "date": "2023-02-01", 
                    "link": "https://doi.org/10.1016/j.dib.2022.108876",
                    "description": "This dataset contains 16504 multispectral images acquired via UAV over a 1.06 ha vineyard in northern Spain affected by Botrytis cinerea. The images were taken during four flights at 30 m height with varying camera tilt angles (0°, 30°, 45°, and 0° in the afternoon to capture plant shadows). The dataset supports research in disease detection, 3D reconstruction, and orthomosaic generation in vineyards.",
                    "parameters": {
                        "location": "Bodegas Terras Gauda, Spain",
                        "coordinates (wgs84)": "41.937042, -8.79074",
                        "sensor_type": ["Multispectral"],
                        "contents": "Multispectral images, orthomosaics, vegetation indices",
                        "labels": "No"
                    }
                },
                {
                    "name": "Dataset on UAV High-resolution Images from Grassland with Broad-leaved Dock (Rumex Obtusifolius)",
                    "author": "João Valente, Lammert Kooistra",
                    "date": "2021-07-22", 
                    "link": "https://zenodo.org/records/5119205#.Yx7aknZByHt",
                    "description": "The dataset contains orthophotos constructed from UAV images of a grassland field in Kleve, Germany, where several Rumex obtusifolius plants were detected. The images were captured at 10, 15, and 30 meters height, and labels/annotations of the plants are provided.",
                    "parameters": {
                        "location": "Kleve, Germany",
                        "coordinates (wgs84)": "51.7889, 6.1387",
                        "sensor_type": ["RGB"],
                        "contents": "Orthophotos, labels of Rumex obtusifolius plants",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "Diurnal UAV-based sun-induced chlorophyll fluorescence over potato and sugar beet fields",
                    "author": "Na Wang, Juha Suomalainen, Harm Bartholomeus, Lammert Kooistra, Dainius Masiliunas, Jan Clevers",
                    "date": "2022-08-23", 
                    "link": "https://data.4tu.nl/articles/_/20348382",
                    "description": "This dataset contains UAV-based sun-induced chlorophyll fluorescence data acquired over potato and sugar beet fields on August 2, 2018, during clear sky conditions. Four flights were conducted following a diurnal cycle at 9:45, 11:38, 14:15, and 16:28. Multispectral measurements were collected around 11:00 using a Parrot Sequoia+ sensor.",
                    "parameters": {
                        "location": "Wageningen University and Research farm, the Netherlands",
                        "coordinates (wgs84)": "51.987, 5.652",
                        "sensor_type": ["Multispectral", "Hyperspectral"],
                        "contents": "Sun-induced chlorophyll fluorescence data, multispectral data, shapefile.",
                        "labels": "No"
                    }
                },
                {
                    "name": "Haus Riswick grassland experiment with N fertilization and plant growth monitoring based on Unmanned Aerial Vehicle (UAV) hyperspectral imagery - campaigns of 2014 and 2017",
                    "author": "Lammert Kooistra, Marston H D Franceschini, Rolf Becker, Florian Wichern, J.M. (Juha) Suomalainen, Harm Bartholomeus, Alessandra Capolupo",
                    "date": "2022-02-25", 
                    "link": "https://doi.org/10.4121/19188872.v1",
                    "description": "This dataset contains UAV hyperspectral imagery and field measurements of dry aboveground biomass, nitrogen content (N%), and nitrogen uptake from experimental grassland plots near Kleve, Germany. Data were collected during five campaigns on two dates in 2014 and three dates in 2017. The UAV flights used the WageningenUR Hyperspectral Mapping System (HYMSY) with a pushbroom imaging sensor and a photogrammetric camera.",
                    "parameters": {
                        "location": "Kleve, Germany",
                        "coordinates (wgs84)": "51.782036, 6.163150",
                        "sensor_type": ["Hyperspectral"],
                        "contents": "Hyperspectral images, dry biomass, nitrogen measures, ground truth plant growth data.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "UAV-based Multispectral & Thermal dataset for exploring the diurnal variability, radiometric & geometric accuracy for precision agriculture",
                    "author": "Christina Kallimani, Ramin Heidarian, Frits K. van Evert, Bert Rijk, Lammert Kooistra",
                    "date": "2020-04-22", 
                    "link": "https://doi.org/10.18174/odjar.v6i0.16317",
                    "description": "This dataset explores diurnal variability, radiometric and geometric accuracy in UAV-based multispectral and thermal imagery for precision agriculture. The data were collected in a one-day field campaign on 21 June 2017 over wheat, barley, and potato fields at Wageningen University and Research (WUR) farm. The dataset includes imagery from various sensors and validation measurements from radiometric calibration plates and plant sample locations.",
                    "parameters": {
                        "location": "Wageningen University and Research farm, the Netherlands",
                        "coordinates (wgs84)": "51.9706, 5.6671",
                        "sensor_type": ["Multispectral", "Thermal", "RGB"],
                        "contents": "Multispectral images, thermal images, orthomosaics, radiometric calibration, plant sample validation.",
                        "labels": "No"
                    }
    
                },
                {
                    "name": "Speulderbos Terrestrial (TLS) and Unmanned Aerial Vehicle Laser Scanning (UAV-LS) 2017",
                    "author": "Benjamin Brede, A.I. (Alvaro) Lau Sarmiento, Pasi Raumonen, Harm Bartholomeus, Martin Herold, Lammert Kooistra",
                    "date": "2020-10-07", 
                    "link": "https://doi.org/10.4121/13061306.v1",
                    "description": "This dataset contains TLS and UAV-LS point clouds from the 2017 Speulderbos Laser Scanning campaign. It covers approximately 2 hectares of forest, including 1 hectare of mature beech and oak trees and 1 hectare of mixed species. The data was acquired during early May 2017, when around half of the deciduous trees had no leaves. It provides valuable information for studying above-ground plant biomass and forest inventory.",
                    "parameters": {
                        "location": "Speulderbos, the Netherlands",  
                        "coordinates (wgs84)": "52.2580, 5.6910",
                        "sensor_type": ["TLS", "LiDAR"],
                        "contents": "TLS pointclouds, forest inventory data, LAZ, shapefiles",
                        "labels": "No"
                    }
                }
        ],
    
    "WU":[
            {
                "name": "RGB-to-NDVI-conversion dataset",
                "author": "Jurrian Doornbos",
                "date": "2025-03-19", 
                "link": "https://zenodo.org/records/15051398",
                "description": "Dataset for the article: Evaluating Generalization of Methods for Artificially Generating NDVI from UAV RGB Imagery in Vineyards",
                "parameters": {
                        "location": "Wageningen, the Netherlands",  
                        "coordinates (wgs84)": "52.2580, 5.6910",
                        "sensor_type": ["RGB", "Multispectral"],
                        "contents": "RGB chip images, NDVI chip images, orthomosaics, CHM, DEM, DSM.",
                        "labels": "No"
                    }
            },
            {
                "name": "msuav500k: Foundational Dataset for Multispectral and RGB UAV Imagery",
                "author": "Jurrian Doornbos",
                "date": "2025-08-14", 
                "link": "https://zenodo.org/records/16743975",
                "description": "The msuav500k dataset is a comprehensive collection of 598,300 curated UAV images designed for computer vision and remote sensing applications. This foundational dataset addresses critical interoperability challenges in UAV-based multispectral imaging by providing standardized, radiometrically calibrated imagery from multiple sensor platforms.",
                "parameters": {
                        "location": "Global",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB", "Multispectral"],
                        "contents": "RGB images, Multispectral images",
                        "labels": "No"
                    }
            }
        ],
    
    "Online": [
                {
                    "name": "UFRGS_UAV",
                    "author": "Ricardo S. Westhauser",
                    "date": "2019-02-05",
                    "link": "https://zenodo.org/records/4076075",
                    "description": "This dataset contains five sets (each set from a different spectra) of images taken by an UAV equipped with a downward looking multispectral camera (Parrot Sequoia), in one flight. The region in which the UAV flyed was Porto Alegre (more specifically, Federal University of Rio Grande do Sul, Campus do Vale), a city from Rio Grande do Sul, Brazil. In addition to the sets of images, there are also a folder with twelve satellite images from Porto Alegre, a README file, a .kml and a .DAT file.",
                    "parameters": {
                        "location": "Porto Alegre, Rio Grande do Sul, Brazil",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral"],
                        "contents": "UAV multispectral images, satellite images, .kml files, .DAT files",
                        "labels": "No"
                    }
                },
                {
                    "name": "Gummern - Mining Waste Deposits Multispectral UAV Imagery",
                    "author": "Enoc Sanz Albeno",
                    "date": "2023-06-09", 
                    "link": "https://zenodo.org/records/13622459",
                    "description": "Mining Waste Deposits Multispectral UAV Imagery from DJI Mavic 3M",
                    "parameters": {
                        "location": "Gummern, Austria",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral"],
                        "contents": "Multispectral images",
                        "labels": "No"
                    }
                },
                {
                    "name": "Hullerbusch beech forest, Germany, captured by UAV-based multispectral imaging",
                    "author": "Robert Jackisch",
                    "date": "2022-08-09", 
                    "link": "https://zenodo.org/records/7057321",
                    "description": "This data set contains data products of a multispectral UAV survey over the Hullerbusch beach forest, located in southern Mecklenburg–Western Pomerania, Germany. As survey drone, the DJI M300 with RTK antenna was employed and the multispectral image data was captured with a Micasense Altum multispectral camera. Data were processed in Agisoft Metashape 1.7, with the orthomosaic converted to reflectance.",
                    "parameters": {
                        "location": "Hullerbusch, Mecklenburg, Germany",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral"],
                        "contents": "Orthomosaics, DSM, RGB pointcloud, shapefile, Agisoft processing report",
                        "labels": "No"
                    }
                },
                {
                    "name": "Cherry Tree Disease Detection Dataset",
                    "author": "Christos Chaschatzis, Ilias Siniosoglou, Anna Triantafyllou, Chrysoula Karaiskou, Athanasios Liatifis, Panagiotis Radoglou-Grammatikis, Dimitrios Pliatsios, Vasiliki Kelli, Thomas Lagkas, Vasileios Argyriou, Panagiotis Sarigiannidis",
                    "date": "2022-07-01", 
                    "link": "https://zenodo.org/records/7144071",
                    "description": "This cherry tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of cherry trees, including stress analysis and prediction. An orchard of cherry trees is considered in the area of Western Macedonia, where 577 cherry trees were recorded in a full crop season starting from Jul. 2021 to Jul. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos. Two agronomist experts annotated the dataset by identifying a stress, which in this case is a common disease in cherry trees known as Armillaria [1][2].",
                    "parameters": {
                        "location": "Western Macedonia, Macedonia",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "RGB images, multispectral images, disease annotations.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "FlexiGroBots - Blueberry orchard UAV dataset",
                    "author": "Ivošević Bojana, Grbović Željana, Stefanović Dimitrije, Filipović Vladan, Antić Aleksandar, Pajević Nina, Marko Oskar, Panić Marko",
                    "date": "2021-07-02", 
                    "link": "https://zenodo.org/records/5712792",
                    "description": "Dataset consists of UAV drone images: 6 channels: reflectance blue, reflectance green, reflectance red, reflectance red edge, reflectance NIR, RGB. In order to align different channels, registration was applied and because of uneven illumination during the acquisition process, illumination correction was performed. Thus, the results of each preprocessing step are located in separate folders, i.e. raw data are in 100FPLAN and 101FPLAN, results of registration are in 100FPLAN_registrated and 101FPLAN_registrated, while images with corrected illumination are in 100FPLAN_registrated_corrected and 101FPLAN_registrated_corrected. Orthomosaics created before and after preprocessing are located in UAV orthomosaics folder.",
                    "parameters": {
                        "location": "Babe, Serbia",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "RGB images, multispectral images (B, G, R, RE, NIR), orthomosaics",
                        "labels": "No"
                    }
                },
                {
                    "name": "mDRONES4rivers-project: UAV-imagery of the project area Nonnenwerth at the Rhine River, Germany",
                    "author": "Gilles Rock, Tina Mölter, Paul Deffert, Filip Dzunic, Laura Giese, Edvinas Rommel, Frederik Kathoefer",
                    "date": "2019-06-06", 
                    "link": "https://zenodo.org/records/5828865",
                    "description": "Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany",
                    "parameters": {
                        "location": "Nonnenwerth, Germany",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "Multispectral orthophotos, RGB orthophotos, DSM, technical report, metadata.",
                        "labels": "No"
                    }
                },
                {
                    "name": "mDRONES4rivers-project: UAV-imagery of the project area Reitwein at the River Oder, Germany",
                    "author": "Gilles Rock, Tina Mölter, Paul Deffert, Filip Dzunic, Laura Giese, Edvinas Rommel, Frederik Kathoefer",
                    "date": "2019-06-06", 
                    "link": "https://zenodo.org/records/5828899",
                    "description": "Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany",
                    "parameters": {
                        "location": "Reitwein, Germany",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "Multispectral orthophotos, RGB orthophotos, DSM, technical reports, metadata.",
                        "labels": "No"
                    }
                },
                {
                    "name": "mDRONES4rivers-project: UAV-imagery of the project area Kuehkopf Knoblochsaue at the Rhine River, Germany",
                    "author": "Gilles Rock, Tina Mölter, Paul Deffert, Filip Dzunic, Laura Giese, Edvinas Rommel, Frederik Kathoefer",
                    "date": "2019-06-06", 
                    "link": "https://zenodo.org/records/5828282",
                    "description": "Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany",
                    "parameters": {
                        "location": "Kuekopf Knoblochsaue, Germany",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "Multispectral orthophotos, RGB orthophotos, DSM, technical reports, metadata",
                        "labels": "No"
                    }
                },
                {
                "name": "mDRONES4rivers-project: UAV-imagery of the project area Laubenheim at the Rhine River, Germany",
                "author": "Gilles Rock, Tina Mölter, Paul Deffert, Filip Dzunic, Laura Giese, Edvinas Rommel, Frederik Kathoefer",
                "date": "2019-02-18", 
                "link": "https://zenodo.org/records/5828543",
                "description": "Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany",
                "parameters": {
                    "location": "Laubenheim, Germany",  
                    "coordinates (wgs84)": "",
                    "sensor_type": ["Multispectral", "RGB"],
                    "contents": "Multispectral orthophotos, RGB orthophotos, DSM, technical reports, metadata",
                    "labels": "No"
                    }
                },
                {
                    "name": "mDRONES4rivers-project: UAV-imagery of the project area Emmericher Ward at the Rhine River, German",
                    "author": "Gilles Rock, Tina Mölter, Paul Deffert, Filip Dzunic, Laura Giese, Edvinas Rommel, Frederik Kathoefer",
                    "date": "2019-07-23", 
                    "link": "https://zenodo.org/records/5827756",
                    "description": "Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in German",
                    "parameters": {
                        "location": "Emmericher Ward, Germany",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "Multispectral orthophotos, RGB orthophotos, DSM, technical reports, metadata",
                        "labels": "No"
                    }
                },
                {
                    "name": "mDRONES4rivers-project: UAV-imagery of the project area Niederwerth at the Rhine River, Germany",
                    "author": "Gilles Rock, Tina Mölter, Paul Deffert, Filip Dzunic, Laura Giese, Edvinas Rommel, Frederik Kathoefer",
                    "date": "2019-03-08", 
                    "link": "https://zenodo.org/records/5822652",
                    "description": "Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany",
                    "parameters": {
                        "location": "Niederwerth, Germany",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "Multispectral orthophotos, RGB orthophotos, DSM, technical reports, metadata",
                        "labels": "No"
                    }
                },
                {
                    "name": "Multispectral imaging dataset of potato plants on the field, acquired using UAV",
                    "author": "Janez Lapajne, Andrej Vončina, Ana Vojnović",
                    "date": "2022-07-20", 
                    "link": "https://zenodo.org/records/10934163",
                    "description": "Multispectral images of potato field crops were captured using a SKYHERO SPYDER X4-850 GEO Edition Unmanned Aerial Vehicle (UAV) with a Micasense Rededge MX 5-band multispectral camera.",
                    "parameters": {
                        "location": "Central Slovenia, Slovenia",  
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral"],
                        "contents": "Multispectral orthophotos, shapefiles, ground physiological measurements, disease scoring, metadata",
                        "labels": "No"
                    }
                },
                {
                    "name": "Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, on October 22, 2020, Rio Grande do Sul, Brazil",
                    "author": "Fabio Marcelo Breunig, Eduardo Rieder, Renato Souza Santos",
                    "date": "2020-10-22",
                    "link": "https://zenodo.org/records/4559718",
                    "description": "The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also Drone) covering a forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil. The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm. The rainfall is well distributed throughout the year.",
                    "parameters": {
                        "location": "Frederico Westphalen, Rio Grande do Sul, Brazil",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "Multispectral images, RGB images, orthomosaics, DEM.",
                        "labels": "No"
                    }
                },
                {
                    "name": "A dataset of unmanned aerial vehicle multispectral images acquired over a field to identify nitrogen requirements",
                    "author": "C.L.I.S. Fonseka, Thilina Halloluwa, K.P. Hewagamage, Upul Rathnayake, R.M.U. S Bandara",
                    "date": "2023-08-14",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340924004487",
                    "description": "The technique of detecting and tracking an area's physical properties from a distance by measuring its reflected and emitted radiation is known as remote sensing. It gathered data accurately in near real-time. For this purpose, multispectral cameras mounted on UAVs that capture images with different bands can be used to generate vegetation indexes (NDVI, NDRE), which are useful in precision agriculture. In this study UAV image dataset contains 336 multispectral images from a 0.06 ha paddy field with three different phonological cycles of the crop (vegetative, reproductive, and ripening) in the north-western province of Sri Lanka. The selected sample rice variety is BG300. The images were taken over five days, starting from August 14 to October 5, 2023.",
                    "parameters": {
                        "location": "Batalagoda, Ibbagamuwa, Kurunegala, Sri Lanka",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "Multispectral images (G, R, RE, NIR), RGB images, SPAD meter readings, shapefiles.",
                        "labels": "No"
                    }
                },
                {
                    "name": "High resolution image dataset by RGB and multispectral cameras on an unmanned aerial vehicle over a secondary tropical dry forest",
                    "author": "Masuly Vega-Puga, Jaime Garatuza-Payán, Miguel A. Rivera-Diaz, Onésimo Galaz, Juan C. Alvarez-Yépiz, Enrico A. Yepéz",
                    "date": "2022-07-01",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340923009307",
                    "description": "Unmanned Aerial Vehicles (UAV) are advantageous to assess vegetation and terrain changes at a high spatial resolution, advancing our understanding of shifts in ecosystem states and processes. The extension of land that we sought to observe belongs to the footprint of an Eddy Covariance System (around 7.5 ha), installed in a secondary ecological succession of the tropical dry forest within the Sierra de Álamos-Rio Cuchujaqui natural protected area, Sonora, Mexico. Aerial images were obtained with a system of multispectral and RGB cameras mounted on a UAV, which executed automated missions in two different seasons: 1) dry season (June 2023) and 2) first rain season (July 2023), totalling 1116 images.",
                    "parameters": {
                        "location": "El Area Natural Protegida Sierra de Alamos-Rio Cuchujaqui, Sonora, Mexico",
                        "coordinates (wgs84)": "27.00598005 N, 108.77913821 W",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "RGB images, multispectral images (B, G, R, RE, NIR), orthomosaics, metadata",
                        "labels": "No"
                    }
                },
                {
                    "name": "Orthophotos and DSMs/DTM derived from RPAS flights over the nature reserve Landschap De Liereman in Flanders, Belgium",
                    "author": "Jeroen Vanden Borre, Pauly Klaas, Stijn Van Hoey, Jeroen De Reu, Peter Desmet",
                    "date": "2017-07-17",
                    "link": "https://zenodo.org/records/3063335",
                    "description": "Landschap De Liereman is a nature reserve situated in Oud-Turnhout, in the province of Antwerp, Flanders, Belgium. The area is managed by the nature conservation NGO Natuurpunt and consists of a diverse landscape of wet and dry heathlands, Nardus grasslands on siliceous soils, forests and transition mires, as well as some remaining arable fields and high-intensity agricultural grasslands.",
                    "parameters": {
                        "location": "De Liereman, Belgium",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "RGB orthophotos, multispectral orthophotos, DSM, DTM, flight metadata",
                        "labels": "No"
                    }
                },
                {
                    "name": "UAS-SfM data from Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA",
                    "author": "Sean Reilly, Matthew L. Clark, Lika Loechler, Jack Spillane, Melina Kozanitas, Paris Krause, David Ackerly, Lisa Patrick Bentley, Imma Oliveras Menor",
                    "date": "2019-09-01",
                    "link": "https://zenodo.org/records/12809155",
                    "description": "Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA",
                    "parameters": {
                        "location": "California, United States of America",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral"],
                        "contents": "Multispectral UAS-SfM data",
                        "labels": "No"
                    }
                },
                {
                    "name": "Multispectral imagery of macroalgae in Illas Cíes (NW Spain)",
                    "author": "Andrea Martinez-Movilla, Marta Román-Geada, Juan Luis Rodríguez Somoza, Celia Olabarria, Joaquín Martínez-Sánchez",
                    "date": "2019-09-02",
                    "link": "https://zenodo.org/records/13880699",
                    "description": "This dataset focuses on an intertidal coastal environment in Illas Cíes, NW Spain. It includes multispectral orthoimages (5 bands), photoquadrat images, and manually segmented shapefiles. The dataset spans 4 months and contains 20 orthoimages (5 per month, one for each band: Blue, Green, Red, Red edge, and NIR). There are also 164 photoquadrat images and 8 shapefiles with a total of 949 polygons. The dataset classifies the intertidal area into 9 categories: Codium spp., Ulva spp., C. peregrina, B. bifurcata, E. selaginoides, S. polyschides, S. muticum, Inert and water.",
                    "parameters": {
                        "location": "Illas Cíes, Spain",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral"],
                        "contents": "Multispectral orthoimages (B, G, R, RE, NIR), photoquadrat images, shapefiles.",
                        "labels": "No"
                    }
                },
                {
                    "name": "Data for estimating spruce tree health using drone-based RGB and multispectral imagery",
                    "author": "Samuli Junttila, Roope Näsi, Niko Koivumäki, Mohammad Imangholiloo, Ninni Saarinen, Juha Raisio, Markus Holopainen, Hannu Hyyppä, Juha Hyyppä, Päivi Lyytikäinen-Saarenmaa, Mikko Vastaranta, Eija Honkavaara",
                    "date": "2019-09-08",
                    "link": "https://zenodo.org/records/13925862",
                    "description": "The dataset contains multispectral and RGB orthomosaics (.tif), and photogrammetric point clouds (.laz) of four study areas (about 25 ha each), where bark beetle-related decline of Norway spruce has been observed in Helsinki, Finland. The filenames refer to Area 1 (Männikkötie), Area 2 (Maunulanmaja), Area 3 (Hakuninmaa), and Area 4 (Paloheinä), described in detail in Junttila et al. 2022.",
                    "parameters": {
                        "location": "Helsinki, Finland",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral", "RGB"],
                        "contents": "Multispectral orthomosaics, RGB orthomosaics, pointclouds, geolocated tree crown polygons.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "Post-remediation evaluation of contaminated site using geophysical methods: Multispectral UAV data Olkusz (Poland) 20220629",
                    "author": "Marta Kondracka",
                    "date": "2022-06-09",
                    "link": "https://zenodo.org/records/7358770",
                    "description": "In order to analyze the vegetation condition, photos were taken in the infrared (NIR, 750 - 2500 nm) and infrared (Red Edge, 690-720 nm) range. The DJI Matrice 600 platform was used for the raid. The photos were taken from the ceiling of 150 m with the MicaSense Red Edge M camera with a focal length of 6 mm.",
                    "parameters": {
                        "location": "Olkusz, Poland",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["Multispectral"],
                        "contents": "Multispectral images (RE, NIR).",
                        "labels": "No"
                    }
                },
                {
                    "name": "Annotated image dataset with different stages of European pear rust for UAV-based automated symptom detection in orchards",
                    "author": "Maß, Virginia; Alirezazadeh, Pendar; Seidl-Schulz, Johannes; Leipnitz, Matthias; Fritzsche, Eric; Ibraheem, Rasheed Ali Adam; Geyer, Martin; Pflanz, Michael; Reim, Stefanie",
                    "date": "2024-08-03",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340925000034",
                    "description": "RGB drone imagery dataset for pear rust detection and analysis captured using DJI Phantom 4 Pro/Matrice 300 RTK at 0.7m altitude.",
                    "parameters": {
                        "location": "Dresden-Pillnitz, Germany",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 0.2-0.07m resolution, CHIP format.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "CoFly-WeedDB: A UAV image dataset for weed detection and species identificationn",
                    "author": "Marios Krestenitis, Emmanuel K. Raptis, Athanasios Ch. Kapoutsis, Konstantinos Ioannidis, Elias B. Kosmatopoulos, Stefanos Vrochidis, Ioannis Kompatsiaris",
                    "date": "2022-12-12",
                    "link": "https://www.sciencedirect.com/science/article/pii/S235234092200782X",
                    "description": "RGB drone imagery dataset for cotton weed detection and species identification captured using DJI Phantom 4 Pro at 1.2m altitude.",
                    "parameters": {
                        "location": "Larissa, Greece",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, CHIP format.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "An instance segmentation dataset of cabbages over the whole growing season for UAV imagery",
                    "author": "Yui Yokoyama, Tsutomu Matsui, Takashi S.T. Tanaka",
                    "date": "2024-08-08",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340924006668",
                    "description": "RGB drone imagery dataset for cabbage detection during growing season captured using DJI Matrice 300 RTK at 0.7m altitude.",
                    "parameters": {
                        "location": "Gifu Prefecture, Japan",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, CHIP format.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "MH-SoyaHealthVision: An Indian UAV and Leaf Image Dataset for Integrated Crop Health Assessment",
                    "author": "Sayali Shinde, Dr.Vahida Attar",
                    "date": "2024-08-12",
                    "link": "https://data.mendeley.com/datasets/hkbgh5s3b7/1",
                    "description": "Currently, the farming sector is transitioning towards Agriculture 5.0, also known as digital farming. This approach utilizes data-driven technologies, such as artificial intelligence and computer vision, to transform the agriculture sector. These technologies enable the automation of several farming tasks. To develop accurate and robust machine learning/deep learning models high quality datasets are needed. With this aim, we have created a comprehensive dataset of soybean crop images affected by diseases and pest attacks from original fields of Maharashtra region located in India. Data acquisition was conducted across two seasons through aerial as well as ground-based approaches. The dataset is enriched with 4 types of diseases and 1 pest attack. The proposed dataset will serve as a valuable resource for training and testing machine learning and deep learning models ,enabling accurate detection and classification of diseases and pests attack damage.",
                    "parameters": {
                        "location": "Sangli, India",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 4m altitude, RAW format.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "OliveTreeCrownsDb, Detection and Segmentation in Agricultural Computer Vision",
                    "author": "",
                    "date": "2025-06-06",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340925002471",
                    "description": "OliveTreeCrownsDb, a comprehensive dataset of high-resolution images captured by a DJI Phantom 4 RTK drone. The dataset includes 46 images covering an entire olive farm, focusing on the detection and analysis of olive tree crowns and supporting segmentation tasks. Each image is accompanied by detailed metadata, such as focal distance, capture altitude, GPS coordinates, and other essential parameters for accurate tree mapping and localization. OliveTreeCrownsDb is publicly accessible, promoting research in precision agriculture, including tree crown detection, segmentation, geometric shape analysis, automation, yield estimation, and computer vision applications.",
                    "parameters": {
                        "location": "Meknas, Morocco",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 2m altitude, RAW format.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images",
                    "author": "Haohui Yan, Junfei Zhang",
                    "date": "2023-12-12",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340923007710",
                    "description": "The UAV-PDD2023 dataset, captured by unmanned aerial vehicles (UAVs), provides a benchmark for road damage detection. It is highly useful for municipal authorities and road agencies to conduct low-cost road condition monitoring.",
                    "parameters": {
                        "location": "Tianjin, China",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 2m altitude, CHIP format.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "Unmanned aerial vehicle (UAV) images of road vehicles dataset",
                    "author": "Nama Ezzaalddin Mustafa, Fattah Alizadeh",
                    "date": "2024-06-06",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340924002336",
                    "description": "This annotated dataset contains five different types of vehicles: cars, taxis, trucks, buses, and motorcycles taken from Unmanned Aerial Vehicles (UAVs), which we commonly know as drones. Mavic Air 2 Drone was used to take all the pictures in the Iraqi cities of Sulaimaniyah and Erbil.",
                    "parameters": {
                        "location": "Nama Ezzaalddin Mustafa, Fattah Alizadeh",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 0.3m altitude, RAW format.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "Avo-AirDB: An avocado UAV Database for agricultural image segmentation and classification",
                    "author": "Khalid EL Amraoui, Mouataz Lghoul, Ayoub Ezzaki, Lhoussaine Masmoudi, Majid Hadri, Hicham Elbelrhiti, Aziz Abdou Simo",
                    "date": "2022-12-12",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340922009428",
                    "description": "Avo-AirDB represents a database of an avocado field located in Morocco. it contains 986 high-resolution RGB aerial images, as well as 93 annotated images in two different ways. the data is dedicated to researchers in the digital agricultural domain. it can be used to develop machine learning algorithms.",
                    "parameters": {
                        "location": "Allal Tazi, Morocco",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 8m altitude, CHIP format, 2.7m resolution.",
                        "labels": "Yes"
                    }
                },
                {
                    "name": "UAV-Images dataset over a small intertidal coastal strip within El Soldado, estuary in Guaymas, Sonora, México.",
                    "author": "Miguel Sebastián Encinas Lara, Luis Arturo Mendez Barroso",
                    "date": "2020-06-06",
                    "link": "https://www.sciencedirect.com/science/article/pii/S235234092030319X",
                    "description": "This dataset contains 314 aerial photographs taken in March 02, 2019 at 9 a.m. (UTC - 07:00) over an experimental site instrumented with an eddy covariance tower and meteorological sensors at El Soldado estuary located at the South-eastern margin of the Gulf of California",
                    "parameters": {
                        "location": "Sonora, México",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 1.4m altitude, RAW format.",
                        "labels": "No"
                    }
                },
                {
                    "name": "Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion",
                    "author": "Lina M. García-Moreno, Jean P. Díaz-Paz, Humberto Loaiza-Correa, Andrés D. Restrepo-Girón",
                    "date": "2020-04-04",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340920302201",
                    "description": "This dataset presents thermal and visible aerial images of a flat scene with their respective geospatial data, a visible georeferenced orthoimage and the homography matrices between the images and the orthoimage, which are useful for other researchers to assess and develop new techniques for improvement, registration and fusion of thermal and visible images.",
                    "parameters": {
                        "location": "Valle del Cauca, Colombia",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 0.15m altitude, ORTHO format.",
                        "labels": "No"
                    }
                },
                {
                    "name": "Mine 4.0-mineCareerDB: A high-resolution image dataset for mining career segmentation and object detection",
                    "author": "Nasreddine Haqiq, Mounia Zaim, Mohamed Sbihi, Khalid El Amraoui, Mustapha El Alaoui, Lhoussaine Masmoudi, Hamza Echarrafi",
                    "date": "2024-12-12",
                    "link": "https://www.sciencedirect.com/science/article/pii/S2352340924009387",
                    "description": "Mine 4.0-MineCareerDB, a publicly available dataset of high-resolution image captured by a DJI Phantom 4 RTK drone specifically designed for analyzing mining careers. The dataset comprises a collection of 373 images depicting various mining operations and activities. Each image is georeferenced and offers a detailed view of mining activities, including the use of various equipment, infrastructure, and overall mining environment.",
                    "parameters": {
                        "location": "Benslimane Career site, Morocco",
                        "coordinates (wgs84)": "",
                        "sensor_type": ["RGB"],
                        "contents": "RGB images with 3-channel data, 4m altitude, CHIP format.",
                        "labels": "Yes"
                    }
                }
    ]
}