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A self-contained ArcPy toolbox for processing high-resolution remote sensing imagery in archaeology, with workflows for spectral enhancement and Masked Autoencoder–based crop mark detection.

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ArcPy Toolbox

This toolbox performs a series of remote sensing–derived products for archaeological analysis, with a particular focus on crop mark detection. It also includes an option to apply a Masked Autoencoder to identify zones with a higher probability of containing crop marks.

The toolbox is distributed as a self-contained package and does not require Conda, Miniforge, or manual environment setup by the end user.

The available operations are executed in the following recommended order:

  1. atmcorr – atmospheric correction
  2. pansharpening – spatial–spectral fusion
  3. pca – principal component analysis
  4. spectral_indices – spectral index computation
  5. highpass – spatial filtering
  6. mae – Masked Autoencoder–based analysis

Each operation provides a graphical user interface for configuring the parameters required for execution.

Available sensors

Atmospheric correction is currently supported for the following sensors:

  • GEOSAT
  • WorldView-3 (WV3)
  • WorldView LEGION-06

The pca, pansharpening and spectral_indices operations are available only for WV3 and LEGION sensors.

The highpass and mae operations are available for any image provided in GeoTIFF format.

Data

This repository includes a reduced WorldView-3 image provided for testing and demonstration purposes. The dataset covers the Zar Tepe archaeological site (Uzbekistan).

Installation

No installation is required beyond downloading and extracting the toolbox.

  1. Download the toolbox release.
  2. Extract the archive to a local directory (e.g. C:\Toolbox).
  3. Ensure the directory structure is preserved.

The toolbox includes all required Python environments and external dependencies.

Directory structure

After extraction, the toolbox directory must have the following structure:

Toolbox
|
|- app        (Python scripts used to perform the operations)
|- env        (packed Python environment for preprocessing)
|  |
|  |- OTB-9.1.1-Win64  (Orfeo ToolBox binaries)
|- env_mae    (packed Python environment for MAE analysis)
|- atmcorr.bat
|- ...
|- README.md

External dependencies

The toolbox relies on Orfeo ToolBox (OTB) version 9.1.1, which is already included in the release. No additional downloads are required.

Running the toolbox

To launch the toolbox operations:

  1. Double-click on the desired operation (e.g. atmcorr.bat), or
  2. Run it from a Windows command prompt.

Each batch file automatically activates the appropriate environment and routes the operation to the correct backend.

Supported systems

  • Operating system: Windows 11
  • No administrator privileges required

Environment construction (for developers)

The release includes the complete application folder, together with the required environments. To replicate the same structure, follow the steps below in a Miniforge or Miniconda console.

Install the environments using the provided .yml files:

mamba env create -f requirements_otb.yml
mamba env create -f requirements_mae.yml

Pack the environments and place the resulting archives inside the toolbox directory. They must be unpacked later:

conda pack -n arcpy_otb -o Toolbox/env.zip
conda pack -n mae -o Toolbox/env_mae.zip

Finally, include Orfeo ToolBox inside the env folder. Download OTB version 9.1.1 and place it within the toolbox directory as follows:

Toolbox
|
|- env
|  |
|  |- ...
|  |- OTB-9.1.1-Win64
|  |- ...
|_ ...

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A self-contained ArcPy toolbox for processing high-resolution remote sensing imagery in archaeology, with workflows for spectral enhancement and Masked Autoencoder–based crop mark detection.

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