Archaeological Research and Protection
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ArqPy, a Python toolbox designed to automate and standardise image preprocessing, enhancement and initial interpretation for archaeological prospection, was applied to the preliminary inspection of crop marks at the Zar Tepe archaeological site in southern Uzbekistan before the 2026 field campaign.
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Remote sensing is widely used in archaeology, but the lack of standardised and readily deployable preprocessing workflows limits reproducibility and cross-study comparability, particularly for very high-resolution multispectral imagery. This study presents ArqPy, a Python toolbox designed to automate and standardise image preprocessing, enhancement and initial interpretation for archaeological prospection. The toolbox includes atmospheric correction, conventional pansharpening, spectral indices, principal component analysis (PCA), and spatial filtering. Its object-oriented architecture currently supports WorldView-3 (WV3) and WorldView Legion (LEGION) imagery and facilitates the future integration of additional sensors. ArqPy also incorporates complementary AI-based tools: Masked Autoencoder (MAE) feature analysis for exploring and ranking derived products, Z-PNN for deep-learning-based pansharpening, and SAM 3 for text-guided segmentation of candidate crop marks. The toolbox was applied to the preliminary inspection of crop marks at the Zar Tepe archaeological site in southern Uzbekistan before the 2026 field campaign. This case study illustrates the proposed workflow and demonstrates how ArqPy provides a reproducible and readily deployable environment through which archaeologists can access advanced image-processing methods. AI-based tools can support preliminary reconnaissance, but they cannot replace expert interpretation without further training and validation using site-specific archaeological data.
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@article{Iranzo2026ArqPy,
title = {ArqPy: A Python Toolbox for Remote Sensing Image Preprocessing and AI-Assisted Interpretation of Derived Products for Archaeological Prospection},
author = {Cristian Iranzo and Paula Uribe Agudo and Jorge Angás Pajas and Fernando Pérez},
journal = {Sensors},
year = {2026},
doi = {10.3390/s26175478},
url = {https://doi.org/10.3390/s26175478}
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