Satellite-Based Detection of Looted Archaeological Sites Using Machine Learning

2026-02-23

AI Summary

This study addresses the challenge of monitoring looting at archaeological sites in remote regions, a critical threat to cultural heritage preservation. The authors develop a scalable detection pipeline leveraging monthly PlanetScope mosaics and multi-temporal data from 1,943 sites in Afghanistan, augmented with spatial masks to isolate site-specific signals. They systematically evaluate the performance of end-to-end convolutional neural networks (CNNs), random forests, SatCLIP-V geovisual embeddings, and handcrafted spectral/textural features for looting identification. The work presents the first validation of ImageNet pretraining and spatial masking efficacy under domain shift in remote sensing contexts. The best-performing model achieves an F1 score of 0.926, substantially outperforming conventional approaches (F1 = 0.710), demonstrating that looting signatures are highly localized and that deep learning models possess superior discriminative capacity for this task.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Looting at archaeological sites poses a severe risk to cultural heritage, yet monitoring thousands of remote locations remains operationally difficult. We present a scalable and satellite-based pipeline to detect looted archaeological sites, using PlanetScope monthly mosaics (4.7m/pixel) and a curated dataset of 1,943 archaeological sites in Afghanistan (898 looted, 1,045 preserved) with multi-year imagery (2016--2023) and site-footprint masks. We compare (i) end-to-end CNN classifiers trained on raw RGB patches and (ii) traditional machine learning (ML) trained on handcrafted spectral/texture features and embeddings from recent remote-sensing foundation models. Results indicate that ImageNet-pretrained CNNs combined with spatial masking reach an F1 score of 0.926, clearly surpassing the strongest traditional ML setup, which attains an F1 score of 0.710 using SatCLIP-V+RF+Mean, i.e., location and vision embeddings fed into a Random Forest with mean-based temporal aggregation. Ablation studies demonstrate that ImageNet pretraining (even in the presence of domain shift) and spatial masking enhance performance. In contrast, geospatial foundation model embeddings perform competitively with handcrafted features, suggesting that looting signatures are extremely localized. The repository is available at https://github.com/microsoft/looted_site_detection.

Problem

• looting detection
• archaeological sites
• satellite monitoring
• cultural heritage protection
• remote sensing

Innovation

• satellite-based looting detection
• foundation models
• spatial masking
• archaeological site monitoring
• machine learning for cultural heritage