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Detection of Artisanal Small-Scale Mining(ASM) Using Segmentation-Based Deep Learning and Sentinel 2 Imagery In Madre de Dios, Peru
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Resumo(s)
Artisanal and small-scale mining (ASM) is a major driver of environmental degradation in tropical regions, contributing to deforestation, land degradation, and water pollution. However, mapping ASM activities using satellite imagery remains challenging due to their small spatial extent, spectral variability, and similarity to other forms of land disturbance. This study aims to investigate how transferable ASM models can be from one mining site to another and focuses on two major ASM hotspots: Madre de Dios, Peru (source region), and the Kayapó Indigenous Territory, Brazil (target region), with analysis conducted using multi-temporal Sentinel-2 imagery acquired between January to July of 2024. The study presents a deep learning–based approach for detecting ASM sites using multi-temporal Sentinel-2 imagery and forest loss information derived from the Hansen Global Forest Change dataset. A U-Net semantic segmentation model with a ResNet50 encoder was employed to capture both fine-scale spatial details and high-level contextual features associated with mining activities. The model was trained using labeled data generated from areas of forest disturbance and evaluated using standard segmentation metrics, including Intersection over Union(IoU), Dice coefficient, precision, recall, and F1-score. Within the source region (Madrede Dios), the best-performing model achieved an F1-score of 0.933 and an IoU of 0.880,with overall accuracy ranging between 0.96 and 0.97 across feature configurations. To assess model generalization, both zero-shot transfer and fine-tuning experiments were conducted across geographically distinct regions characterized by differences in vegetation structure, soil properties, and mining morphology. Direct zero-shot transfer to Kayapó resulted in a reduced F1-score of 0.763 and an IoU of 0.648, reflecting substantial performance degradation due to domain shift. However, transfer learning through limited fine-tuning improved performance to an F1-score of 0.850 and an IoU of0.749, increasing spatial overlap with reference ASM areas from 51.66% to 80.06%. Spatial overlap analysis further demonstrated the model’s ability to capture mining-related land cover changes beyond conventional forest loss mapping, highlighting its potential as a complementary tool for environmental monitoring. Despite limitations associated with the use of global forest loss data as proxy ground truth, the study demonstrates that deep learning combined with freely available satellite imagery offers a scalable framework for ASM detection. The findings contribute to the development of transferable remote sensing models for monitoring environmentally destructive activities in data-scarce regions.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
Artisanal Small-Scale Mining Deep Learning Remote Sensing Transfer Learning