Mapping artisanal and small-scale mines at large scale from space with deep learning.

Artisanal and small-scale mines (asm) are on the rise. They represent a crucial source of wealth for numerous communities but are rarely monitored or regulated. The main reason being the unavailability of reliable information on the precise location of the asm which are mostly operated informally or illegally. We address this issue by developing a strategy to map the asm locations using a convolutional neural network for image segmentation, aiming to detect surface mining with satellite data. Our novel dataset is the first comprehensive measure of asm activity over a vast area: we cover 1.75 million km2 across 13 countries in Sub-Tropical West Africa. The detected asm activities range from 0.1 ha to around 2, 000 ha and present a great diversity, yet we succeed in hitting acceptable compromises of performance, as achieving 70% precision while maintaining simultaneously 42% recall. Ultimately, the remarkable robustness of our procedure makes us confident that our method can be applied to other parts of Africa or the world, thus facilitating research and policy opportunities in this sector.

Citation

Mathieu Couttenier, Sebastien Di Rollo, Louise Inguere, Mathis Mohand, Lukas Schmidt. Mapping artisanal and small-scale mines at large scale from space with deep learning. PloS one. 2022;17(9):e0267963

Mesh Tags

PMID: 36136980

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