A Sentinel-2 Image Dataset for Mining Detection Across Mining Proportion Ranges in the Brazilian Legal Amazon
Resumo
The recent expansion of mining in the Amazon is a major driver of environmental degradation. Machine learning and satellite imagery are effective tools to monitor this problem, but most datasets treat mining detection as a strictly binary task. This ignores how the actual mining proportion inside an image affects classification results. We present a Sentinel-2 dataset designed to explore this behavior. It contains 2,600 image patches collected across the Brazilian Legal Amazon in 2024 using Google Earth Engine and MapBiomas Collection 10 data. The dataset includes metadata detailing geographic locations, mining proportion ranges in 10% intervals, and a suggested practical split for experiments.
Palavras-chave:
Amazon, mining detection, remote sensing, dataset
Referências
Camalan, S., Cui, K., Pauca, V. P., Alqahtani, S., Silman, M., Chan, R., Plemmons, R. J., Dethier, E. N., Fernandez, L. E., and Lutz, D. A. (2022). Change detection of Amazonian alluvial gold mining using deep learning and Sentinel-2 imagery. Remote Sensing, 14(7):1746.
European Space Agency (2025). Sentinel-2 user handbook. Available at: [link]. Accessed on: 13/01/2026.
Gallwey, J., Robiati, C., Coggan, J., Vogt, D., and Eyre, M. (2020). A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining. Remote Sensing of Environment, 248:111970.
Global Initiative Against Transnational Organized Crime (2023). Amazon underworld: economias criminosas na maior floresta tropical do mundo. Technical report, GI-TOC and Amazon Watch and InfoAmazonia, Geneva, Switzerland. Institutional report.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202:18–27.
MapBiomas (2023). Amazônia concentra mais de 90% do garimpo no Brasil. Available at: [link]. Accessed on: 13/01/2026.
Sentinel Hub (2024). s2cloudless: Machine learning cloud detector for Sentinel-2 imagery. Available at: [link]. Accessed on: 13/01/2026.
European Space Agency (2025). Sentinel-2 user handbook. Available at: [link]. Accessed on: 13/01/2026.
Gallwey, J., Robiati, C., Coggan, J., Vogt, D., and Eyre, M. (2020). A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining. Remote Sensing of Environment, 248:111970.
Global Initiative Against Transnational Organized Crime (2023). Amazon underworld: economias criminosas na maior floresta tropical do mundo. Technical report, GI-TOC and Amazon Watch and InfoAmazonia, Geneva, Switzerland. Institutional report.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202:18–27.
MapBiomas (2023). Amazônia concentra mais de 90% do garimpo no Brasil. Available at: [link]. Accessed on: 13/01/2026.
Sentinel Hub (2024). s2cloudless: Machine learning cloud detector for Sentinel-2 imagery. Available at: [link]. Accessed on: 13/01/2026.
Publicado
19/07/2026
Como Citar
GRUPIONI, Leonardo Fajardo; GALLOIS, Thomas Jean Georges; ALMEIDA, Felipe Valencia de.
A Sentinel-2 Image Dataset for Mining Detection Across Mining Proportion Ranges in the Brazilian Legal Amazon. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 17. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 348-351.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2026.22440.
