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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.07740 |
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| _version_ | 1866918489560711168 |
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| author | Kahl, Matthias Chen, Zhaiyu Saha, Sudipan Kochupillai, Mrinalini Kondmann, Lukas Zhu, Xiao Xiang |
| author_facet | Kahl, Matthias Chen, Zhaiyu Saha, Sudipan Kochupillai, Mrinalini Kondmann, Lukas Zhu, Xiao Xiang |
| contents | Mining operations are of utmost importance to the economy of some nations. However, such operations result in land-use change, very high energy consumption, and negative impacts on the environment, including soil erosion and deforestation. The mining process can impact an area much larger than the mining site itself. Adding to the negative externalities linked to mining is the fact that, in addition to government-sanctioned legal mining operations, illegal mining is widespread, including in various countries of Africa. The ability to monitor remote mining site activities can be useful, e.g., for the detection of illegal artisanal mining activities and their environmental impacts. An important outcome of such monitoring could include a better understanding of the interrelationship between mine facility attributes (e.g., mining types, processing methods, commodities, etc.) and their impact on the natural environment. In this work, we present a data set that contains 150 Large Scale Mining (LSM) sites and 870km^2 annotated area of Artisanal Small-scale Mining (ASM) sites. The metadata includes nine eminent LSM sections and 27 mining site attributes for each LSM site. We also discuss the data set's possible contribution to the research community, social and environmental consequences, and researchers' responsibilities from an ethics perspective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_07740 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LAMES: A Large-Scale and Artisanal Mining Environmental Segmentation Dataset Kahl, Matthias Chen, Zhaiyu Saha, Sudipan Kochupillai, Mrinalini Kondmann, Lukas Zhu, Xiao Xiang Computer Vision and Pattern Recognition Mining operations are of utmost importance to the economy of some nations. However, such operations result in land-use change, very high energy consumption, and negative impacts on the environment, including soil erosion and deforestation. The mining process can impact an area much larger than the mining site itself. Adding to the negative externalities linked to mining is the fact that, in addition to government-sanctioned legal mining operations, illegal mining is widespread, including in various countries of Africa. The ability to monitor remote mining site activities can be useful, e.g., for the detection of illegal artisanal mining activities and their environmental impacts. An important outcome of such monitoring could include a better understanding of the interrelationship between mine facility attributes (e.g., mining types, processing methods, commodities, etc.) and their impact on the natural environment. In this work, we present a data set that contains 150 Large Scale Mining (LSM) sites and 870km^2 annotated area of Artisanal Small-scale Mining (ASM) sites. The metadata includes nine eminent LSM sections and 27 mining site attributes for each LSM site. We also discuss the data set's possible contribution to the research community, social and environmental consequences, and researchers' responsibilities from an ethics perspective. |
| title | LAMES: A Large-Scale and Artisanal Mining Environmental Segmentation Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.07740 |