LAMES: A Large-Scale and Artisanal Mining Environmental Segmentation Dataset

2026-05-08

AI Summary

This study addresses the critical lack of large-scale, finely annotated remote sensing datasets for monitoring legal and illegal mining activities and their environmental impacts. To bridge this gap, the authors construct and release the first publicly available segmentation dataset dedicated to mining-related environmental research, encompassing 150 large-scale mining sites and 870 square kilometers of artisanal and small-scale mining areas. The dataset integrates high-resolution satellite imagery with multidimensional metadata, featuring nine mining site categories and 27 structured attributes. Through a systematic geospatial and environmental segmentation annotation methodology, this resource fills a significant data void in the field and provides essential support for applications such as illicit mining detection, ecological impact assessment, and evidence-based sustainable policy formulation.

Abstract

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.

Problem

• mining monitoring
• illegal mining
• environmental impact
• artisanal small-scale mining
• land-use change

Innovation

• mining environmental segmentation
• artisanal small-scale mining (ASM)
• large-scale mining (LSM)
• remote sensing dataset
• land-use change monitoring