构建大规模手工采矿数据集，助力环境监测与非法开采识别。
LAMES: A Large-Scale and Artisanal Mining Environmental Segmentation Dataset
- 采集150个大型矿场与870平方公里手工矿场标注数据。
- 包含9类大型矿场特征及27项矿场属性元数据。
- 适用于环境影响研究、非法采矿检测与可持续采矿分析。
采矿对部分国家经济至关重要，但带来土地利用变化、高能耗及土壤侵蚀、森林砍伐等环境问题，影响范围常远超矿区本身。除合法采矿外，非洲等地非法手工采矿现象普遍。监测偏远矿区活动有助于发现非法行为及其环境影响，进而理解矿场特征（如采矿类型、处理方法、矿产种类等）与自然环境之间的关联。本文构建了一个数据集，包含150个大型采矿点（LSM）和870km²的手工小型采矿点（ASM）标注区域，每处大型采矿点配有9个显著区域及27项属性元数据。该数据集可支持环境影响研究、非法采矿识别，并引发关于研究伦理与社会影响的讨论。
原文摘要 · Abstract (English)
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.
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