Assessing the environmental impact of the mineral extraction industry plays a critical role in understanding and mitigating the ecological consequences of extractive activities. This paper presents MineSegSAT, a model that presents a novel approach to predicting environmentally impacted areas of mineral extraction sites using the SegFormer deep learning segmentation architecture trained on Sentinel-2 data. The data was collected from non-overlapping regions over Western Canada in 2021 containing areas of land that have been environmentally impacted by mining activities that were identified from high-resolution satellite imagery in 2021. The SegFormer architecture, a state-of-the-art semantic segmentation framework, is employed to leverage its advanced spatial understanding capabilities for accurate land cover classification. We investigate the efficacy of loss functions including Dice, Tversky, and Lovasz loss respectively. The trained model was utilized for inference over the test region in the ensuing year to identify potential areas of expansion or contraction over these same periods. The Sentinel-2 data is made available on Amazon Web Services through a collaboration with Earth Daily Analytics which provides corrected and tiled analytics-ready data on the AWS platform. The model and ongoing API to access the data on AWS allow the creation of an automated tool to monitor the extent of disturbed areas surrounding known mining sites to ensure compliance with their environmental impact goals.

翻译：评估矿产开采行业的环境影响，对于理解并减轻开采活动导致的生态后果具有关键作用。本文提出MineSegSAT模型，该模型采用基于SegFormer深度学习分割架构的创新方法，利用Sentinel-2数据训练，预测受采矿活动影响的区域范围。研究数据收集自2021年加拿大西部非重叠区域，包含经高分辨率卫星影像识别的、受采矿活动环境影响的土地区域。SegFormer架构作为最先进的语义分割框架，凭借其卓越的空间理解能力实现精确的土地覆盖分类。我们分别探讨了Dice损失、Tversky损失和Lovasz损失函数的有效性。训练后的模型被用于对次年测试区域进行推断，以识别相同时期内潜在的区域扩张或收缩变化。通过Earth Daily Analytics的合作，Sentinel-2数据经校正和分幅处理后形成分析就绪数据，并在亚马逊云服务（AWS）平台上开放获取。结合该模型及用于访问AWS数据的持续运行API，可构建自动化工具，持续监测已知矿区周边扰动区域的范围变化，确保其环境影响目标的合规性。