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Assessing the environmental impact of the mineral extraction industry plays acritical role in understanding and mitigating the ecological consequences ofextractive activities. This paper presents MineSegSAT, a model that presents anovel approach to predicting environmentally impacted areas of mineralextraction sites using the SegFormer deep learning segmentation architecturetrained on Sentinel-2 data. The data was collected from non-overlapping regionsover Western Canada in 2021 containing areas of land that have beenenvironmentally impacted by mining activities that were identified fromhigh-resolution satellite imagery in 2021. The SegFormer architecture, astate-of-the-art semantic segmentation framework, is employed to leverage itsadvanced spatial understanding capabilities for accurate land coverclassification. We investigate the efficacy of loss functions including Dice,Tversky, and Lovasz loss respectively. The trained model was utilized forinference over the test region in the ensuing year to identify potential areasof expansion or contraction over these same periods. The Sentinel-2 data ismade available on Amazon Web Services through a collaboration with Earth DailyAnalytics which provides corrected and tiled analytics-ready data on the AWSplatform. The model and ongoing API to access the data on AWS allow thecreation of an automated tool to monitor the extent of disturbed areassurrounding known mining sites to ensure compliance with their environmentalimpact goals.

MineSegSAT: An automated system to evaluate mining disturbed area extents from Sentinel-2 imagery