MineSegSAT: An automated system to evaluate mining disturbed area
  extents from Sentinel-2 imagery
Ezra MacDonald Derek Jacoby Yvonne Coady
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.