MineSegSAT: An Automated System to Evaluate Mining Disturbed Area Extents from Sentinel-2 Imagery

doi:doi:10.57702/552q5lor

MineSegSAT: An Automated System to Evaluate Mining Disturbed Area Extents from Sentinel-2 Imagery

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 predicts environmentally impacted areas of mineral extraction sites using the SegFormer deep learning segmentation architecture trained on Sentinel-2 data.

BibTex:

@dataset{Ezra_MacDonald_and_Derek_Jacoby_and_Yvonne_Coady_2024,
    abstract = {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 predicts environmentally impacted areas of mineral extraction sites using the SegFormer deep learning segmentation architecture trained on Sentinel-2 data.},
    author = {Ezra MacDonald and Derek Jacoby and Yvonne Coady},
    doi = {10.57702/552q5lor},
    institution = {No Organization},
    keyword = {'Environmental Impact', 'Mineral Extraction', 'SegFormer', 'Sentinel-2'},
    month = {dec},
    publisher = {TIB},
    title = {MineSegSAT: An Automated System to Evaluate Mining Disturbed Area Extents from Sentinel-2 Imagery},
    url = {https://service.tib.eu/ldmservice/dataset/minesegsat--an-automated-system-to-evaluate-mining-disturbed-area-extents-from-sentinel-2-imagery},
    year = {2024}
}