BS-Mamba for black-soil area detection on the Qinghai-Tibetan plateau

Extremely degraded grassland on the Qinghai-Tibetan plateau (QTP) presents a significant environmental challenge due to overgrazing, climate change, and rodent activity, which degrade vegetation cover and soil quality. These extremely degraded grassland on QTP, commonly referred to as black-soil areas, require accurate assessment to guide effective restoration efforts. We present a QTP black-soil dataset, annotated under expert guidance. We introduce a neural network model, BS-Mamba, specifically designed for black-soil area detection using unmanned aerial vehicle remote sensing imagery. The BS-Mamba model demonstrates higher accuracy in identifying black-soil areas across two independent test datasets than the state-of-the-art models. This research contributes to grassland restoration by providing an efficient method for assessing the extent of black-soil area on the QTP.