BS-Mamba for Black-Soil Area Detection On the Qinghai-Tibetan Plateau
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arXiv
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| Format: | Preprint |
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2025
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| author | Ma, Xuan Lv, Zewen Ma, Chengcai Zhang, Tao Xin, Yuelan Zhan, Kun |
| author_facet | Ma, Xuan Lv, Zewen Ma, Chengcai Zhang, Tao Xin, Yuelan Zhan, Kun |
| contents | 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 area, require accurate assessment to guide effective restoration efforts. In this paper, we present a newly created QTP black-soil dataset, annotated under expert guidance. We introduce a novel neural network model, BS-Mamba, specifically designed for the black-soil area detection using UAV remote sensing imagery. The BS-Mamba model demonstrates higher accuracy in identifying black-soil area 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12495 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BS-Mamba for Black-Soil Area Detection On the Qinghai-Tibetan Plateau Ma, Xuan Lv, Zewen Ma, Chengcai Zhang, Tao Xin, Yuelan Zhan, Kun Computer Vision and Pattern Recognition 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 area, require accurate assessment to guide effective restoration efforts. In this paper, we present a newly created QTP black-soil dataset, annotated under expert guidance. We introduce a novel neural network model, BS-Mamba, specifically designed for the black-soil area detection using UAV remote sensing imagery. The BS-Mamba model demonstrates higher accuracy in identifying black-soil area 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. |
| title | BS-Mamba for Black-Soil Area Detection On the Qinghai-Tibetan Plateau |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.12495 |