用无人机影像精准识别青藏高原黑土滩，助力草地修复
BS-Mamba for Black-Soil Area Detection On the Qinghai-Tibetan Plateau
- 基于无人机影像设计新模型BS-Mamba
- 在两个测试集上精度超越现有最优模型
- 为草地退化评估提供高效工具，适合生态修复研究者
青藏高原（QTP）极端退化草原因过度放牧、气候变化和鼠类活动导致植被覆盖和土壤质量下降，形成常被称为黑土滩的退化区域。准确评估这些区域对有效恢复至关重要。本文构建了首个由专家标注的QTP黑土滩数据集，并提出一种新型神经网络模型BS-Mamba，专用于基于无人机遥感影像的黑土滩检测。该模型在两个独立测试数据集上均表现出优于当前最优模型的识别精度。本研究为草地恢复提供了高效的评估方法。
原文摘要 · Abstract (English)
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.
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