Recent advances in artificial intelligence have increasingly empowered landslide detection and geological hazard mitigation. However, existing deep learning architectures still face challenges in global contextual modeling, multi-scale feature extraction, and computational efficiency. To address these challenges, we propose the MSRS-MambaUNet, a novel multi-scale remote sensing model designed for landslide detection. The proposed model integrates the Omnidirectional Selective Scan Module (OSSM) and the Multi-Scale Feed-Forward Network (MS-FFN) as its core components. Specifically, the OSSM block globally models multi-directional long-range dependencies, while the MS-FFN aggregates multi-directional contextual information to facilitate the efficient extraction of multi-scale features. We evaluate the proposed model on two representative clustered landslide events triggered by the 2022 Lushan earthquake in a seismically active mountainous region and the 2024 Shaoguan rainstorm in a rainfall-prone hilly area. Beyond the red-green-blue bands of optical imagery, additional features derived from synthetic aperture radar, spectral indices, and topographic data are incorporated through a progressive feature enrichment strategy to enhance data representation capability. Experimental results show that the proposed model outperforms conventional approaches in both study areas. In the Lushan study area, F 1 -score and intersection over union (IoU) increase by 0.81%–4.06% and 1.33%–6.69%, respectively, while in the Shaoguan study area, the corresponding improvements range from 0.35%–6.87% and 0.56%–10.92%. Multi-source remote sensing data integration also improves detection performance, with topographic features acting as among the most effective auxiliary inputs, yielding improvements of 7.50% ( F 1 -score ) and 12.00% ( IoU ) in Lushan, and 2.63% and 4.05% in Shaoguan.
Zhang et al. (Wed,) studied this question.