The frequent occurrence of landslides poses a serious threat to people’s lives and property. In order to evaluate disaster hazards based on remote sensing images via machine learning means, it is essential to establish an image database with manually labeled boundaries of landslides. However, the existing datasets do not cover diverse types of mountainous landslides. To address this issue, we propose a high-resolution (1 m) diverse mountainous landslide remote sensing dataset (DMLD), including 990 landslide instances across different terrain in southwestern China. To evaluate the performance of the DMLD, seven state-of-the-art deep learning models with different loss functions were implemented on it. The experiment results demonstrate not only that all of these deep learning methods with different characteristics can adapt well to the DMLD, but also that the DMLD has potential adaptability to other geographical regions.

The deployment of landslide intelligent recognition models in non-training regions encounters substantial challenges, primarily attributed to heterogeneous remote sensing acquisition parameters and inherent geospatial variability in factors such as topography, vegetation cover, and soil characteristics across distinct geographic zones. Addressing the issue of underutilization of landslide contextual information and morphological integrity in domain adaptation methods, this paper introduces a cross-domain landslide extraction approach that integrates image masking with enhanced morphological information. Specifically, our approach implements a pixel-level mask on target domain imagery, facilitating the utilization of context information from the masked images. Furthermore, it establishes a morphological information extraction module, grounded in predefined thresholds and rules, to produce morphological pseudo-labels for the target domain. The results demonstrate that our method achieves an IoU (intersection over union) improvement of 1.78% and 6.02% over the suboptimal method in two cross-domain tasks, respectively, and a remarkable performance enhancement of 33.13% and 31.79% compared to scenarios without domain adaptation. This cross-domain extraction method not only substantially boosts the accuracy of cross-domain landslide identification but also enhances the completeness of landslide morphology information, offering robust technical support for landslide disaster monitoring and early warning systems.