Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods for utilizing multi-source data and deep learning techniques to improve the efficiency and accuracy of landslide identification in complex environments are still a focus of research and a difficult issue in landslide research. In this study, we address the above problems and construct a landslide identification model based on the shifted window (Swin) transformer. We chose Ya’an, which has a complex terrain and experiences frequent landslides, as the study area. Our model, which fuses features from different remote sensing data sources and introduces a loss function that better learns the boundary information of the target, is compared with the pyramid scene parsing network (PSPNet), the unified perception parsing network (UPerNet), and DeepLab_V3+ models in order to explore the learning potential of the model and test the models’ resilience in an open-source landslide database. The results show that in the Ya’an landslide database, compared with the above benchmark networks (UPerNet, PSPNet, and DeepLab_v3+), the Swin Transformer-based optimization model improves overall accuracies by 1.7%, 2.1%, and 1.5%, respectively; the F1_score is improved by 14.5%, 16.2%, and 12.4%; and the intersection over union (IoU) is improved by 16.9%, 18.5%, and 14.6%, respectively. The performance of the optimized model is excellent.

Existing change detection (CD) research mostly focuses on pixel-level dense prediction, while object detection (OD) for entire damaged/changed buildings is rare. Object-level detection datasets for damaged/changed buildings are also lacking. This paper proposes an object-oriented damaged/changed building CD model, OoCDNet, along with five global-scale OD datasets for damaged/changed buildings. OoCDNet bridges and integrates the dual tasks of CD and OD, driven by the task of locating damaged/changed buildings. By modeling the changes in buildings at the object level between bi-temporal images, it achieves rapid identification of target buildings. OoCDNet consists of four parts, the two paths of the dual-path feature extraction module (DouBackbone) are responsible for extracting the base features of the image, the bidirectional pyramid feature aggregation module (AggNeck) models the change information of the features aggregated at the end, the cross-informative self-attentive short-circuit enhancement module enhances the features from the DouBackbone with highly efficient self-attentive information and supplements the enhancement information to the AggNeck, and the enhanced features with semantic and localization information are fed into the detection module for OD. The proposed OoEWEBD is a global-scale OD dataset for damaged buildings, containing 10,377 image pairs, each sized 256×256 pixels. The remaining four datasets are created based on the WHU_CD, LEVIR-CD+, S2Looking, and xBD datasets, targeting buildings with general changes or those affected by disasters. Compared with state-of-the-art OD and CD methods, OoCDNet can quickly and effectively detect target buildings, achieving the highest accuracy and having high application value. The code and datasets will be available at https://github.com/Haiming-Z/OD-based-Change-Detection-mode-OoCDNet.

Landslide susceptibility prediction usually involves the comprehensive analysis of terrain and other factors that may be distributed with spatial patterns. Without considering the spatial correlation and mutual influence between pixels, conventional prediction methods often focus only on information from individual pixels. To address this issue, the present study proposes a new strategy for neighboring pixel collaboration based on the Unified Perceptual Parsing Network (UPerNet), the Vision Transformer (ViT), and Vision Graph Neural Networks (ViG). This strategy efficiently utilizes the strengths of deep learning in feature extraction, sequence modeling, and graph data processing. By considering the information from neighboring pixels, this strategy can more accurately identify susceptible areas and reduce misidentification and omissions. The experimental results suggest that the proposed strategy can predict landslide susceptibility zoning more accurately. These predictions can identify flat areas such as rivers and distinguish between areas with high and very high landslide susceptibility. Such refined zoning outcomes are significant for landslide prevention and mitigation and can help decision-makers formulate targeted response measures.