Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the SVM-LSM toolbox. The toolbox realizes landslide susceptibility mapping based on a support vector machine (SVM), which can be integrated into the ArcGIS or ArcGIS Pro platform. The toolbox includes three sub-toolboxes, namely: (1) influence factor production, (2) factor selection and dataset production, and (3) model training and prediction. Influence factor production provides automatic calculation of DEM-related topographic factors, converts line vector data to continuous raster factors, and performs rainfall data processing. Factor selection uses the Pearson correlation coefficient (PCC) to calculate the correlations between factors, and the information gain ratio (IGR) to calculate the contributions of different factors to landslide occurrence. Dataset sample production includes the automatic generation of non-landslide data, data sample production and dataset split. The accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) are used to evaluate the prediction ability of the model. In addition, two methods—single processing and multiprocessing—are used to generate LSM. The prediction efficiency of multiprocessing is much higher than that of the single process. In order to verify the performance and accuracy of the toolbox, Wuqi County, Yan’an City, Shaanxi Province was selected as the test area to generate LSM. The results show that the AUC value of the model is 0.8107. At the same time, the multiprocessing prediction tool improves the efficiency of the susceptibility prediction process by about 60%. The experimental results confirm the accuracy and practicability of the proposed toolbox in LSM.

Semantic segmentation of remote sensing images is crucial for disaster monitoring, urban planning, and land use. Due to scene complexity and multi-scale features of targets, semantic segmentation of remote sensing images has become a challenging task. Deep convolutional neural networks capture remote contextual dependencies is limited. Meanwhile, restoring the image size quickly leads to under-sampling at object edges, resulting in poor boundary prediction. Therefore, this paper proposes a multi-scale semantic segmentation network of remote sensing images based on edge optimization, namely MSEONet. The decoder of the network consists of a multi-scale context aggregation (MSCA) module, a coarse edge extraction (CEE) module, and an edge point feature optimization (EPFO) module. The MSCA module is used to capture multi-scale contextual information and global dependencies between pixels. The CEE module is used for boundary extraction of multi-class coarse segmentation results. The EPFO module is used to optimize edge point features during the up-sampling process. We conducted extensive experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam 2D dataset, the ISPRS Vaihingen 2D dataset and FLAIR #1 dataset. The results show the effectiveness and superiority of our proposed MSEONet model compared to most of the state-of-the-art models. The CEE and EPFO modules can enhance the edge segmentation effect without increasing the computational and parametric quantities too much. The code is publicly available at https://github.com/HuangWBill/MSEONet.

Wide area landslide detection is a major international research hotspot in the field of geological hazards, and the integration of multi-temporal optical satellite images and spaceborne interferometric synthetic aperture radar (InSAR) appears to be an effective way to realize this. In this paper, a technical framework is presented for wide area landslide detection: (i) multi-temporal satellite optical images are used to detect landslides with distinguishable geomorphological features; (ii) Generic Atmospheric Correction Online Service (GACOS) assisted InSAR stacking is employed to generate annual surface displacement rate maps in radar line of sight using satellite SAR images from both ascending and descending tracks, which are in turn utilized to automatically detect active landslides from ground motion using hotspot analysis, and (iii) the distribution characteristics of the detected landslides are investigated by examining their relationships with topographic and hydrological factors. Three expressways in Sichuan Province, China—namely the Yakang (Ya’an-Kangding), Yaxi (Ya’an-Xichang), and Lushi (Luding-Shimian) expressways—and their surrounding regions (a total area of approximately 20,000 square kilometers) were chosen as the study area. A total of 413 landslides were detected, among which 320 were detected using multi-temporal satellite optical images, and 109 were detected using GACOS-assisted InSAR stacking. It should be noted that only 16 landslides were detected by both approaches; these landslides all exhibited not only obvious geomorphological features but also ground motion. A statistical analysis of the topographic and hydrological factors shows that of the detected landslides: 81% are distributed at elevations of 1000–2500 m, over 60% lie within the elevation range of 100~400 m, and 90% present with medium and steep slopes (20°~45°), and 80% are located within areas seeing an annual rainfall of 950~1050 mm. Nine landslides were found to pose potential safety hazards to the expressways. The research findings in this paper have directly benefitted the Sichuan expressways; equally important, it is believed that the technical framework presented in this paper will provide guidance for hazard mitigation and the prevention of transportation hazards in the future.

The rapid mapping of co-seismic landslides is essential for emergency management and loss assessment. Deep learning algorithms generally follow a supervised learning workflow, where the trained model is used to predict landslides in surrounding areas, achieving landslide mapping with high accuracy. For a new study area landslide extraction task, the performance of the model trained on a specific dataset will be greatly reduced due to the varying data distribution of co-seismic landslides. Considering urgent need for large large-scale co-seismic landslide mapping, we developed a generalized deep learning-based landslide identification method. Firstly, a new model – ResU-SENet is developed to generate semantic segmentation maps of landslides. The proposed model adaptively emphasizes the channel-wise weights of the input data. Three multi-domain models are then designed by combining annotated landslide samples from two different domains to improve the model generalization ability. Lastly, the trained models are applied directly to completely unknown domains to test model generalizability. Experiments in Iburi and Jiuzhaigou showed that the proposed model yielded the recall values of 5.93% and 7.51% higher than ResU-Net. The adoption of multi-domain models effectively reduced the number of new training samples required by 50% and maintained a similar identification performance as if trained entirely with new samples. Applying the models trained by Jiuzhaigou and Iburi samples directly to Palu, the F1-score under the ResU-SENet model reached 0.6875. Moreover, the connections of model generalization and data distribution was demonstrated. This work could provide a fast response for future large-scale co-seismic landslide mapping.

The use of deep learning techniques for semantic segmentation in remote sensing has been increasingly prevalent. Effectively modeling remote contextual information and integrating high-level abstract features with low-level spatial features are critical challenges for semantic segmentation task. This paper addresses these challenges by constructing a Graph Space Reasoning (GSR) module and a Dual-channel Cross Attention Upsampling (DCAU) module. Meanwhile, a new domain incremental learning (DIL) framework is designed to alleviate catastrophic forgetting when the deep learning model is used in cross-domain. This framework makes a balance between retaining prior knowledge and acquiring new information through the use of frozen feature layers and multi-feature joint loss optimization. Base on this, a new domain incremental learning of remote sensing semantic segmentation with multi-feature constraints in graph space (GSMF-RS-DIL) framework is proposed. Extensive experiments, including ablation experiments on the ISPRS and LoveDA datasets, demonstrate that the proposed method achieves superior performance and optimal computational efficiency in both single-domain and cross-domain tasks. The code is publicly available at https://github.com/Huang WBill/GSMF-RS-DIL.

Large-scale and long-term landslide susceptibility assessments are crucial for revealing the patterns of landslide risk variation and for guiding the formulation of disaster prevention and mitigation policies at the national level. This study, through the establishment of a global dynamic landslide susceptibility model, uses the multi-dimensional analysis strategy and studies the development trend of China’s large-scale landslide susceptibility. First, a global landslide dataset consisting of 8023 large-scale landslide events triggered by rainfall and earthquakes between 2001 and 2020 was constructed based on the GEE (Google Earth Engine) platform. Secondly, a global dynamic landslide susceptibility model was developed using the ResNet18 (18-layer residual neural network) DL (deep learning) framework, incorporating both dynamic and static LCFs (landslide conditioning factors). The model was utilized to generate sequential large-scale landslide susceptibility maps for China from 2001 to 2022. Finally, the MK (Mann–Kendall) test was used to investigate the change trends in the large-scale landslide susceptibility of China. The results of the study are as follows. (1) The ResNet18 model outperformed SVMs (support vector machines) and CNNs (convolutional neural networks), with an AUC value of 0.9362. (2) SHAP (Shapley Additive Explanations) analyses revealed that precipitation played an important factor in the occurrence of landslides in China. In addition, profile curvature, NDVI, and distance to faults are thought to have a significant impact on landslide susceptibility. (3) The large-scale landslide susceptibility trends in China are complex and varied. Particular emphasis should be placed on Southwest China, including Chongqing, Guizhou, and Sichuan, which exhibit high landslide susceptibility and notable upward trends, and also consider Northwest China, including Shaanxi and Shanxi, which have high susceptibility but decreasing trends. These results provide valuable insights for disaster prevention and mitigation in China.