The 5 September 2018 (UTC time) Mw6.6 earthquake of Tomakomai, Japan has triggered about 10,000 landslides with high density, causing widespread concern. We attempted to establish a detailed inventory of this slope failure and use proper methods to assess landslide susceptibility in the entire affected area. To this end we applied the logistic regression (LR) and the support vector machine (SVM) for this study. Based on high-resolution (3 m) optical satellite images (planet image) before and after the earthquake, we delineated 9295 individual landslides triggered by the earthquake, occupying an area of 30.96 km². Ten controlling factors were selected for susceptibility analysis, including elevation, slope angle, aspect, curvature, distances to faults, distances to the epicenter, Peak ground acceleration (PGA), distance to rivers, distances to roads and lithology. Using the LR and SVM, two landslide susceptibility maps were produced for the study area. The results show that in the LR model, the success rate is 84.7% between the landslide susceptibility map and the training dataset, and the prediction rate is 83.9% shown by comparing the test dataset and the landslide susceptibility map. In the SVM model, a success rate of 90.9% exists between the susceptibility map and the test samples, and a prediction rate of 87.1% from comparison of the test dataset and the landslides susceptibility map. In comparison, the performance of the SVM is slightly better than the LR model.

After a major earthquake, the rapid identification and mapping of co-seismic landslides in the whole affected area is of great significance for emergency rescue and loss assessment of seismic hazards. In recent years, researchers have achieved good results in research on a small scale and single environment characteristics of this issue. However, for the whole earthquake-affected area with large scale and complex environments, the correct rate of extracting co-seismic landslides remains low, and there is no ideal method to solve this problem. In this paper, Planet Satellite images with a spatial resolution of 3 m are used to train a seismic landslide recognition model based on the deep learning method to carry out rapid and automatic extraction of landslides triggered by the 2018 Iburi earthquake, Japan. The study area is about 671.87 km², of which 60% is used to train the model, and the remaining 40% is used to verify the accuracy of the model. The results show that most of the co-seismic landslides can be identified by this method. In this experiment, the verification precision of the model is 0.7965 and the F1 score is 0.8288. This method can intelligently identify and map landslides triggered by earthquakes from Planet images. It has strong practicability and high accuracy. It can provide assistance for earthquake emergency rescue and rapid disaster assessment.

Landslide susceptibility mapping (LSM) is a vital tool for proactive disaster mitigation. Although numerous studies utilize slope units (SUs) for LSM, the limited integration of adjacency information, including spatial autocorrelation, often reduces predictive accuracy. In this study, GRASS GIS was utilized to generate slope units, and a spatial logistic regression (SLR) model was developed to incorporate the adjacency information of the slope units to predict the landslide susceptibility. Then, the spatial stratification heterogeneity patterns of landslide susceptibility were analyzed using GeoDetector. The results showed that the SLR model achieved an area under the curve (AUC) of 0.89, a notable improvement of 0.26 compared to the traditional logistic regression (LR) model that does not incorporate adjacency information. This indicates that incorporating adjacency information effectively enhances LSM accuracy by mitigating spatial autocorrelation. Furthermore, lithology, PGV, and distance to the epicenter were identified as the primary factors contributing to the formation of the spatial stratification heterogeneity of landslide susceptibility. Among these, the interaction between lithology and PGV exhibits the strongest nonlinear enhancement. By integrating both mapping units and their adjacency information, this study provides a novel approach to improving the predictive accuracy of LSM. Moreover, by analyzing the driving factors of spatial stratification heterogeneity in landslide susceptibility maps, the study advances the practical utility of LSM for disaster management and mitigation.