Unsupervised Deep Clustering Method for Coseismic Landslide Recognition Based on High-Resolution Images and Implicit Knowledge

Rapid identification of numerous coseismic landslides following a major earthquake is essential for emergency response and postdisaster recovery. While convolutional neural networks (CNNs) and vision transformers (ViTs) have shown excellent capability in automatic recognition of coseismic landslides, these models require large amounts of training data and computational resources. In addition, there is a lack of additional constraints to restrict the high false alarms of coseismic landslides. To address these challenges, this study proposes an unsupervised deep clustering method coupling remote sensing and implicit knowledge (UC-RSIK) for the recognition of coseismic landslides. The UC-RSIK model integrates a remote sensing feature extraction branch for image reconstruction and an implicit knowledge branch that derives insights from normalized difference vegetation index (NDVI), seismological, geological, topographic, and geographic data. These extracted features are fused and then processed by a self-training adaptive clustering optimization module. This module incorporates hierarchical clustering and autonomously determines the optimal number of image categories, enhancing the effectiveness and robustness of the clustering process. Tested on datasets from the Iburi, Haiti, and Luding earthquakes, UC-RSIK outperformed the state-of-the-art methods such as K-means, Mini-batch K-means, FCM, FSECSGL, and CAE in key metrics like mean Intersection over Union (mIoU) and $F1$ values. Furthermore, t-SNE visualization confirmed that UC-RSIK effectively separates landslides and no-landslides in the feature space, demonstrating superior clustering and category discriminating abilities. The results highlight UC-RSIK’s potential as a highly accurate, robust, and adaptable tool for coseismic landslide identification in diverse terrain conditions.