Earthquake-Induced Terrian and Road Damage Analysis Using UAV-Derived Geospatial and Texture Information

Remote sensing technologies provide effective means for monitoring and analyzing the environmental impacts of natural hazards. Among Earth observation approaches, optical remote sensing has remained a fundamental data source for decades. In recent years, unmanned aerial vehicles (UAVs), also referred to as drones, have emerged as a flexible and cost-effective platform for acquiring high-resolution geospatial data, including optical imagery, thermal data, and LiDAR point clouds. Owing to their high operational flexibility, UAVs are particularly suitable for collecting first-hand spatial data shortly after disaster events, supporting rapid damage assessment. This study employs UAV-based optical imagery acquired after the Noto earthquake, which occurred on 1 January 2024 in Japan, to support post-disaster geovisualization and spatial analysis. Structure-from-motion (SfM) and multi-view stereo (MVS) techniques are applied to reconstruct three-dimensional (3D) geoinformation from the UAV images. Two key products—a textured triangulated irregular network (TIN) model and an orthophoto—are generated to visualize affected areas and support geospatial analysis. The study focuses on interpreting earthquake-induced damage by integrating 3D models and texture information, with particular emphasis on road damage assessment. Texture features are extracted from orthophotos and represented using indicators derived from the grey-level co-occurrence matrix (GLCM). These texture descriptors, combined with geospatial attributes, are used as inputs to an extreme gradient boosting (XGBoost) model for semi-automatic road damage prediction. The predicted damage results are subsequently correlated with the 3D TIN models to identify locations where road damage is likely to occur.  By integrating texture-based analysis with 3D geovisualization, this workflow improves the interpretation of earthquake-related damage across virtual and real-world contexts. The results indicate that UAV-derived optical imagery, combined with machine learning and 3D reconstruction techniques, can support efficient post-disaster damage assessment. This approach enables advanced simulation of decision-making processes and rescue operations, reducing unnecessary costs while improving the effectiveness and timeliness of hazard response.