The Geospatial Information Authority of Japan (GSI) conducts monitoring of crustal and ground deformation associated with earthquakes and volcanic activities using satellite Synthetic Aperture Radar (SAR) through analysis techniques such as interferometric analysis and time-series analysis. In recent years, satellite SAR has been used to detect areal crustal deformation accompanying the Earthquake offshore east of Aomori Prefecture (M7.5) that occurred in December 2025 and volcanic activities at Iwatesan since 2024, as well as topographic changes at Ioto following eruptions in 2025. GSI will continue to utilize satellite SAR like ALOS-4 to monitor crustal and ground deformation both during normal times and disasters, thereby advancing the management of national land.

This paper introduces the “Disaster Drill,” an integrated public-private satellite operation exercise designed for large-scale emergency scenarios. The drill, coordinated by the Consortium for Satellite Earth Observation (CONSEO), confirmed that satellite data is highly effective for rapid damage assessment and for mitigating secondary disasters, underscoring its critical role in disaster management. Furthermore, the exercise demonstrated that public-private collaboration enhances the societal impact of space technologies, paving the way for broader applications in future disaster preparedness and response.

Application of Structure-from-Motion (SfM) and Multi-View Stereo (MVS) to archival aerial photographs enables quantitative re-examination of terrain and surface changes caused by past disasters. This study reassessed three types of historical disasters－volcanic eruptions, landslides, and windthrow events－through differential analysis of pre- and post-event digital surface models (DSMs) reconstructed from the imagery. The SfM/MVS-derived DSMs captured volcanic terrain changes such as scoria-cone formation and lava-flow emplacement, and quantitatively delineated areas affected by landslides and windthrow.

This paper presents recent technological trends in Mobile Mapping Systems (MMS) and introduces case studies in which MMS was utilized to assess road conditions following an earthquake.

This study examines the applicability of the building extraction AI model released by the Geospatial Information Authority of Japan (GSI) to fire-damaged urban areas following the 2024 Noto Peninsula Earthquake. The target area is the central district of Wajima City, which suffered extensive structural losses due to post-earthquake fires. Orthomosaic images were generated from aerial photographs and automatic building extraction was performed using the pretrained convolutional neural network (CNN) model. The results demonstrated that the GSD 11cm image, consistent with the training data specifications, enabled effective detection of building distributions. By overlaying these results with the fire-burned areas, the spatial extent of the damage was intuitively visualized. In contrast, the GSD 2.3cm image produced numerous misclassifications, as the tile size was too small to capture entire building footprints. These findings reveal that the accuracy of AI-based building extraction is highly dependent on input resolution. This case study highlights both the potential and limitations of AI interpretation for rapid disaster assessment and suggests improvements, including multi-scale model development, continuous dataset updates, and cloud-based real-time sharing mechanisms.

Traffic monitoring cameras are installed on expressways to check road conditions and respond quickly to traffic accidents. However, the images captured by these cameras are currently monitored by humans, which causes a delay in response due to missed objects. To improve this situation, the automatic detection of road areas and objects on the areas is necessary day and night. Given the above, we propose a method of automatically detecting road areas in nighttime video images. The white line segments that appear when illuminated by the headlights of passing vehicles are detected from the video images, and the entire road area is automatically detected by accumulating information over time. We developed a prototype program based on the proposed method and conducted experiments using real video images to confirm the effectiveness of the method.