UAV Photogrammetry for Rapid Post-. Disaster Assessment

Abstract

Unmanned Aerial Vehicle (UAV) photogrammetry has rapidly emerged as a vital tool for post-disaster assessment. In 2019, several studies explored its practical application, demonstrating high-resolution mapping capabilities for evaluating natural disaster damage. Notably, Kim et al. (2019) developed a UAV-based surveying procedure implemented in Korea, producing orthophotos with ground sample distances under 5 cm and detailed 3D point clouds of landslide and flood-affected areas ISPRS Archives. Their work showed that UAV photogrammetry enables timely, quantitative damage analysis—measuring soil erosion, facility destruction, and landslide geometries—with mapping accuracy comparable to traditional labor-intensive field surveys noa.gwlb.de. Similarly, Kerle et al. (2019) reviewed six years of European projects and highlighted UAVs’ capacity for high-resolution structural damage mapping, leveraging stereo imagery, point clouds, and machine learning to support first responders ISPRS Archives. Other 2019 research introduced fast image-stitching algorithms using SURF features for rapid UAV imagery processing in emergency contexts SPIE Digital Library. This study synthesizes insights from key 2019 works to outline the operational workflow, benefits, and challenges of UAV photogrammetry in disaster contexts. The methodology emphasizes ease of deployment, high spatial accuracy, and efficiency relative to conventional surveys. Findings suggest UAV photogrammetry offers transformative potential, though influenced by terrain, regulations, and processing demands. Recommendations for future improvement include enhanced automation and integration with machine learning tools for real-time damage detection.