ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-X-5-W4-2025-331-2026 Post-Disaster Building Damage Assessment Using 3D Surface Models Derived from Unmanned Aerial Vehicle (UAV) Imagery Manzano Alyssa Patricia J. https://orcid.org/0009-0006-9331-8879 1 Blanco Ariel C. 1 2 Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City, Philippines Training Center for Applied Geodesy and Photogrammetry, University of the Philippines Diliman, Quezon City, Philippines 10 02 2026 X-5/W4-2025 331 337 Copyright: © 2026 Alyssa Patricia J. Manzano 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/X-5-W4-2025/331/2026/isprs-annals-X-5-W4-2025-331-2026.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/X-5-W4-2025/331/2026/isprs-annals-X-5-W4-2025-331-2026.pdf

The Philippines is highly vulnerable to tropical cyclones (TCs), which frequently cause devastating impacts on infrastructure and communities. Rapid and accurate identification of damage extent and location is essential to trigger appropriate post-disaster response, expedite recovery, and facilitate better reconstruction. This study developed a methodology to classify building damage using unmanned aerial vehicle (UAV) images collected in February 2014 after Typhoon Haiyan hit the study area located in Tacloban City in November 2013. The methodology employed Structure-from-Motion (SfM) technique, texture analysis, and topographic modeling to analyze and extract building damage information. A damage rating system using percent area of damage as basis for damage severity was also developed. Correlation-based feature selection algorithm was used to refine and reduce the possible building damage predictor attributes. Random Forest was used to predict each building’s level of damage. Binary model R-A1, which classified completely damaged and undamaged buildings, had an accuracy of 93.5%, average precision of 0.938, average recall of 0.935 and average f-measure of 0.935, while model R-A2, which classified damaged and undamaged buildings, had an accuracy of 80.3%, average precision of 0.803, average recall of 0.803 and average f-measure of 0.803. Ternary model R-B1, which classified completely damaged, partially damaged and undamaged buildings, had an accuracy of 81.6%, average precision of 0.812, average recall of 0.816, and average f-measure of 0.813. The R-A2 model had an accuracy of 73.4% when tasked to classify 613 previously unseen damaged buildings. The R-B1 model had an accuracy of 70.3% when tasked to classify 575 previously unseen partially damaged and completely damaged buildings. This study highlighted the challenges in identifying and classifying building damage markers, some of which are unique to the Philippine setting, and demonstrated the value of UAV-based assessments for rapid and high-resolution damage evaluation.