What question did this study set out to answer?

The central aim is to create a robust methodology for assessing building damage after tropical cyclones using UAV imagery.

February 12, 2026ISPRS annals of the photogrammetry, remote sensing and spatial information sciences0 citationsOpen Access

Post-Disaster Building Damage Assessment Using 3D Surface Models Derived from Unmanned Aerial Vehicle (UAV) Imagery

AMAlyssa Patricia J. ManzanoUniversity of the Philippines Diliman ABA. C. BlancoUniversity of the Philippines System

Key Points

The central aim is to create a robust methodology for assessing building damage after tropical cyclones using UAV imagery.
Developed a damage classification methodology employing UAV images after Typhoon Haiyan.
Used Structure-from-Motion (SfM) for 3D surface modeling.
Employed texture analysis and topographic modeling for damage assessment.
Implemented correlation-based feature selection for refining damage predictors.
Utilized Random Forest for predicting building damage levels.
Binary model R-A1 achieved 93.5% accuracy in classifying completely damaged versus undamaged buildings.
Model R-A2 classified damaged and undamaged buildings with 80.3% accuracy.
Ternary model R-B1 classified building damage levels with 81.6% accuracy.
R-A2 model had 73.4% accuracy on previously unseen damaged buildings.
R-B1 model achieved 70.3% accuracy on unseen partially and completely damaged buildings.

Abstract

Abstract. 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.

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