Identification of Building Damage from UAV-Based Photogrammetric Point Clouds Using Supervoxel Segmentation and Latent Dirichlet Allocation Model.

Accurate assessment of building damage is very important for disaster response and rescue. Traditional damage detection techniques using 2D features at a single observing angle cannot objectively and accurately reflect the structural damage conditions. With the development of unmanned aerial vehicle photogrammetric techniques and 3D point processing, automatic and accurate damage detection for building roof and facade has become a research hotspot in recent work. In this paper, we propose a building damage detection framework based on the boundary refined supervoxel segmentation and random forest-latent Dirichlet allocation classification. First, the traditional supervoxel segmentation method is improved to segment the point clouds into good boundary refined supervoxels. Then, non-building points such as ground and vegetation are removed from the generated supervoxels. Next, latent Dirichlet allocation (LDA) model is used to construct the high-level feature representation for each building supervoxel based on the selected 2D image and 3D point features. Finally, LDA model and random forest algorithm are employed to identify the damaged building regions. This method is applied to oblique photogrammetric point clouds collected from the Beichuan Country Earthquake Site. The research achieves the 3D damage assessment for building facade and roof. The result demonstrates that the proposed framework is capable of achieving around 94% accuracy for building point extraction and around 90% accuracy for damage identification. Moreover, both of the precision and recall for building damage detection reached around 89%. Concluded from comparison analysis, the proposed method improved the damage detection accuracy and the highest improvement ratio is over 8%.