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

Sensors
Chaoxian LiuLihong Huang

Abstract

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 fr...Continue Reading

References

Jul 11, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Hao Wu, Saurabh Prasad
Sep 13, 2018·Sensors·Jongseong ChoiMohammad R Jahanshahi

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Methods Mentioned

BETA
feature extractor

Software Mentioned

DJI
Supervoxel
Ground Station
Visual C +
Agisoft PhotoScan
Pix4d
PyCharm
Ostu

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