Authors:
Lukas Lucks
;
Dimitri Bulatov
;
Ulrich Thönnessen
and
Melanie Böge
Affiliation:
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation Gutleuthausstr. 1, 76275 Ettlingen and Germany
Keyword(s):
Damage Detection, Superpixels, Feature Extraction, Random Forest, Classification.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
Surveying buildings that are damaged by natural disasters, in particular, assessment of roof damage, is challenging, and it is costly to hire loss adjusters to complete the task. Thus, to make this process more feasible, we developed an automated approach for assessing roof damage from post-loss close-range aerial images and roof outlines. The original roof area is first delineated by aligning freely available building outlines. In the next step, each roof area is decomposed into superpixels that meet conditional segmentation criteria. Then, 52 spectral and textural features are extracted to classify each superpixel as damaged or undamaged using a Random Forest algorithm. In this way, the degree of roof damage can be evaluated and the damage grade can be computed automatically. The proposed approach was evaluated in trials with two datasets that differed significantly in terms of the architecture and degree of damage. With both datasets, an assessment accuracy of about 90% was attain
ed on the superpixel level for roughly 800 buildings.
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