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Superpixel-wise Assessment of Building Damage from Aerial Images

: Lucks, Lukas; Bulatov, Dimitri; Thönnessen, Ulrich; Böge, Melanie


Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
VISIGRAPP 2019, 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Proceedings. Vol.4: VISAPP : Prague, Czech Republic, February 25-27, 2019
Sétubal: SciTePress, 2019
ISBN: 978-989-758-354-4
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <14, 2019, Prague>
International Conference on Computer Vision Theory and Applications (VISAPP) <14, 2019, Prague>
Conference Paper
Fraunhofer IOSB ()
damage detection; superpixel; feature extraction; random forest

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 attaine d on the superpixel level for roughly 800 buildings.