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  4. Superpixel-wise Assessment of Building Damage from Aerial Images
 
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2019
Konferenzbeitrag
Titel

Superpixel-wise Assessment of Building Damage from Aerial Images

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 attaine d on the superpixel level for roughly 800 buildings.
Author(s)
Lucks, Lukas
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Bulatov, Dimitri
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Thönnessen, Ulrich
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Böge, Melanie
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Hauptwerk
VISIGRAPP 2019, 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Proceedings. Vol.4: VISAPP
Konferenz
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 2019
International Conference on Computer Vision Theory and Applications (VISAPP) 2019
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DOI
10.5220/0007253802110220
Language
Englisch
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IOSB
Tags
  • damage detection

  • superpixel

  • feature extraction

  • random forest

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