Superpixel-wise Assessment of Building Damage from Aerial Images

Lukas Lucks, Dimitri Bulatov, Ulrich Thönnessen, Melanie Böge

2019

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 attained on the superpixel level for roughly 800 buildings.

Paper Citation

in Harvard Style

Lucks L., Bulatov D., Thönnessen U. and Böge M. (2019). Superpixel-wise Assessment of Building Damage from Aerial Images. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 211-220. DOI: 10.5220/0007253802110220

in Bibtex Style

@conference{visapp19,
author={Lukas Lucks and Dimitri Bulatov and Ulrich Thönnessen and Melanie Böge},
title={Superpixel-wise Assessment of Building Damage from Aerial Images},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={211-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007253802110220},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Superpixel-wise Assessment of Building Damage from Aerial Images
SN - 978-989-758-354-4
AU - Lucks L.
AU - Bulatov D.
AU - Thönnessen U.
AU - Böge M.
PY - 2019
SP - 211
EP - 220
DO - 10.5220/0007253802110220
PB - SciTePress