TOWARDS A MORE EFFICIENT DETECTION OF EARTHQUAKE INDUCED FAÇADE DAMAGES USING OBLIQUE UAV IMAGERY
| Autor: | Duarte, D., Nex, F.C., Kerle, N., Vosselman, G., Stachniss, C., Förstner, W., Schneider, J. |
|---|---|
| Přispěvatelé: | Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, UT-I-ITC-ACQUAL, Department of Earth Systems Analysis, UT-I-ITC-4DEarth |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
lcsh:Applied optics. Photonics
business.industry Computer science lcsh:T Deep learning 0211 other engineering and technologies Point cloud Oblique case lcsh:TA1501-1820 02 engineering and technology lcsh:Technology Urban search and rescue lcsh:TA1-2040 0202 electrical engineering electronic engineering information engineering RGB color model 020201 artificial intelligence & image processing Facade Computer vision Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) 021101 geological & geomatics engineering Remote sensing |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-2-W6, Pp 93-100 (2017) Proceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany, 93-100 STARTPAGE=93;ENDPAGE=100;TITLE=Proceedings of International Conference on Unmanned Aerial Vehicles in Geomatics, 4-7 September 2017, Bonn, Germany |
| ISSN: | 2194-9034 1682-1750 |
| Popis: | Urban search and rescue (USaR) teams require a fast and thorough building damage assessment, to focus their rescue efforts accordingly. Unmanned aerial vehicles (UAV) are able to capture relevant data in a short time frame and survey otherwise inaccessible areas after a disaster, and have thus been identified as useful when coupled with RGB cameras for façade damage detection. Existing literature focuses on the extraction of 3D and/or image features as cues for damage. However, little attention has been given to the efficiency of the proposed methods which hinders its use in an urban search and rescue context. The framework proposed in this paper aims at a more efficient façade damage detection using UAV multi-view imagery. This was achieved directing all damage classification computations only to the image regions containing the façades, hence discarding the irrelevant areas of the acquired images and consequently reducing the time needed for such task. To accomplish this, a three-step approach is proposed: i) building extraction from the sparse point cloud computed from the nadir images collected in an initial flight; ii) use of the latter as proxy for façade location in the oblique images captured in subsequent flights, and iii) selection of the façade image regions to be fed to a damage classification routine. The results show that the proposed framework successfully reduces the extracted façade image regions to be assessed for damage 6 fold, hence increasing the efficiency of subsequent damage detection routines. The framework was tested on a set of UAV multi-view images over a neighborhood of the city of L’Aquila, Italy, affected in 2009 by an earthquake. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|