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UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning
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DOI：10.5194/nhess-15-1087-2015.png)
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Abstract
En 中文
Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of fa double dagger ades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on fa double dagger ades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.
Keywords:
HIGH-RESOLUTION SATELLITE
BUILDING DAMAGE
EARTHQUAKE
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