Abstract
In this paper, a knowledge-based algorithm is proposed for automatically generating three-dimensional (3D) building models in dense urban areas by using airborne light detection and ranging (LiDAR) data and aerial images. Automatic 3D building modeling using LiDAR is challenging in dense urban areas, in which houses are typically located close to each other and their heights are similar. This makes it difficult to separate point clouds into individual buildings. A combination of airborne LiDAR and aerial images can be an effective approach to resolve this issue. Information about individual building boundaries, derived by segmentation of images, can be utilized for modeling. However, shadows cast by adjacent buildings cause segmentation errors. The algorithm proposed in this paper uses an improved segmentation algorithm (Susaki, J. 2012.) that functions even for shadowed buildings. In addition, the proposed algorithm uses assumptions about the geometry of building arrangement to calculate normal vectors to candidate roof segments. By considering the segmented regions and the normals, models of four common roof types—gable-roof, hip-roof, flat-roof, and slant-roof buildings—are generated. The proposed algorithm was applied to two areas of Higashiyama ward, Kyoto, Japan, and the modeling was successful even in dense urban areas.
Highlights
Disaster preparedness and mitigation require accurate information about buildings and other infrastructure that might be impacted by a typhoon, earthquake, or other disaster
The slant angles of individual roofs can be determined by using airborne light detection and ranging (LiDAR) data, we found that some of the estimated slant angles were unstable, partly because small roofs may not contain enough data points to calculate their normal, owing to occlusion, and partly because the points belonging to neighboring buildings may be included when calculating normals
A knowledge-based algorithm is proposed that automatically generates 3D building models for dense urban areas by using the results of building segmentation from an aerial image
Summary
Disaster preparedness and mitigation require accurate information about buildings and other infrastructure that might be impacted by a typhoon, earthquake, or other disaster. A physics-based fire spread model has been developed [1], and its performance examined by application to the dense urban areas of Kyoto [2]. Such models require many building parameters including three-dimensional (3D) building models. 3D building models are not available to researchers at a reasonable cost. Better simulation of fire spread requires a model that separates individual buildings [1]
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