Abstract
This paper presents a new approach for roof facet segmentation based on ridge detection and hierarchical decomposition along ridges. The proposed approach exploits the fact that every roof can be composed of a set of gabled roofs and single facets which are separated by the gabled roofs. In this work, firstly, building footprints stored in OpenStreetMap are used to extract 3D points on roofs. Then, roofs are segmented into roof facets. The algorithm starts with detecting roof ridges using RANSAC since they are parallel to the horizon and situated on the top of the roof. The roof ridges are utilized to indicate the location and direction of the gabled roof. Thus, points on the two roof facets along a roof ridge can be identified based on their connectivity and coplanarity. The results of the segmentation benefit the further process of roof reconstruction because many parameters, including the position, angle and size of the gabled roof can be calculated and used as priori knowledge for the model-driven approach, and topologies among the point segments are made known for the data-driven approach. The algorithm has been validated in the test sites of two towns next to Bavaria Forest national park. The experimental results show that building roofs can be segmented with both high correctness and completeness simultaneously.
Highlights
Recent technological advances such as aerial photogrammetry, laser scanning measurement, In general, 3D building reconstruction can be distinguished between image-based and point cloud based approach
A gabled roof has two roof facets with a shared roof ridge which is situated on the top of the roof and parallel to the horizon
A hipped roof consists of a gabled roof and two disconnected roof facets
Summary
Recent technological advances such as aerial photogrammetry, laser scanning measurement, In general, 3D building reconstruction can be distinguished between image-based and point cloud based approach. It is suitable for detection roofs with simple shape or structure, e.g., flat roof, shed roof and gabled or hipped roof It can fragment or miss the line segments inside of the outlines, due to low contrast, occlusions and bad perspectives. It is not appropriate for roofs with complicated shape and structure, e.g., multi-gabled or hipped roof, or complicated roofs of building group To overcome this limitation, many researchers used stereo images to generate a Digital Surface Model (DSM) to remove non-building structure using height information at first. Many researchers used stereo images to generate a Digital Surface Model (DSM) to remove non-building structure using height information at first They focused on building shape and rooftop contours. Brunn and Weidner [12] separated buildings and vegetation areas using height and geometric information on DSM data, and extracted rooftop geometries using surface normal
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