Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints
Currently, very few roof shape information for complex buildings is available on OSM. Moreover, additional data requirements (e.g. 3D point clouds) limit the applicability of many roof reconstruction approaches. To mitigate this issue, we propose an approach to roof shape recommendations for complex...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2018
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| In: |
International journal of digital earth
Year: 2018, Volume: 11, Issue: 10, Pages: 1039-1063 |
| ISSN: | 1753-8955 |
| DOI: | 10.1080/17538947.2017.1373867 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1080/17538947.2017.1373867
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| Author Notes: | Xuke Hu, Hongchao Fan, Alexey Noskov |
| Summary: | Currently, very few roof shape information for complex buildings is available on OSM. Moreover, additional data requirements (e.g. 3D point clouds) limit the applicability of many roof reconstruction approaches. To mitigate this issue, we propose an approach to roof shape recommendations for complex buildings by exploring the inherited characteristics of building footprints: the disclosure of rectangles combinations in a partition of footprints and the symmetrical features of footprints. First, it decomposes a complex footprint into rectangles by using an advanced minimal non-overlapping cover algorithm. Second, a graph-based symmetry detection algorithm is proposed to identify all the symmetrical sub-clusters in partitions. Then, a set of selection rules are defined to rank partitions, and the best ones are chosen for roof shape recommendation. Finally, a set of combination rules and a symmetry rule are defined. It enables to evaluate the probability of a footprint being a certain combination of roof shapes. Experimental results show the growth of the probability of correctly recommending roof shapes for single rectangles and buildings from a prior probability of 17-45% and from a prior probability of 0.29-14.3%, removing 60% and 93% of the incorrect roof shape options, respectively. |
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| Item Description: | Published online: 12 Sep 2017 Gesehen am 10.10.2019 |
| Physical Description: | Online Resource |
| ISSN: | 1753-8955 |
| DOI: | 10.1080/17538947.2017.1373867 |