GUO Qingsheng, LI Jiayi, CAO Yuanhui, WANG Yong, LIU Jiping, ZHENG Chuanbang. Automatic Aggregation of Building Footprint Polygons[J]. Geomatics and Information Science of Wuhan University, 2021, 46(1): 12-18. DOI: 10.13203/j.whugis20190345
Citation: GUO Qingsheng, LI Jiayi, CAO Yuanhui, WANG Yong, LIU Jiping, ZHENG Chuanbang. Automatic Aggregation of Building Footprint Polygons[J]. Geomatics and Information Science of Wuhan University, 2021, 46(1): 12-18. DOI: 10.13203/j.whugis20190345

Automatic Aggregation of Building Footprint Polygons

Funds: 

The National Natural Science Foundation of China 41871378

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  • Author Bio:

    GUO Qingsheng, PhD, professor, majors in cartographic generalization, intelligent processing and visualization of geographic information.E-mail: guoqingsheng@whu.edu.cn

  • Received Date: January 06, 2020
  • Published Date: January 04, 2021
  • Building footprint polygons have been used in a wide range of aspects in many fields. However, they are too detailed in many applications to meet the production requirements. Aggregating building footprint polygons can remove unnecessary details while preserving the overall structure and visual impression of them. Nowadays they are usually generated from high resolution cadastral and remote sensing data, having many topological problems. These topological problems will influence application of the data, then bring new requirements to buildings aggregation in automated cartographic generalization. So we should solve these new problems. This paper proposes a method to aggregate building footprint polygons automatically, which can remove these topological problems. This method can effectively maintain the consistency of spatial topological relationship among building footprint polygons, and make three dimension visualization of independent buildings more convenient.And a desktop application to implement our proposed method in this paper. Comparison and analysis of experimental results with results from polygon generalization tool of ArcGIS verified that the proposed method is effective and practicable. The results show that our method has following advantages: (1) This method can remove topological problems while aggregating building footprint polygons. (2) Our classification of triangles is beneficial for aggregation of building footprint polygons. (3) This method can acquire overall building contour conforming cognitive knowledges and independent building footprint polygons with topological consistency at the same time.
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