标题: A hybrid approach to building simplification with an evaluator from a backpropagation neural network

作者: Yang, M (Yang, Min); Yuan, T (Yuan, Tuo); Yan, XF (Yan, Xiongfeng); Ai, TH (Ai, Tinghua); Jiang, CJ (Jiang, Chenjun)

来源出版物: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE  DOI: 10.1080/13658816.2021.1873998  提前访问日期: JAN 2021  

摘要: Research has developed numerous algorithms to simplify building data. Each algorithm has strengths and weaknesses in addressing shape characteristics, but no single algorithm can appropriately simplify all buildings. This study proposes a hybrid approach that identifies the best simplified representation of a building among four existing algorithms. The proposed approach applies the four algorithms to generate simplification candidates. With a backpropagation neural network, an evaluator is built through supervised learning based on measurements describing the changes in position, size, orientation, and shape between the original building and the candidates of its simplified representations. The evaluator determines the most appropriate candidate. Experiments on buildings from residential and commercial areas in Shenzhen city show that the hybrid approach can combine the advantages of different algorithms. The percentages of unreasonable simplified buildings in the results obtained using the hybrid algorithm are 3.8% in the residential area and 0 in the commercial area, respectively, which are significantly lower than those in the results of standalone applications of the four algorithms. Furthermore, comparison with the simplification algorithm in the popular software, ArcGIS, confirms that our approach shows better results in terms of corner squaring and maintaining the regional characteristics of buildings.

入藏号: WOS:000609540500001

语言: English

文献类型: Article; Early Access

作者关键词: Building simplification; hybrid approach; backpropagation neural network

地址: [Yang, Min; Yuan, Tuo; Ai, Tinghua; Jiang, Chenjun] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Yan, Xiongfeng] Tongji Univ, Coll Surveying & Geoinformat, Shanghai, Peoples R China.

通讯作者地址: Yan, XF (通讯作者)，Tongji Univ, Coll Surveying & Geoinformat, Shanghai, Peoples R China.

电子邮件地址: xiongfeng.yan@whu.edu.cn