A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling. Issue 2 (13th March 2023)

• Record Type: Journal Article
• Authors: Liu, Lingjia; Fu, Zhiyi; Xia, Yu; Lin, Hui; Ding, Xiaohui; Liao, Kaitao
• Is Part Of: Transactions in GIS. Volume 27:Issue 2(2023)
• Page Start: 541
• Page End: 563
• Publication Date: 2023-03-13
• Subjects: Geographic information systems -- Periodicals; 910.285
• DOI: 10.1111/tgis.13039
• Languages: English
• ISSNs: 1361-1682

Abstract

Abstract: Volunteered geographic information (VGI) is an emerging phenomenon where anyone can create geographic information and share it with others. Compared with traditional authoritative geospatial data, it has several advantages, such as enriched data, instant updates, and low cost. The object matching method is widely used in VGI quality assessment and data updates. However, VGI matching faces certain challenges, such as the levels of detail that vary from object to object, the uneven distribution of data quality, and the automated matching requirement. To resolve these problems, this article proposes a new matching method that effectively combines the advantages of minimum bounding rectangle combinatorial optimization (MBRCO) and relaxation labeling. The proposed method (1) avoids setting the similarity threshold and weights and does not require training samples. This process is realized based on contextual information and optimization. (2) It overcomes the disadvantage that the MBRCO algorithm cannot distinguish adjacent buildings with similar shapes. Our approach is experimentally validated using two publicly available spatial datasets: OpenStreetMap and AutoNavi map. The experimental studies show that the proposed automatic matching method outperforms all the threshold‐based MBRCO methods and achieves high accuracy with a precision of 97.8% and a recall of 99.2%.