Recognizing building groups for generalization: a comparative study. Issue 3 (4th May 2018)

• Record Type: Journal Article
• Authors: Deng, Min; Tang, Jianbo; Liu, Qiliang; Wu, Fang
• Is Part Of: Cartography and geographic information science. Volume 45:Issue 3(2018)
• Page Start: 187
• Page End: 204
• Publication Date: 2018-05-04
• Subjects: Building generalization -- building groups -- pattern recognition -- Gestalt laws of grouping -- map generalization; Cartography -- Periodicals; Geographic information systems -- Periodicals; 526
• DOI: 10.1080/15230406.2017.1302821
• Languages: English
• ISSNs: 1523-0406

Abstract

ABSTRACT: Recognition of building groups is a critical step in building generalization. To find building groups, various approaches have been developed based on the principles of grouping (or the Gestalt laws of grouping), and the effectiveness of these approaches needs to be evaluated. This study presents a comparative analysis of nine typical such approaches, including three approaches that only consider proximity principle and six approaches that consider multiple grouping principles. Real-life dataset at 1:5000, 1:10, 000, and 1:50, 000 scales provided by National Geomatics Center of China is used to evaluate the performance of these approaches. Buildings at smaller scales are used to construct the benchmarks to test the grouping results at larger scales, and the adjusted Rand index is adopted to indicate the accuracy of the detected groups. Significant tests (Friedman test and Wilcoxon signed-rank test) are also performed to provide both the overall and pairwise comparisons of these approaches. The results show that (1) the average accuracy of most existing approaches is between 0.3 and 0.5, and the performances of these approaches are significantly different; (2) when only proximity is considered, the buffer analysis approach performs significantly better than other approaches; (3) when multiple grouping principles are considered, the local constraint-based approach usually performs better than other approaches; (4) existing approaches that consider similarity and/or continuity seldom improve the performance of building grouping.