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Open AccessArticle

Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis

by Xiaodong Zhou 1,2,3, Zhe Chen 1, Xiang Zhang 1,4,5,* and Tinghua Ai 1,4,5
1
School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
State Key Laboratory of Geo-Information Engineering, Xi’an 710054, China
3
Xi’an Research Institute of Surveying and Mapping, Xi’an 710054, China
4
Key Laboratory of Geographic Information System, Ministry of Education, Wuhan 430079, China
5
Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geo-Information, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(10), 406; https://doi.org/10.3390/ijgi7100406
Received: 31 August 2018 / Revised: 4 October 2018 / Accepted: 9 October 2018 / Published: 13 October 2018
Crowd-sourced geographic information is becoming increasingly available, providing diverse and timely sources for updating existing spatial databases to facilitate urban studies, geoinformatics, and real estate practices. However, the discrepancies between heterogeneous datasets present challenges for automated change detection. In this paper, we identify important measurable factors to account for issues like boundary mismatch, large offset, and discrepancies in the levels of detail between the more current and to-be-updated datasets. These factors are organized into rule sets that include data matching, merge of the many-to-many correspondence, controlled displacement, shape similarity, morphology of difference parts, and the building pattern constraint. We tested our approach against OpenStreetMap and a Dutch topographic dataset (TOP10NL). By removing or adding some components, the results show that our approach (accuracy = 0.90) significantly outperformed a basic geometric method (0.77), commonly used in previous studies, implying a more reliable change detection in realistic update scenarios. We further found that distinguishing between small and large buildings was a useful heuristic in creating the rules. View Full-Text
Keywords: change detection; OpenStreetMap; update; scale; data matching; turning function; Delaunay triangulation; building patterns; cartographic generalization change detection; OpenStreetMap; update; scale; data matching; turning function; Delaunay triangulation; building patterns; cartographic generalization
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MDPI and ACS Style

Zhou, X.; Chen, Z.; Zhang, X.; Ai, T. Change Detection for Building Footprints with Different Levels of Detail Using Combined Shape and Pattern Analysis. ISPRS Int. J. Geo-Inf. 2018, 7, 406.

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