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Article

Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN

1
Center for Data Science, Peking University, Beijing 100871, China
2
College of Engineering, Peking University, Beijing 100871, China
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College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
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College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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TH Center of China, Beijing 100094, China
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Institute of Space Science and Applied Technology, Harbin Institute of Technology, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(2), 75; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020075
Received: 19 November 2020 / Revised: 9 January 2021 / Accepted: 9 February 2021 / Published: 13 February 2021
(This article belongs to the Special Issue Geospatial Artificial Intelligence)
Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datasets at two different scales: 1:2000 and 1:10,000. Our results indicate that the granularity factor is effective both in improving the matching score of complex matching and reducing the occurrence of missing matching, and our matching model is suitable for multiscale spatial polygonal object matching, with a high precision and recall reach of 97.2% and 90.6%. View Full-Text
Keywords: multi-scale; spatial polygonal object; match; granularity factor; BPNN multi-scale; spatial polygonal object; match; granularity factor; BPNN
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MDPI and ACS Style

Zhu, D.; Cheng, C.; Zhai, W.; Li, Y.; Li, S.; Chen, B. Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN. ISPRS Int. J. Geo-Inf. 2021, 10, 75. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020075

AMA Style

Zhu D, Cheng C, Zhai W, Li Y, Li S, Chen B. Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN. ISPRS International Journal of Geo-Information. 2021; 10(2):75. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020075

Chicago/Turabian Style

Zhu, Daoye, Chengqi Cheng, Weixin Zhai, Yihang Li, Shizhong Li, and Bo Chen. 2021. "Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN" ISPRS International Journal of Geo-Information 10, no. 2: 75. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020075

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