期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 31, 期 10, 页码 1929-1951出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2017.1341632
关键词
Autoencoder network; OpenStreetMap; quality assessment; building footprint
类别
资金
- National Natural Science Foundation of China [41401443, 41671400]
- Hubei Natural Science Foundation of China [2015CFA012]
- Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG160226]
Volunteered geographic information (VGI), OpenStreetMap (OSM), has been used in many applications, especially when official spatial data are unavailable or outdated. However, the quality of VGI remains a valid concern. In this paper, we use the matched results between OSM building footprints and official data as the samples for training an autoencoder network, which encodes and reconstructs the sample populations according to unknown complex multivariate probability distributions. Then, the OSM data are assessed based on the theory that small probability samples contribute little to the autoencoder network and that they can be recognized by the higher reconstructed errors during training. In the method described here, the selected measures, including data completeness, positional accuracy, shape accuracy, semantic accuracy and orientation consistency between OSM and official data, are used as the inputs for a deep autoencoder network. Finally, building footprint data from Toronto, Canada, are evaluated, and experiments show that the proposed method can assess the OSM data comprehensively, objectively and accurately.
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