Quality assessment of building footprint data using a deep autoencoder network. Issue 10 (3rd October 2017)

• Record Type: Journal Article
• Authors: Xu, Yongyang; Chen, Zhanlong; Xie, Zhong; Wu, Liang
• Is Part Of: International journal of geographical information science. Volume 31:Issue 10(2017)
• Page Start: 1929
• Page End: 1951
• Publication Date: 2017-10-03
• Subjects: Autoencoder network -- OpenStreetMap -- quality assessment -- building footprint; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2017.1341632
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: 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OpenStreetMap (OSM), has been used in many applications, especially when official spatial data are unavailable or outdated.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.