Synthesizing location semantics from street view images to improve urban land-use classification. Issue 9 (2nd September 2021)

• Record Type: Journal Article
• Authors: Fang, Fang; Yu, Yafang; Li, Shengwen; Zuo, Zejun; Liu, Yuanyuan; Wan, Bo; Luo, Zhongwen
• Is Part Of: International journal of geographical information science. Volume 35:Issue 9(2021)
• Page Start: 1802
• Page End: 1825
• Publication Date: 2021-09-02
• Subjects: Land-use map -- street view image -- location semantics -- land-use classification; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1831515
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Land-use maps are instrumental to inform urban planning and environmental research. Street view images (SVIs) have shown great potential for automated land-use classification for land-use mapping. However, previous studies overlooked SVI-derived location contextual information that may help improve land-use classification. This study proposes a novel land-use classification method that synthesizes location semantics from SVIs to account for contextual information from SVIs, land parcels and roads around the SVIs. The proposed method first generates land-use scene images (LUSIs) by using an SVI-derived straightforward algorithm. The LUSIs are then relocated to land parcels by using a displacement strategy and classified into land-use types by using a deep learning network. This study determines the land-use types of land parcels with classified LUSIs. Two case studies, consisting of LUSIs for five land-use types, show that introducing location semantics of SVIs can remarkably improve the classification accuracy of land-use types.