An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification

• Published in: ISPRS international journal of geo-information Vol. 8; no. 2; p. 90
• Main Authors: Ge, Panpan, He, Jun, Zhang, Shuhua, Zhang, Liwei, She, Jiangfeng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 15.02.2019
• Subjects: Accuracy; Algorithms; big data; Cellular telephones; Classification; Clustering; Evaluation; Feature extraction; Frameworks; fuzzy c-means; fuzzy comprehensive evaluation; human activity features; Inflow; Land use; Land use classification; Outflow; Remote sensing; Time series; Urban areas; Urban planning
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi8020090

Urban land use information is critical to urban planning, but the increasing complexity of urban systems makes the accurate classification of land use extremely challenging. Human activity features extracted from big data have been used for land use classification, and fusing different features can help improve the classification. In this paper, we propose a framework to integrate multiple human activity features for land use classification. Features were fused by constructing a membership matrix reflecting the fuzzy relationship between features and land use types using the fuzzy c-means (FCM) clustering method. The classification results were obtained by the fuzzy comprehensive evaluation (FCE) method, which regards the membership matrix as the fuzzy evaluation matrix. This framework was applied to a case study using taxi trajectory data from Nanjing, and the outflow, inflow, net flow and net flow ratio features were extracted. A series of experiments demonstrated that the proposed framework can effectively fuse different features and increase the accuracy of land use classification. The classification accuracy achieved 0.858 (Kappa = 0.810) when the four features were fused for land use classification.