Abstract
The utilization of urban land use maps can reveal the patterns of human behavior through the extraction of the socioeconomic and demographic characteristics of urban land use. Remote sensing that holds detailed and abundant information on spectral, textual, contextual, and spatial configurations is crucial to obtaining land use maps that reveal changes in the urban environment. However, social sensing is essential to revealing the socioeconomic and demographic characteristics of urban land use. This data mining approach is related to data cleaning/outlier removal and machine learning, and is used to achieve land use classification from remote and social sensing data. In bicycle and taxi density maps, the daytime destination and nighttime origin density reflects work-related land uses, including commercial and industrial areas. By contrast, the nighttime destination and daytime origin density pattern captures the pattern of residential areas. The accuracy assessment of land use classified maps shows that the integration of remote and social sensing, using the decision tree and random forest methods, yields accuracies of 83% and 86%, respectively. Thus, this approach facilitates an accurate urban land use classification. Urban land use identification can aid policy makers in linking human activities to the socioeconomic consequences of different urban land uses.
Highlights
Urban land use carries crucial information for human activity for urban planning and economic analysis, as well as hazard and pollution management [1]
Issues associated with land use have attracted considerable interest from communities because of the central role of land use as a cause and a consequence of human activity
Land use is a human–environment system consisting of the relationship between human activities and socioeconomic, environmental, and demographic characteristic components of urban land
Summary
Urban land use carries crucial information for human activity for urban planning and economic analysis, as well as hazard and pollution management [1]. Previous studies have used social sensing data for mapping dynamic urban land use patterns [7,8], and traffic and transportation systems [5,6]. Data from remote sensing (satellite image), and social sensing (bicycle trips and taxi routes) were collected to estimate urban land uses. From the social sensing data, the spatial and temporal distribution of human activities, combined with the current telemetry classification, can be used to understand the spatial and temporal distribution of land use and human activities. Social sensing with data cleaning provides highly reliable and detailed information on human activities in urban structures and facilitates the assignment of socioeconomic functions to different zones. This study collects sensing data on bicycle information, taxi routes and remote sensing to estimate urban land use using data cleaning and machine learning. The machine learning of remote sensing and social sensing ensures a highly accurate urban land use classification
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