Abstract
Abstract. Night-time light (NTL) remote sensing data has been widely used in the analysis of human activities at night for a large extent, such as light pollution monitoring and recognition of urban functional regions. In most previous studies, the spatial resolutions of NTL remote sensing data are rather low (e.g., 500 m or coarser). Besides, remote sensing classification of land use rather than land cover types is often a hard task. Due to the reasons, it is difficult to meet the demand of urban refined management. In this study, mobile big data and Luojia1-01 (LJ1-01) NTL remote sensing satellite data with higher spatial resolution are adopted for extracting urban functional regions at the street-level scale. Taking Shenzhen city as a case, mobile big data (i.e., the volume of mobile devices) with the accuracy of approximate 150 m (i.e., 7-bit GeoHash encoding format) is integrated with NTL remote sensing data. After a hot spot analysis, the correlation between the two types of data are analysed. The typical supervised classification algorithms including KNN, SVM and random forest are used to distinguish urban functional regions, which are represented by five types, namely residential, business and commercial, industrial, transportation and other areas. The results show the effectiveness in extracting land use types in cities by combination of Luojia1-01 night-time light remote sensing and mobile big data. This study provides a basis for monitoring night light pollution of residential area, urban planning and the construction of smart cities.
Highlights
With an increasing usage of mobile phone in modern cities, mobile big data has been widely utilized for urban analysis, such as estimation of travel demand (Toole et al, 2015), analysis of the distribution of economy activities (Chang et al, 2014), home detection (Vanhoof et al, 2018), classification of land use and land cover types (Mao et al, 2016) and understanding of individual mobility patterns
Relevant studies are focused on urban information extraction (Li and Chen, 2018), urban built-up areas analysing (Ouyang et al, 2016) night-time light pollution (Kuechly et al, 2012), identification of land use (Aubrecht and León Torres, 2016) and analysis of economic activity (Doll et al, 2006)
Recognition of urban functional regions based on night-time light remote sensing was conducted at city-level scale due to the restriction of the coarser spatial resolutions
Summary
With an increasing usage of mobile phone in modern cities, mobile big data has been widely utilized for urban analysis, such as estimation of travel demand (Toole et al, 2015), analysis of the distribution of economy activities (Chang et al, 2014), home detection (Vanhoof et al, 2018), classification of land use and land cover types (Mao et al, 2016) and understanding of individual mobility patterns. Apart from mobile phone data, night-time light (NTL) remote sensing which reflects human activities is important for studying social issues such as poverty, environment, and ecology (Hu et al, 2017). This kind of data is utilized as an important supplementary data for the census (Calabrese et al, 2013). Due to the coarse resolution of NTL remote sensing data, traditional NTL data are inappropriate for classifying urban land use in details. Recognition of urban functional regions based on night-time light remote sensing was conducted at city-level scale due to the restriction of the coarser spatial resolutions. Residential areas and commercial areas are difficult to distinguish from each other on NTL remote sensing images
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