Abstract
Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions.
Highlights
Urban areas account for 0.5% of the planet’s surface but accommodate more than half the global population [1]
Medium-spatial-resolution remote sensing imagery (RSI) is primarily useful for mapping land cover, and not well suited for distinguishing land uses. This is because the reflectances of several land use classes are very similar, which means they can often be categorized as a single class in a land cover map
Liu et al combined spectral, textural, and spatial features calculated from high-resolution RSI and social features derived from mobile phone positioning data (MPPD) to identify land use types of different scenes
Summary
Urban areas account for 0.5% of the planet’s surface but accommodate more than half the global population [1]. Zhao et al used the scene classification method to construct a bag-of-visual-words representation according to multiscale spatial features characterized by a convolutional neural network, which they applied to classification of the University of California Merced Land Use Dataset [22] These spectral-spatial approaches applied to high-spatial-resolution RSI have the potential to distinguish different urban land use types, some limitations remain. Liu et al combined spectral, textural, and spatial features calculated from high-resolution RSI and social features derived from mobile phone positioning data (MPPD) to identify land use types of different scenes. This paper proposes an urban land use mapping method that combines medium-spatial-resolution RSI and mobile phone positioning data (MPPD) to extract urban land use information.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
