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Open AccessArticle

Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data

1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101 China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
4
Department of Geography, The State University of New York, Buffalo, NY 14261, USA
5
School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 446; https://doi.org/10.3390/rs10030446
Received: 31 January 2018 / Revised: 28 February 2018 / Accepted: 6 March 2018 / Published: 12 March 2018
(This article belongs to the Special Issue Multisensor Data Fusion in Remote Sensing)
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. View Full-Text
Keywords: land use mapping; remote sensing imagery; mobile phone positioning data; decision fusion land use mapping; remote sensing imagery; mobile phone positioning data; decision fusion
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MDPI and ACS Style

Jia, Y.; Ge, Y.; Ling, F.; Guo, X.; Wang, J.; Wang, L.; Chen, Y.; Li, X. Urban Land Use Mapping by Combining Remote Sensing Imagery and Mobile Phone Positioning Data. Remote Sens. 2018, 10, 446.

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