The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 33–39, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-33-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 33–39, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-33-2018

  19 Sep 2018

19 Sep 2018

LAND USE CLASSIFICATION FROM COMBINED USE OF REMOTE SENSING AND SOCIAL SENSING DATA

A. S. Anugraha and H.-J. Chu A. S. Anugraha and H.-J. Chu
  • Dept. of Geomatics, National Cheng Kung University, Taiwan

Keywords: Human Behavior, Social Sensing, Remote Sensing, Decision Tree, Geographic Information System, Accuracy Assessment

Abstract. Large amounts of data can be sensed and analyzed to discover patterns of human behavior in cities for the benefit of urban authorities and citizens, especially in the areas of traffic forecasting, urban planning, and social science. In New York, USA, social sensing, remote sensing, and urban land use information support the discovery of patterns of human behavior. This research uses two types of openly accessible data, namely, social sensing data and remote sensing data. Bike and taxi data are examples of social sensing data, whereas sentinel remote sensed imagery is an example of remote sensing data. This research aims to sense and analyze the patterns of human behavior and to classify land use from the combination of remote sensing data and social sensing data. A decision tree is used for land use classification. Bike and taxi density maps are generated to show the locations of people around the city during the two peak times. On the basis of a geographic information system, the maps also reflect the residential and office areas in the city. The overall accuracy of land use classification after the consideration of social sensing data is 85.3%. The accuracy assessment shows that the combination of remote sensing data and social sensing data facilitates accurate urban land use classification.