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Open AccessArticle

Mapping Urban Land Use by Using Landsat Images and Open Social Data

by Tengyun Hu 1, Jun Yang 1,2,*, Xuecao Li 1 and Peng Gong 1,2
1
Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
2
Joint Center for Global Change Studies, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yuhong He, Qihao Weng, Magaly Koch and Prasad S. Thenkabail
Remote Sens. 2016, 8(2), 151; https://doi.org/10.3390/rs8020151
Received: 23 October 2015 / Revised: 31 January 2016 / Accepted: 4 February 2016 / Published: 17 February 2016
High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government. View Full-Text
Keywords: urban land parcel; remote sensing; social data; land use urban land parcel; remote sensing; social data; land use
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MDPI and ACS Style

Hu, T.; Yang, J.; Li, X.; Gong, P. Mapping Urban Land Use by Using Landsat Images and Open Social Data. Remote Sens. 2016, 8, 151.

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