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Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach

by Ying Tu 1, Bin Chen 2, Tao Zhang 1 and Bing Xu 1,3,4,*
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA
Tsinghua Urban Institute, Tsinghua University, Beijing 100084, China
Center for Healthy Cities, Institute for China Sustainable Urbanization, Tsinghua University, Beijing 100084, China
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1058;
Received: 2 March 2020 / Revised: 23 March 2020 / Accepted: 24 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Urban Land Use Mapping and Analysis in the Big Data Era)
Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future. View Full-Text
Keywords: urban land use; segmentation; machine learning; Ningbo urban land use; segmentation; machine learning; Ningbo
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MDPI and ACS Style

Tu, Y.; Chen, B.; Zhang, T.; Xu, B. Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach. Remote Sens. 2020, 12, 1058.

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