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Open AccessArticle

Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning

by 1,2, 1,2,*, 3, 1, 1,2 and 1,2
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Science, Chang’an University, Xi’an 710064, China
*
Author to whom correspondence should be addressed.
Academic Editor: Saeid Homayouni
Remote Sens. 2021, 13(3), 373; https://doi.org/10.3390/rs13030373
Received: 13 December 2020 / Revised: 14 January 2021 / Accepted: 20 January 2021 / Published: 21 January 2021
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Urban functional zones are important space carriers for urban economic and social function. The accurate and rapid identification of urban functional zones is of great significance to urban planning and resource allocation. However, the factors considered in the existing functional zone identification methods are not comprehensive enough, and the recognition of functional zones stops at their categories. This paper proposes a framework that combines multisource heterogeneous data to identify the categories of functional zones and draw the portraits of functional zones. The framework comprehensively describes the features of functional zones from four aspects: building-level metrics, landscape metrics, semantic metrics, and human activity metrics, and uses a combination of ensemble learning and active learning to balance the identification accuracy of functional zones and the labeling cost during large-scale generalization. Furthermore, sentiment analysis, word cloud analysis, and land cover proportion maps are added to the portraits of typical functional zones to make the image of functional zones vivid. The experiment carried out within the Fifth Ring Road, Haidian District, Beijing, shows that the overall accuracy of the method reached 82.37% and the portraits of the four typical functional zones are clear. The method in this paper has good repeatability and generalization, which is helpful to carry out quantitative and objective research on urban functional zones. View Full-Text
Keywords: urban functional zone; ensemble learning; active learning; functional portrait urban functional zone; ensemble learning; active learning; functional portrait
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MDPI and ACS Style

Xu, N.; Luo, J.; Wu, T.; Dong, W.; Liu, W.; Zhou, N. Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning. Remote Sens. 2021, 13, 373. https://doi.org/10.3390/rs13030373

AMA Style

Xu N, Luo J, Wu T, Dong W, Liu W, Zhou N. Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning. Remote Sensing. 2021; 13(3):373. https://doi.org/10.3390/rs13030373

Chicago/Turabian Style

Xu, Nan; Luo, Jiancheng; Wu, Tianjun; Dong, Wen; Liu, Wei; Zhou, Nan. 2021. "Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning" Remote Sens. 13, no. 3: 373. https://doi.org/10.3390/rs13030373

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