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Article: Regional mapping of essential urban land use categories in China: A segmentation-based approach

TitleRegional mapping of essential urban land use categories in China: A segmentation-based approach
Authors
KeywordsSegmentation
Urban land use
Ningbo
Machine learning
Issue Date2020
Citation
Remote Sensing, 2020, v. 12, n. 7, article no. 1058 How to Cite?
AbstractUnderstanding 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.
Persistent Identifierhttp://hdl.handle.net/10722/299625

 

DC FieldValueLanguage
dc.contributor.authorTu, Ying-
dc.contributor.authorChen, Bin-
dc.contributor.authorZhang, Tao-
dc.contributor.authorXu, Bing-
dc.date.accessioned2021-05-21T03:34:49Z-
dc.date.available2021-05-21T03:34:49Z-
dc.date.issued2020-
dc.identifier.citationRemote Sensing, 2020, v. 12, n. 7, article no. 1058-
dc.identifier.urihttp://hdl.handle.net/10722/299625-
dc.description.abstractUnderstanding 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.-
dc.languageeng-
dc.relation.ispartofRemote Sensing-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectSegmentation-
dc.subjectUrban land use-
dc.subjectNingbo-
dc.subjectMachine learning-
dc.titleRegional mapping of essential urban land use categories in China: A segmentation-based approach-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/rs12071058-
dc.identifier.scopuseid_2-s2.0-85084266227-
dc.identifier.volume12-
dc.identifier.issue7-
dc.identifier.spagearticle no. 1058-
dc.identifier.epagearticle no. 1058-
dc.identifier.eissn2072-4292-

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