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Conference Paper: Issues and challenges of remote sensing-based local climate zone mapping for high-density cities

TitleIssues and challenges of remote sensing-based local climate zone mapping for high-density cities
Authors
KeywordsLocal Climate Zones
WUDAPT
High-density
Issue Date2017
Citation
2017 Joint Urban Remote Sensing Event, JURSE 2017, 2017 How to Cite?
Abstract© 2017 IEEE. The local climate zone (LCZ) classification system provides a standard organizing principle for urban climate studies, such as urban heat island analysis, in which urban structures are classified into 17 standard LCZ classes according to urban land surface properties, such as land use and land coverage. Based on the standard definitions of the LCZ, freely available Landsat satellite data have been used to generate LCZ maps for cities worldwide. This research aims to evaluate the performance of the existing methods for high-density cities and to discuss the main issues, challenges, and possible solutions for further applications. Our experimental results indicate that three main factors, including training samples, input features, and the classifiers used, had a decisive effect on the final mapping result. In particular, low-quality training samples coupled with complex urban scenarios tended to yield low-quality LCZ mapping results. To improve the LCZ mapping results, some practical solutions and suggestions were given for future applications.
Persistent Identifierhttp://hdl.handle.net/10722/262745

 

DC FieldValueLanguage
dc.contributor.authorXu, Yong-
dc.contributor.authorRen, Chao-
dc.contributor.authorCai, Meng-
dc.contributor.authorWang, Ran-
dc.date.accessioned2018-10-08T02:46:55Z-
dc.date.available2018-10-08T02:46:55Z-
dc.date.issued2017-
dc.identifier.citation2017 Joint Urban Remote Sensing Event, JURSE 2017, 2017-
dc.identifier.urihttp://hdl.handle.net/10722/262745-
dc.description.abstract© 2017 IEEE. The local climate zone (LCZ) classification system provides a standard organizing principle for urban climate studies, such as urban heat island analysis, in which urban structures are classified into 17 standard LCZ classes according to urban land surface properties, such as land use and land coverage. Based on the standard definitions of the LCZ, freely available Landsat satellite data have been used to generate LCZ maps for cities worldwide. This research aims to evaluate the performance of the existing methods for high-density cities and to discuss the main issues, challenges, and possible solutions for further applications. Our experimental results indicate that three main factors, including training samples, input features, and the classifiers used, had a decisive effect on the final mapping result. In particular, low-quality training samples coupled with complex urban scenarios tended to yield low-quality LCZ mapping results. To improve the LCZ mapping results, some practical solutions and suggestions were given for future applications.-
dc.languageeng-
dc.relation.ispartof2017 Joint Urban Remote Sensing Event, JURSE 2017-
dc.subjectLocal Climate Zones-
dc.subjectWUDAPT-
dc.subjectHigh-density-
dc.titleIssues and challenges of remote sensing-based local climate zone mapping for high-density cities-
dc.typeConference_Paper-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/JURSE.2017.7924558-
dc.identifier.scopuseid_2-s2.0-85020164175-
dc.identifier.spagenull-
dc.identifier.epagenull-

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