SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images

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成果类型：

期刊论文

作者：

Wu, Hao;Luo, Wenting;Lin, Anqi;Hao, Fanghua;Olteanu-Raimond, Ana-Maria;...

通讯作者：

Lin, Anqi(linanqi@mails.ccnu.edu.cn)

作者机构：

[Hao, Fanghua; Wu, Hao; Li, Yan; Liu, Lanfa; Luo, Wenting; Lin, Anqi] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, 152 Luoyu Rd, Wuhan, Peoples R China.

[Hao, Fanghua; Wu, Hao; Li, Yan; Liu, Lanfa; Luo, Wenting; Lin, Anqi] Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan, Peoples R China.

[Olteanu-Raimond, Ana-Maria] Univ Gustave Eiffel, LASTIG, ENSG, IGN, St Mande, France.

[Lin, Anqi] Cent China Normal Univ, Room 318,10 Bldg,152 Luoyu Rd, Wuhan, Peoples R China.

通讯机构：

[Anqi Lin] H

Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, 152 Luoyu Rd, Wuhan, PR China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, 152 Luoyu Rd, Wuhan, PR China

语种：

英文

关键词：

Ensemble learning;SALT features;Urban functional zone mapping;Volunteered geographic information

期刊：

Computers, Environment and Urban Systems

ISSN：

0198-9715

年：

2023

卷：

100

页码：

101921

DOI：

10.1016/j.compenvurbsys.2022.101921

基金类别：

This work was supported by the National Natural Science Foundation of China [ 42201468 , 42071358 ], Postdoctoral Innovation Talents Support Program of China [ BX20220128 ], China Postdoctoral Science Foundation [ 2022M721283 ] and Fundamental Research Funds for the Central Universities [ CCNU22QN018 ].

机构署名：

本校为第一机构

院系归属：

城市与环境科学学院

摘要：

Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building ...

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SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images

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