Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning

Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ying Tu, Bin Chen, Wei Lang, Tingting Chen, Miao Li, Tao Zhang, Bing Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	remote sensing land use classification ensemble learning mixed land use urban planning Science Q
Acceso en línea:	https://doaj.org/article/3c6a17472d6c4aadbbd865e0b2743c23
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3c6a17472d6c4aadbbd865e0b2743c23
record_format	dspace
spelling	oai:doaj.org-article:3c6a17472d6c4aadbbd865e0b2743c232021-11-11T18:51:04ZUncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning10.3390/rs132142412072-4292https://doaj.org/article/3c6a17472d6c4aadbbd865e0b2743c232021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4241https://doaj.org/toc/2072-4292Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users’ demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies.Ying TuBin ChenWei LangTingting ChenMiao LiTao ZhangBing XuMDPI AGarticleremote sensingland use classificationensemble learningmixed land useurban planningScienceQENRemote Sensing, Vol 13, Iss 4241, p 4241 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	remote sensing land use classification ensemble learning mixed land use urban planning Science Q
spellingShingle	remote sensing land use classification ensemble learning mixed land use urban planning Science Q Ying Tu Bin Chen Wei Lang Tingting Chen Miao Li Tao Zhang Bing Xu Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning
description	Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users’ demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies.
format	article
author	Ying Tu Bin Chen Wei Lang Tingting Chen Miao Li Tao Zhang Bing Xu
author_facet	Ying Tu Bin Chen Wei Lang Tingting Chen Miao Li Tao Zhang Bing Xu
author_sort	Ying Tu
title	Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning
title_short	Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning
title_full	Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning
title_fullStr	Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning
title_full_unstemmed	Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning
title_sort	uncovering the nature of urban land use composition using multi-source open big data with ensemble learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3c6a17472d6c4aadbbd865e0b2743c23
work_keys_str_mv	AT yingtu uncoveringthenatureofurbanlandusecompositionusingmultisourceopenbigdatawithensemblelearning AT binchen uncoveringthenatureofurbanlandusecompositionusingmultisourceopenbigdatawithensemblelearning AT weilang uncoveringthenatureofurbanlandusecompositionusingmultisourceopenbigdatawithensemblelearning AT tingtingchen uncoveringthenatureofurbanlandusecompositionusingmultisourceopenbigdatawithensemblelearning AT miaoli uncoveringthenatureofurbanlandusecompositionusingmultisourceopenbigdatawithensemblelearning AT taozhang uncoveringthenatureofurbanlandusecompositionusingmultisourceopenbigdatawithensemblelearning AT bingxu uncoveringthenatureofurbanlandusecompositionusingmultisourceopenbigdatawithensemblelearning
_version_	1718431696848683008

Uncovering the Nature of Urban Land Use Composition Using Multi-Source Open Big Data with Ensemble Learning

Ejemplares similares