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Identification and Classification of Urban PLES Spatial Functions Based on Multisource Data and Machine Learning

文献类型：期刊论文

作者Fu, Jingying1,3; Bu, Ziqiang1,3; Jiang, Dong1,2,3; Lin, Gang1,3
刊名LAND
出版日期2022-10-01
卷号11期号:10页码:17
关键词multisource data machine learning PLES random forest
DOI10.3390/land11101824
通讯作者Lin, Gang(ling@lreis.ac.cn)
英文摘要Production space, living space, and ecological space (PLES) increasingly restrict and influence each other, and the urban PLES conflict significantly affects the sustainable development of a city. This study extracts multi-dimensional features from high-resolution remote sensing images, building vectors, points of interest (POI), and nighttime lighting data, and applies them to urban PLES feature recognition, dividing Ningbo into an agricultural production space, industrial and commercial production space, public living space, resident living space and ecological space. The specific research work was as follows: first, a convolutional neural network (CNN) was used to extract high-rise scene information from high-resolution remote sensing images; at the same time, through the geostatistical method, the building vector features, POI features, and night light features were extracted to express the economic and social characteristics of a city. Then, we used the nearest neighbor algorithm, decision-making tree algorithm, and random forest algorithm to train individual and combined features. Finally, random forest, which had the best training effect, was selected as the classifier in the fusion stage; as a result, the prediction accuracy rate reached 90.79%. The experimental results showed that the recognition model, based on multisource data and machine learning, had a good classification effect. Finally, we analyzed the current situation of the spatial distribution of PLES in Ningbo.
WOS关键词SOCIAL MEDIA DATA ; LAND-COVER ; COMPLETION ; SPACE
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19040305] ; National Natural Science Foundation of China[41971250] ; Youth Innovation Promotion Association[2018068] ; State Key Laboratory of Resources and Environmental Information System and Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[E0V00112YZ]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000875065100001
出版者MDPI
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Youth Innovation Promotion Association ; State Key Laboratory of Resources and Environmental Information System and Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/186116]  
专题中国科学院地理科学与资源研究所
通讯作者Lin, Gang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Minist Nat Resources, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Fu, Jingying,Bu, Ziqiang,Jiang, Dong,et al. Identification and Classification of Urban PLES Spatial Functions Based on Multisource Data and Machine Learning[J]. LAND,2022,11(10):17.
APA Fu, Jingying,Bu, Ziqiang,Jiang, Dong,&Lin, Gang.(2022).Identification and Classification of Urban PLES Spatial Functions Based on Multisource Data and Machine Learning.LAND,11(10),17.
MLA Fu, Jingying,et al."Identification and Classification of Urban PLES Spatial Functions Based on Multisource Data and Machine Learning".LAND 11.10(2022):17.

入库方式： OAI收割

来源：地理科学与资源研究所

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