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

Jingying Fu, Ziqiang Bu, Dong Jiang and Gang Lin ()
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Jingying Fu: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Ziqiang Bu: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Dong Jiang: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Gang Lin: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Land, 2022, vol. 11, issue 10, 1-17

Abstract: 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.

Keywords: multisource data; machine learning; PLES; random forest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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