Fine extraction of urban villages in provincial capitals based on multivariate data

doi:10.6046/zrzyyg.2020368

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Abstract

Urban villages refer to the rural settlements where the homesteads are surrounded by cities after the farmland in the settlements is expropriated. Given the lack of data support and quantitative analyses in the current researches on urban villages, this study aims to extract the boundaries of urban villages using the deep learning tools in ENVI based on multiple spatial data such as high-resolution remote sensing images, building outlines, and points of interest (POI). The study area is the main urban area of Guangzhou City-the capital of Guangdong Province, for which the initial correct recognition rate of urban villages was 64.31%. To overcome the confusion between the urban villages and some old residential areas and industrial areas in the extraction results, high-resolution remote sensing images were further segmented using the road network to produce label data of urban villages. Then the outlines of the urban villages in the city were extracted using a support vector machine classifier based on machine learning classification algorithms, obtaining precision of up to 90.19%. Therefore, this study can serve as a reference for the reconstruction of urban villages and urban planning and design in the study area to some extent.

Keywords urban villages road network-based segmentation label raster deep learning support vector machine

ZTFLH:

TP79

Corresponding Authors: ZHANG Zhihua E-mail: 916678730@qq.com;43447077@qq.com

Issue Date: 24 September 2021

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	Dongdong FENG
	Zhihua ZHANG
	Haoyue SHI

Cite this article:

Dongdong FENG,Zhihua ZHANG,Haoyue SHI. Fine extraction of urban villages in provincial capitals based on multivariate data[J]. Remote Sensing for Natural Resources, 2021, 33(3): 272-278.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020368 OR https://www.gtzyyg.com/EN/Y2021/V33/I3/272

Fig.1 Overview of the research area

Fig.2 Experimental flow chart

Tab.1 Features of constructed land units

Fig.3 CAM class activation diagram

Fig.4 Extraction results from urban villages

Fig.5 Urban village vector after classification processing

Fig.6 Precision parameters of deep learning model

Fig.7 Plot unit vector pattern spot in the study area

Fig.8 Image segmentation is based on vector segmentation results

Tab.2 Select the classification accuracy of different feature attributes

Fig.9 Urban village identification results

Fig.10 Display the recognition results in Google Earth

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