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

FENG Dongdong， ZHANG Zhihua， SHI Haoyue，; ${author.authorNameEn}; ${author.authorNameEn}

doi:10.6046/zrzyyg.2020368

2021 Vol. 33, No. 3

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FENG Dongdong， ZHANG Zhihua， SHI Haoyue，, . 2021. Fine extraction of urban villages in provincial capitals based on multivariate data. Remote Sensing for Natural Resources, 33(3): 272-278. doi: 10.6046/zrzyyg.2020368

Citation:

FENG Dongdong， ZHANG Zhihua， SHI Haoyue，, . 2021. Fine extraction of urban villages in provincial capitals based on multivariate data. Remote Sensing for Natural Resources, 33(3): 272-278. doi: 10.6046/zrzyyg.2020368

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

1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China；
2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China；
3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China

Received Date: 23 November 2020

Publish Date: 15 September 2021

Abstract

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

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