Fine extraction of urban villages in provincial capitals based on multivariate data
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摘要: 城中村是指农村耕地被收走后,剩余宅基地被城市包围的农村聚落。针对当前城中村的研究缺少数据支撑和定量分析等问题,基于高分辨率遥感影像、建筑物轮廓及兴趣点(point of interest,POI)等多元空间数据,以广东省省会广州市的主城区为研究区域,利用ENVI中深度学习工具提取城中村边界,其城中村正确识别率为64.31%。对于提取结果中存在与部分老旧居民区、工业区混淆的现象,进一步使用路网分割高分辨率遥感影像,制作城中村标签数据。结合机器学习分类方法,使用支持向量机分类器提取城中村轮廓。该方法提取的精度可达到90.19%,对于研究区内城中村改造、城市规划设计等具有一定的参考意义。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.
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Key words:
- urban villages /
- road network-based segmentation /
- label raster /
- deep learning /
- support vector machine /
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