A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Dense and Low-rise Residential Areas Detection by Shadow Data Mining in Urban High-resolution Images
Low density area is one of the main targets of reconstruction in the process of urbanization, and it is also the frequent change areas in building change detection. However, dense and low-rise residential areas are often without obvious shadow features due to over-dense spatial distribution, low height and tree shelter, which further leads to missed and difficult inspection of houses within these areas. Therefore, it is necessary to extract dense and low-rise residential areas for better understanding of urban development. At present, there are few studies on detection of low-density dense areas. This work proposed a rapid detection method for low-density dense building areas in high-resolution images of cities using mining shadow information. The method proposed first preserves small “noise points” after shadow detection to exclude large areas of shadows, then performs morphological processing on the remaining “noise points” of shadow, and finally uses the spatial distribution characteristics of shadow to extract the area with large area as low and dense residential areas. This work selected different urban scenes at home and abroad or experiments. And the results show that the proposed method can easily and quickly detect dense and low-rise building areas
Dense and Low-rise Residential Areas Detection by Shadow Data Mining in Urban High-resolution Images
Low density area is one of the main targets of reconstruction in the process of urbanization, and it is also the frequent change areas in building change detection. However, dense and low-rise residential areas are often without obvious shadow features due to over-dense spatial distribution, low height and tree shelter, which further leads to missed and difficult inspection of houses within these areas. Therefore, it is necessary to extract dense and low-rise residential areas for better understanding of urban development. At present, there are few studies on detection of low-density dense areas. This work proposed a rapid detection method for low-density dense building areas in high-resolution images of cities using mining shadow information. The method proposed first preserves small “noise points” after shadow detection to exclude large areas of shadows, then performs morphological processing on the remaining “noise points” of shadow, and finally uses the spatial distribution characteristics of shadow to extract the area with large area as low and dense residential areas. This work selected different urban scenes at home and abroad or experiments. And the results show that the proposed method can easily and quickly detect dense and low-rise building areas
Dense and Low-rise Residential Areas Detection by Shadow Data Mining in Urban High-resolution Images
Zhang, Hongya (author) / Xu, Wentao (author) / Ren, Hongyu (author) / Dong, Linyao (author) / Fan, Zhongjie (author)
2021-06-01
3406248 byte
Conference paper
Electronic Resource
English
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