Evaluation of blue green space quality in urban blocks based on EfficientNetV2 model land classification:A case study of the urban district of Handan City
As an important component of the urban built environment,blue-green space is of great significance for improving the living environment and strengthening urban ecological construction.In addition,increasing scientific evidence shows that blue-green space can provide a variety of benefits to promote the physical and mental health of residents.They can not only reduce the risk of chronic diseases such as cardiovascular and respiratory disease but also promote social interaction and regulate negative emotions.The existing studies evaluate the quality of blue-green space mainly by calculating the accessibility from streets,communities,and residential areas to large parks,but lack block scale indicator evaluation,or using street view image data to calculate the street green and blue visibility rates,without considering the green spaces and water bodies inside parks and residential areas,which can not reflect the blue-green space quality more comprehensively and truly.Or the normalized difference vegetation index and the normalized difference water index are calculated based on satellite remote sensing images,but these two types of indexes cannot clearly define the land cover type and water body ranges.In recent years,image classification technology based on deep convolutional neural networks has developed rapidly,which makes it possible to achieve rapid and accurate land interpretation for massive remote sensing satellite images in a large area.At present,most datasets used for land use scenario classification abroad are classified based on their land use characteristics,which cannot reflect the land classification status of urban built-up area in China well.The existing domestic land use classification data set,such as the EULUC-China dataset,divides urban built-up area land into industrial land,public management and public service land,commercial service facilities land and so on.However,it is difficult to apply to the refined assessment of urban built environment To better explore the problems in existing blue-green space planning and optimize the distribution pattern of blue-green space,this paper designs a technical framework for evaluating the quality of blue-green space in urban blocks based on image classification technology in the field of deep learning.First,it selectd satellite remote sensing images to build an urban land use classification dataset.Then,it uses the EfficientNetV2 network model to quickly and accurately classify and infer land use from satellite remote sensing images of the main urban area of Handan City,extracting the location distribution information of the blue-green space within the research scope.Using the neighborhood as a research unit,it measures the quality of blue-green space based on availability and accessibility.The calculation of blue space quality is mainly achieved by creating 64 m x 64 m fishing nets in ArcGIS,using the Amap API to obtain the shortest walking distance from the center point of each grid in the block to the surrounding water body,and finally,the average blue space accessibility value of each block is summarized and counted.The calculation of green space quality mainly involves calculating the average green space area ratio of all grids in the block.On this basis,these two types of indicators are normalized and weighted to obtain the comprehensive evaluation results of blue-green space quality.Using univariate spatial autocorrelation analysis to explore the differentiation characteristics of blue-green space quality in the spatial pattern of blocks,and identifying blocks with poor blue-green space quality.The research results show that:1)the distribution of blue space accessibility decreases from inside to outside,and the blocks in Fuxing District have relatively lower blue space accessibility.2)The distribution of green space is generally uneven,and blocks with a relatively low proportion of green space area are mainly concentrated in the south of Fuxing District and Hanshan District.3)There are 59 blocks with poor quality of blue-green space mainly distributed in the north of Fuxing District,Congtai District,and Hanshan District.Finally,potential grids that can improve the quality of blue-green space are found in the low-low and low-high clustering blocks.Based on the above research results and these potential grids,a series of improvement strategies are proposed,including reasonable increment,optimization of inventory,overall planning and system construction.The satellite remote sensing image data used in the study is open source,free of charge,and easy to obtain,without the need for professional calibration and preprocessing operations,making it highly practical for urban built environment evaluation.The EfficientNetV2 model has good accuracy in urban land classification.The refined land classification results can more comprehensively analyze the blue-green space quality at the block scale,helping scientifically select blocks that need priority renewal.Compared to existing studies that only consider the accessibility of park green space or street green visibility,this article evaluates the quality of green spaces within parks,residential areas,and streets in a large-scale research area.Based on green spaces,the quality of blue spaces is also analyzed and discussed.The research framework of this article provides a new approach and method for quantitative analysis of urban built environment.
blue-green spaceland classificationEfficientNetV2blue space accessibilitythe proportion of green space area
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