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• W4280639117 abstract "This paper analyzes and studies the structure and parameters of the VGGNet network model and selects the most commonly used and efficient VGG-16 as the prototype of the improved model. A multiscale sampling layer is added at the end of the VGG-16 convolution part so that the model can input images of any size for training and testing while reducing the number of neurons in the fully connected layer. This improves the training speed of the model under the premise of ensuring the accuracy. This paper uses multisource street spatial data combined with geographic information spatial analysis technology to measure and evaluate the spatial quality of streets in the main urban area. From the three dimensions of vitality, safety, and greenness of urban street space quality, a systematic structure for evaluation and analysis of street space quality is constructed. Street vitality includes eight index factors: entrance and exit density, street furniture density, street sketch density, street characteristic landscape density, POI density, POI diversity, commercial POI ratio, and street population density. There are five index factors: degree, roadside parking occupancy ratio, traffic signal system density, sidewalk width proportion, and street facility density. We use ArcGIS to build an index factor information database for statistical analysis and visualization. According to the natural discontinuous point classification method, the safety level of urban street public space is divided into five grades. The sample size of the first four grades has a small fluctuation range. The sample sizes are 153, 172, 153, and 158, respectively, accounting for 21%, 23%, 21%, and 21% of the total street samples, of which the first two grades occupy a total of 44%, so 44% of the streets in the main urban area have a low-quality level of street space. Level 5 has a sample of 102 streets, accounting for 14%, with an average street space quality value of 0.43." @default.
• W4280639117 created "2022-05-22" @default.
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• W4280639117 date "2022-05-11" @default.
• W4280639117 modified "2023-09-25" @default.
• W4280639117 title "Prediction of Urban Street Public Space Art Design Indicators Based on Deep Convolutional Neural Network" @default.
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• W4280639117 doi "https://doi.org/10.1155/2022/5508623" @default.
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