The street space planning and design of artificial intelligence-assisted deep learning neural network in the Internet of Things.

ABSTRACT

This study begins by discussing Internet of Things (IoT) technology and analyzing the classification of street space into four types, along with the Green Looking Ratio (GLR). Following this, the Fully Convolutional Network (FCN)-8s framework is employed to construct a street view image semantic segmentation model based on FCN principles. Subsequently, IoT technology is utilized to analyze the proportion of GLR and satisfaction in the street space within the historical urban area of T City. The findings reveal a significant positive correlation (significance level p < 0.05, R2 = 0.919) between the GLR satisfaction score of street view images and the average GLR of the area. Among the four types of street space-life leisure, historical streets, traffic areas, and landscape-style streets-the dissatisfaction rates with GLR are 35 %, 33 %, 20 %, and 18 %, respectively, correlating with varying GLR satisfaction levels. To enhance street space greening, planting ponds and boxes are proposed for "blind spots" and "dead corners," thereby completing greenery in these areas. These initiatives aim to improve street greening policies, integrate street function zones, and advance the scientific greening of urban streets. The analysis of GLR and satisfaction in street spaces provides valuable insights for refining urban street space greening efforts.