Song Lei
School of Design, Shandong University of Arts, Jinan, 250300, China.
Heliyon. 2024 Jul 22;10(15):e35031. doi: 10.1016/j.heliyon.2024.e35031. eCollection 2024 Aug 15.
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, R = 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.
本研究首先讨论物联网（IoT）技术，并分析街道空间的四种类型分类以及绿色景观比例（GLR）。在此基础上，采用全卷积网络（FCN）-8s框架，基于FCN原理构建街景图像语义分割模型。随后，利用物联网技术分析T市历史城区街道空间中GLR的比例和满意度。研究结果表明，街景图像的GLR满意度得分与该区域的平均GLR之间存在显著正相关（显著性水平p < 0.05，R = 0.919）。在生活休闲、历史街道、交通区域和景观式街道这四种类型的街道空间中，GLR的不满意率分别为35%、33%、20%和18%，与不同的GLR满意度水平相关。为了加强街道空间绿化，针对“盲点”和“死角”提出了种植池和种植箱，从而完成这些区域的绿化。这些举措旨在完善街道绿化政策，整合街道功能区，并推进城市街道的科学绿化。对街道空间中GLR和满意度的分析为优化城市街道空间绿化工作提供了有价值的见解。
