%0 Journal Article
%T 基于深度学习和街景图像的城市绿化评价系统开发
Development of an Urban Greenery Evaluation System Based on Deep Learning and Street View Images
%A 夏一熙
%A 李果
%J Geographical Science Research
%P 1080-1087
%@ 2168-5770
%D 2024
%I Hans Publishing
%R 10.12677/gser.2024.136104
%X 基于城市空间环境品质的提升，本研究深入探讨了基于街景地图和人工智能深度学习技术，从人的视角测量城市街道空间绿化率的方法和评估模式。通过对城市街道空间街景图像进行收集，利用深度学习语义分割技术对街景图像中的植物进行识别，构建自动化的城市街道空间绿视率计算模型。同时，构建了城市街道空间行人视角绿化空间品质的可视化评估模型。本研究力图通过科学规划与设计促进城市街道空间绿化品质的提升，旨在为城市品质的提升、居民生活福祉的增进，以及公园城市可持续发展目标的推进，贡献一份力量与参考。
Based on the improvement of urban spatial environmental quality, this study deeply explores the methods and evaluation models for measuring the greening rate of urban street space from a human perspective based on street view maps and artificial intelligence deep learning technology. By collecting urban streetscape images, we utilize deep learning semantic segmentation technology to identify plants within these images, thereby constructing an automated model for calculating the green visibility rate of urban street spaces. At the same time, an evaluation model for the quality of green space from the perspective of pedestrians in urban street space was constructed. This study explores how to promote the improvement of the greening quality of urban street space through scientific planning and design, aiming to contribute to the improvement of urban quality, the enhancement of residents’ well-being, and the promotion of the sustainable development goals of park cities.
%K 绿视率，
%K 深度学习，
%K 街景图像，
%K 图像分割
Green View Index
%K Deep Learning
%K Google Street View
%K Segmentation
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=104182