Analyzing Green View Index and Green View Index best path using Google Street View and deep learning
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Abstract (arXiv)
As an important part of urban landscape research, analyzing and studying street-level greenery can increase the understanding of a city's greenery, contributing to better urban living environment planning and design. Planning the best path of urban greenery is a means to effectively maximize the use of urban greenery, which plays a positive role in the physical and mental health of urban residents and the path planning of visitors. In this paper, we used Google Street View (GSV) to obtain street view images of Osaka City. The semantic segmentation model is adopted to segment the street view images and analyze the Green View Index (GVI) of Osaka City. Based on the GVI, we take advantage of the adjacency matrix and Floyd-Warshall Algorithm to calculate Green View Index best path, solving the limitations of ArcGIS software. Our analysis not only allows the calculation of specific routes for the GVI best paths but also realizes the visualization and integration of neighborhood urban greenery. By summarizing all the data, we can conduct an intuitive feeling and objective analysis of the street-level greenery in the research area. Based on this, such as urban residents and visitors can maximize the available natural resources for a better life. The dataset and code are available at https://github.com/Jackieam/GVI-Best-Path.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 26 Apr 2021.
Builds on
15 of 53 references.
References · 15
- 2017Attention Is All You Needcited by 59,932
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
- 2016Pyramid Scene Parsing Networkcited by 2,472
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
- 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
- 2019Segmentation Transformer: Object-Contextual Representations for Semantic Segmentationcited by 573
- 2016An Analysis of Deep Neural Network Models for Practical Applicationscited by 114
- 2016OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networkscited by 109
- 2018Building Instance Classification Using Street View Imagescited by 33
- 2017Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challengescited by 24
3 more
- 2020Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentationcited by 14
- 2017Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancercited by 10
- 2020Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetationcited by 2
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Citing papers per year: 2024: 1.
1 paper.