An investigation of the visual features of urban street vitality using a convolutional neural network-Reference-Cited by-同舟云学术

An investigation of the visual features of urban street vitality using a convolutional neural network

Published:2020-10-01 Issue:4 Volume:23 Page:341-351
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Qi Yi¹²,Chodron Drolma Sonam¹,Zhang Xiang¹,Liang Jing³,Jiang Haibing⁴,Xu Jiangang¹,Ni Tianhua⁵

Affiliation:

1. School of Architecture and Urban Planning, Nanjing University, Nanjing, China

2. Lancaster Environment Centre, Lancaster University, Lancaster, UK

3. Department of Planning and Landscape Design, Nanjing Branch, Huachengboyuan Engineering Technology Group, Nanjing, China

4. School of Urban and Planning, Yancheng Teachers University, Yancheng, China

5. School of Geography and Ocean Science, Nanjing University, Nanjing, China

Funder

China Scholarship Council

National Natural Science Foundation of China

Social Science Foundation of Jiangsu Province, China

Publisher

Informa UK Limited

Subject

Computers in Earth Sciences,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2020.1847002

Reference30 articles.

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2. A high-precision heuristic model to detect home and work locations from smart card data

3. Open Space Evaluation Methodology and Three Dimensional Evaluation Model as a Base for Sustainable Development Tracking

4. Chollet, F., and others. 2015. “Keras.” https://keras.io

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