Does Deep Learning Enhance the Estimation for Spatially Explicit Built Environment Stocks through Nighttime Light Data Set? Evidence from Japanese Metropolitans-Reference-Cited by-同舟云学术

Does Deep Learning Enhance the Estimation for Spatially Explicit Built Environment Stocks through Nighttime Light Data Set? Evidence from Japanese Metropolitans

Published:2023-02-21 Issue:9 Volume:57 Page:3971-3979
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Liu Zhiwei¹^ORCID,Saito Ryusei¹,Guo Jing¹^ORCID,Hirai Chizuko²,Haga Chihiro³,Matsui Takanori³,Shirakawa Hiroaki¹,Tanikawa Hiroki¹

Affiliation:

1. Graduate School of Environmental Studies, Nagoya University, Nagoya 464-8601, Japan

2. Plus GIS, Wakayama 640-8157, Japan

3. Graduate School of Engineering, Osaka University, Suita, Osaka 565-0871, Japan

Funder

Ministry of Education, Culture, Sports, Science and Technology

Ministry of the Environment

Japan Society for the Promotion of Science

Environmental Restoration and Conservation Agency

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.2c08468

Reference69 articles.

1. Taking Stock of Built Environment Stock Studies: Progress and Prospects

2. High-Resolution Mapping of the Urban Built Environment Stocks in Beijing

3. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes

4. The impact of urbanization and the urban heat island effect on day to day temperature variation

5. Material efficiency strategies to reducing greenhouse gas emissions associated with buildings, vehicles, and electronics—a review

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