A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance-Reference-Cited by-同舟云学术

A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance

Published:2024-02 Issue:4 Volume:38 Page:726-750
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Runfola Dan¹²,Stefanidis Anthony²³,Lv Zhonghui¹⁴,O’Brien Joseph²,Baier Heather¹²

Affiliation:

1. Department of Applied Science, William & Mary, Williamsburg, VA, USA

2. Data Science Program, William & Mary, Williamsburg, VA, USA

3. Department of Computer Science, William & Mary, Williamsburg, VA, USA

4. Virginia Institute of Marine Science, William & Mary, Gloucester, VA, USA

Funder

Department of Homeland Security Center

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2024.2305636

Reference71 articles.

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4. Indigenous land rights and deforestation: Evidence from the Brazilian Amazon

5. Predicting road quality using high resolution satellite imagery: A transfer learning approach

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