Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning-Reference-Cited by-同舟云学术

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Granularity at Scale: Estimating Neighborhood Socioeconomic Indicators From High-Resolution Orthographic Imagery and Hybrid Learning

Published:2024 Issue: Volume:17 Page:5668-5679
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Brewer Ethan¹^ORCID,Valdrighi Giovani²^ORCID,Solunke Parikshit³^ORCID,Rulff Joao³^ORCID,Piadyk Yurii³^ORCID,Lv Zhonghui⁴^ORCID,Poco Jorge²^ORCID,Silva Claudio³^ORCID

Affiliation:

1. Spectral Sciences, Inc., Burlington, USA

2. Fundação Getulio Vargas, Rio de Janeiro, Brazil

3. New York University, New York, USA

4. William and Mary, Williamsburg, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10443032.pdf?arnumber=10443032

Reference72 articles.

1. Predicting socioeconomic indicators using transfer learning on imagery data: an application in Brazil

2. The sustainable development goals: Report

3. Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study

4. Contrasting and comparing sustainable development indicator metrics

5. Analyzing Links between Spatio-Temporal Metrics of Built-Up Areas and Socio-Economic Indicators on a Semi-Global Scale

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