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George Azzari

Biographic Data

ID6929486
NAMEGeorge Azzari
GIVEN NAMESGeorge
FAMILY NAMEAzzari
SIGNATUREAZZARI G
AFFILIATIONSStanford University
ORCID0000-0002-8792-7508
VERIFIEDYes
TOTAL WORKS4
TOTAL CITATIONS2
WORKS AS AUTHOR4
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2017
LATEST PUBLICATION YEAR2020
H-INDEX1
  • Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

    Open Access•Christopher Yeh, Anthony Pérez et al.•ARTICLE•Nature Communications•2020

    Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral satellite imagery. Models can explain 70% of the variation in ground-measured village wealth in co…

  • The impact of agricultural interventions can be doubled by using satellite data

    Open Access•Meha Jain, Balwinder Singh et al.•ARTICLE•Nature Sustainability•2019•Cited by: 2•References: 1

  • Satellite detection of cover crops and their effects on crop yield in the Midwestern United States

    Open Access•Christopher Seifert, George Azzari et al.•ARTICLE•Environmental Research Letters•2018

    The practice of planting winter cover crops has seen renewed interest as a solution to environmental issues with the modern maize- and soybean-dominated row crop production system of the US Midwest. We examine whether cover cropping patterns can be assessed at scale using publicly available satellite data, creating a classifier with 91.5% accuracy (.68 kappa). We then use this classifier to examine spatial and temporal trends in cover crop occurr…

  • Satellite detection of rising maize yield heterogeneity in the U.S. Midwest

    Open Access•David B Lobell, George Azzari•ARTICLE•Environmental Research Letters•2017

    The future trajectory of crop yields in the United States will influence food supply and land use worldwide. We examine maize and soybean yields for 2000–2015 in the Midwestern U.S. using a new satellite-based dataset on crop yields at 30m resolution. We quantify heterogeneity both within and between fields, and find that the difference between average and top yielding fields is typically below 30% for both maize and soybean, as expected in advan…

  • The impact of agricultural interventions can be doubled by using satellite data

    Open Access•Meha Jain, Balwinder Singh et al.•ARTICLE•Nature Sustainability•2019•Cited by: 2•References: 1

  • Satellite detection of rising maize yield heterogeneity in the U.S. Midwest

    Open Access•David B Lobell, George Azzari•ARTICLE•Environmental Research Letters•2017

    The future trajectory of crop yields in the United States will influence food supply and land use worldwide. We examine maize and soybean yields for 2000–2015 in the Midwestern U.S. using a new satellite-based dataset on crop yields at 30m resolution. We quantify heterogeneity both within and between fields, and find that the difference between average and top yielding fields is typically below 30% for both maize and soybean, as expected in advan…

  • Satellite detection of cover crops and their effects on crop yield in the Midwestern United States

    Open Access•Christopher Seifert, George Azzari et al.•ARTICLE•Environmental Research Letters•2018

    The practice of planting winter cover crops has seen renewed interest as a solution to environmental issues with the modern maize- and soybean-dominated row crop production system of the US Midwest. We examine whether cover cropping patterns can be assessed at scale using publicly available satellite data, creating a classifier with 91.5% accuracy (.68 kappa). We then use this classifier to examine spatial and temporal trends in cover crop occurr…

  • The impact of agricultural interventions can be doubled by using satellite data

    Open Access•Meha Jain, Balwinder Singh et al.•ARTICLE•Nature Sustainability•2019•Cited by: 2•References: 1

  • Using publicly available satellite imagery and deep learning to understand economic well-being in Africa

    Open Access•Christopher Yeh, Anthony Pérez et al.•ARTICLE•Nature Communications•2020

    Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral satellite imagery. Models can explain 70% of the variation in ground-measured village wealth in co…

Geography (4 works) · Agriculture (3 works) · Climate change impacts on agriculture (3 works) · Environmental Science (3 works) · Satellite (3 works) · Agronomy (2 works) · Biology (2 works) · Computer Science (2 works) · Crop (2 works) · Crop yield (2 works)

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