Publications

fsi books

Publications

Browse FSI scholarship on geopolitics, global health, energy, cybersecurity and more.

Featured Publications

Everything Counts: Building a Control Regime for Nonstrategic Nuclear Warheads in Europe

Building a Control Regime for Nonstrategic Nuclear Warheads in Europe

A new report led by Rose Gottemoeller on non-strategic nuclear warhead policies in Europe, particulary in light of Russia's changing status in the global nuclear community.
3D mockup cover of APARC's volume 'South Korea's Democracy in Crisis'

South Korea’s Democracy in Crisis

A close look at what is driving illiberalism and democratic delcine in today’s Korea, including political polarization, politicization of institutions, societal inequality, education, and social media.
System Error book cover and authors

System Error: Where Big Tech Went Wrong and How We Can Reboot

A forward-thinking manifesto which reveals how big tech’s obsession with optimization and efficiency has sacrificed fundamental human values.

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Stefano Ermon
Working Papers

Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery

Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
AAAI/ACM Conference , 2019 February 20, 2019

Millions of people worldwide are absent from their country’s census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and to studying any aspect of human life in these communities. Satellite imagery can provide sufficient information to build a population map without the cost and time of a government census.

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Journal Articles

Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans

Huaiyang Zhong, Xiaocheng Li, David Lobell, Stefano Ermon, Margaret Brandeau
Environment Systems and Decisions, 2018 June 21, 2018

Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced.

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Working Papers

Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data

Anna Wang, Caelin Tran, Nikhil Desai, David Lobell, Stefano Ermon
COMPASS '18 Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, 2018 June 20, 2018

Accurate prediction of crop yields in developing countries in advance of harvest time is central to preventing famine, improving food security, and sustainable development of agriculture. Existing techniques are expensive and difficult to scale as they require locally collected survey data. Approaches utilizing remotely sensed data, such as satellite imagery, potentially provide a cheap, equally effective alternative. Our work shows promising results in predicting soybean crop yields in Argentina using deep learning techniques.

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Working Papers

Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning

Reid Pryzant, Stefano Ermon, David Lobell
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017 August 24, 2017

Wheat is the most important Ethiopian crop, and rust one of its greatest antagonists. There is a need for cheap and scalable rust monitoring in the developing world, but existing methods employ costly data collection techniques. We introduce a scalable, accurate, and inexpensive method for tracking outbreaks with publicly available remote sensing data. Our approach improves existing techniques in two ways. First, we forgo the spectral features employed by the remote sensing community in favor of automatically learned features generated by Convolutional and Long Short-Term Memory Networks.

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Journal Articles

Combining satellite imagery and machine learning to predict poverty

Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, Stefano Ermon
Science, 2016 August 19, 2016

Policy-makers in the world's poorest countries are often forced to make decisions based on limited data. Consider Angola, which recently conducted its first postcolonial census. In the 44 years that elapsed between the prior census and the recent one, the country's population grew from 5.6 million to 24.3 million, and the country experienced a protracted civil war that displaced millions of citizens.

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