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Articles 1 - 6 of 6
Full-Text Articles in Human Geography
Hybrid U-Net: Semantic Segmentation Of High-Resolution Satellite Images To Detect War Destruction, Shima Nabiee, Matthew Harding, Jonathan Hersh, Nader Bagherzadeh
Hybrid U-Net: Semantic Segmentation Of High-Resolution Satellite Images To Detect War Destruction, Shima Nabiee, Matthew Harding, Jonathan Hersh, Nader Bagherzadeh
Economics Faculty Articles and Research
Destruction caused by violent conflicts play a big role in understanding the dynamics and consequences of conflicts, which is now the focus of a large body of ongoing literature in economics and political science. However, existing data on conflict largely come from news or eyewitness reports, which makes it incomplete, potentially unreliable, and biased for ongoing conflicts. Using satellite images and deep learning techniques, we can automatically extract objective information on violent events. To automate this process, we created a dataset of high-resolution satellite images of Syria and manually annotated the destroyed areas pixel-wise. Then, we used this dataset to …
Monitoring War Destruction From Space Using Machine Learning, Hannes Mueller, Andre Groeger, Jonathan Hersh, Andrea Matranga, Joan Serrat
Monitoring War Destruction From Space Using Machine Learning, Hannes Mueller, Andre Groeger, Jonathan Hersh, Andrea Matranga, Joan Serrat
Economics Faculty Articles and Research
Satellite imagery is becoming ubiquitous. Research has demonstrated that artificial intelligence applied to satellite imagery holds promise for automated detection of war-related building destruction. While these results are promising, monitoring in real-world applications requires high precision, especially when destruction is sparse and detecting destroyed buildings is equivalent to looking for a needle in a haystack. We demonstrate that exploiting the persistent nature of building destruction can substantially improve the training of automated destruction monitoring. We also propose an additional machine-learning stage that leverages images of surrounding areas and multiple successive images of the same area, which further improves detection significantly. …
Poverty Mapping Using Convolutional Neural Networks Trained On High And Medium Resolution Satellite Images, With An Application In Mexico, Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz
Poverty Mapping Using Convolutional Neural Networks Trained On High And Medium Resolution Satellite Images, With An Application In Mexico, Boris Babenko, Jonathan Hersh, David Newhouse, Anusha Ramakrishnan, Tom Swartz
Economics Faculty Articles and Research
Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These “poverty maps” are key inputs for poverty targeting, public goods provision, political accountability, and impact evaluation, that are all the more important given the geographic dispersion of the remaining bottom billion severely poor individuals. In this paper we train Convolutional Neural Networks (CNNs) to estimate poverty directly from high and medium resolution satellite images. We use both Planet and Digital Globe imagery with spatial resolutions of 3-5 m2 and 50 cm2 respectively, covering all 2 million km2 of Mexico. Benchmark poverty estimates come from …
Building A Better Model: Variable Selection To Predict Poverty In Pakistan And Sri Lanka, Marium Afzal, Jonathan Hersh, David Newhouse
Building A Better Model: Variable Selection To Predict Poverty In Pakistan And Sri Lanka, Marium Afzal, Jonathan Hersh, David Newhouse
Economics Faculty Articles and Research
Numerous studies have developed models to predict poverty, but surprisingly few have rigorously examined different approaches to developing prediction models. This paper applies out of sample validation techniques to household data from Pakistan and Sri Lanka, to compare the accuracy of regional poverty predictions from models derived using manual selection, stepwise regression, and Lasso-based procedures. It also examines how much incorporating publically available satellite data into the model improves its accuracy. The five main findings are that: 1) Lasso tends to outperform both discretionary and stepwise models in Pakistan, where the set of potential predictors is large. 2) Lasso and …
Historical Health Conditions In Major Us Cities: The Hue Data Set, Carlos Villareal, Brian Bettenhausen, Eric Hanss, Jonathan Hersh
Historical Health Conditions In Major Us Cities: The Hue Data Set, Carlos Villareal, Brian Bettenhausen, Eric Hanss, Jonathan Hersh
Economics Faculty Articles and Research
The Historical Urban Ecological data set is a new resource detailing health and environmental conditions within seven major U.S. cities during the study period from 1830 to 1930. Researchers collected and digitized ward-level data from annual reports of municipal departments that detail the epidemiological, economic, and demographic conditions within each city. They then drafted new geographic information system data to link the tabular records to ward geographies. These data provide a new foundation to revisit questions surrounding the urban mortality transition and the growth of U.S. cities.
One State Or Two In Israel/Palestine: The Stress On Gender And Citizenship, Gordon Babst, Nicole M. Tellier
One State Or Two In Israel/Palestine: The Stress On Gender And Citizenship, Gordon Babst, Nicole M. Tellier
Political Science Faculty Articles and Research
As is the case with any of the three great Abrahamic religions, there is considerable ambiguity regarding the status and role of women both within doctrinal interpretations, and between religious and other cultural traditions in the community. These ambiguities are reflected in political practice and condition women's aspirations regarding what is possible for them to achieve. Nowhere is it more true that understandings of religious imperatives permeate politics and work to make other lines of division all the more intractable than in Israel/Palestine. The proclivity to violence between the two peoples not only victimizes women, but foreshortens attention to their …