Mapping Poverty with Satellite Imagery
Abstract/Contents
- Abstract
Poverty eradication is the first of 17 Sustainable Development Goals set by the United Nations to reach by 2030. Measuring poverty is an important step to alleviating poverty, as it helps to inform research studies, target aid efforts, guide policy decisions, and generally monitor the progress of such an initiative. In the developing world, where the need for poverty data is the most pressing, poverty data is particularly scarce due to the resource cost associated with conducting surveys.
This data gap is one of the crucial challenges to overcome in order to alleviate poverty. This thesis aims to cover a variety of methods towards closing this poverty data gap using remote sensing and satellite imagery data. We introduce a high-resolution poverty mapping method using only publicly available satellite data. The approach utilizes transfer learning to leverage knowledge from data-rich sources and combat the data gap. We also attempt to reduce the data gap by incorporating additional data, detailed in preliminary work using multiple resolutions of satellite imagery to improve predictive performance. We develop a semi-supervised method which narrows the data gap by utilizing abundant unlabeled satellite imagery. We show that can use this method to also take advantage of spatial correlations of poverty measures to improve our model predictions.
Experiments are conducted on a variety of real-world datasets, as well as poverty measure prediction problems for 5 African countries - Malawi, Tanzania, Uganda, Nigeria, and Rwanda - demonstrating that our methods based on only publicly available data can approach the predictive performance of surveys conducted in the field and potentially transform efforts to track and alleviate poverty. Finally, we detail the implementation of a deployment pipeline system designed to support automated production of global scale poverty maps that is flexible enough to incorporate any dataset and model. This represents the first step towards providing up-to-date poverty maps to guide the decision-making process of nonprofit organizations and policymakers.
Description
Type of resource | text |
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Date created | May 2017 |
Creators/Contributors
Author | Xie, Michael | |
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Primary advisor | Ermon, Stefano | |
Degree granting institution | Stanford University, Department of Computer Science |
Subjects
Subject | Poverty |
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Subject | Satellite |
Subject | Imagery |
Subject | Mapping |
Subject | Convolutional |
Subject | Deep Learning |
Subject | Neural Network |
Subject | Semi-supervised |
Subject | Gaussian Process |
Subject | Deep Kernel Learning |
Subject | Africa |
Subject | Stanford |
Subject | Computer Science |
Subject | School of Engineering |
Subject | SUSTAIN |
Subject | Nighttime Light |
Subject | Artificial Intelligence |
Subject | Science |
Subject | Sustainability |
Subject | Machine Learning |
Genre | Thesis |
Bibliographic information
Related Publication | Michael Xie, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon. Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. 30th AAAI Conference on Artificial Intelligence, 2016. |
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Related Publication | Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon. Combining Satellite Imagery and Machine Learning to Predict Poverty. Science, 353(6301):790{794, 2016. |
Related Publication | Neal Jean, Michael Xie, and Stefano Ermon. Semi-supervised Deep Kernel Learning. NIPS 2016 Bayesian Deep Learning Workshop, 2016. |
Related item | |
Location | https://purl.stanford.edu/dy158hx2433 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
- Michael Xie. Mapping Poverty with Satellite Imagery. Stanford Digital Repository. URL http://purl.stanford.edu/dy158hx2433, 2017.
Collection
Undergraduate Theses, School of Engineering
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- Contact
- xie@cs.stanford.edu
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