Date Thesis Awarded


Access Type

Honors Thesis -- Access Restricted On-Campus Only

Degree Name

Bachelors of Science (BS)


Applied Science


Dan Runfola

Committee Members

Dan Runfola

Matthias Leu

Anthony Stefanidis


Convolutional neural networks are deep-learning models commonly applied when analyzing imagery. Convolutional neural networks and satellite imagery have shown potential for the global estimation of key factors driving socioeconomic ability to adapt to global change. Unlike more traditional approaches to data collection, such as surveys, approaches based on satellite data are low cost, timely, and allow replication by a wide range of parties. We illustrate the potential of this approach with a case study estimating school test scores based solely on publicly available imagery in both the Philippines (2010, 2014) and Brazil (2016), with predictive accuracy across years and regions ranging from 76% to 80%. Finally, we discuss the numerous obstacles remaining to the operational use of CNN-based approaches for understanding multiple dimensions of socioeconomic vulnerability, and provide open source computer code for community use.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

On-Campus Access Only