Yazdani, M., Nguyen, M., Block, J., Crawl, D., Zurutuza, N., Kim, D., Hanson, G., and Altintas, I., Scalable Detection of Rural Schools in Africa using Convolutional Neural Networks and Satellite Imagery, In the fifth international workshop on Smart City Clouds: Technologies, Systems and Applications (SCCTSA) at the IEEE/ACM International Conference on Utility and Cloud Computing (UCC), 2018.
Many countries typically lack sufficient civic data to assess where and what challenges communities face. High resolution satellite images can provide honest assessments of neighborhoods and communities to guide aid workers, policy makers, private sector, and philanthropists. Although humans are very good at detecting patterns, manually inspecting high resolution satellite imagery at scale can be costly and time consuming. Machine learning has the potential to scale this process significantly and automate the detection of regions of interest. Here we tackle the problem of identifying schools in northeastern rural Liberia as a case study for evaluating the value of high resolution satellite imagery and machine learning. In our case study we utilize unsupervised learning with pre-trained convolutional neural networks. Our results suggest that using machine learning with high resolution satellite images can reduce the search space, help find schools with high recall and aid appropriate and relevant resource allocations.