Scaling AI to map every school on the planet
2021-09-30, 14:00–14:30, Ushuaia

UNICEF's Giga Initiative endeavors to map every school on the Planet. Knowing the location of schools is the first step to accelerate connectivity, online learning, and initiatives for children and their communities, and drive economic stimulus, particularly in lower-income countries. Development Seed is working with the UNICEF Office of Innovation to enable rapid school mapping from space across Asia, Africa, and South America with AI. In seven months of development and implementation, we added 23,100 unmapped schools to the map in Kenya, Rwanda, Sierra Leone, Niger, Honduras, Ghana, Kazakhstan, and Uzbekistan.
To accomplish this we built an end-to-end scalable AI model pipeline that scans high-resolution satellite imagery from Maxar, applies our highly refined algorithm for identifying buildings that are likely to be schools, and flags those schools for human review by our talented Data Team. You can view our interactive maps of schools before and after the project for all countries we mapped, and examples of unmapped schools we found through the project.
Scanning high-resolution imagery of large sections of the planet is a massive undertaking. Accomplishing an effort of this scale gave us the opportunity to make improvements to our scalable AI tool (will be open-sourced soon), from efficient model training and experiments with Kubeflow on Google Kubernetes Engine, fast model inference with ML-Enabler, and data curation tools. We are proud to contribute these advances with AI for Good communities through advancing open datasets and open source libraries.

UNICEF and Development Seed are working to leverage machine learning, high-resolution imagery, and inexpensive cloud computing to create a comprehensive map of school at a global scale. Accurate data about school locations is critical to provide quality education and promote lifelong learning, UN sustainable development goal 4 (SDG4), to ensure equal access to opportunity (SDG10) and eventually, to reduce poverty (SDG1). However, in many countries, educational facilities’ records are often inaccurate or incomplete. Understanding the location of schools can help governments and international organizations gain critical insights into the needs of vulnerable populations, and better prepare and respond to exogenous shocks such as disease outbreaks or natural disasters. Unfortunately, some national governments still don’t know where all the schools in their country are or have out-of-date school maps.

Despite their varied structure, many schools have identifiable overhead signatures that make them possible to detect in high-resolution imagery with deep learning techniques. Approximately 18,000 previously unmapped schools across 5 African countries, Kenya, Rwanda, Sierra Leone, Ghana, and Niger, were found in satellite imagery with a deep learning classification model. These 18,000 schools were validated by expert human mapping analysts. In addition to finding previously unmapped schools, the models were able to identify already mapped schools with accuracy between 77 - 95% depending on the country. To facilitate running model inference across over 71 million zoom 18 tiles of imagery development seed relied on our open-source tool ML-Enabler.
ML Enabler generates and visualizes predictions from models that are compatible with Tensorflow’s TF Serving. ML-Enabler makes managing the infrastructure for running inference at scale and visualizing predictions straight-forward from a UI. ML Enabler will spin up the required AWS resources and run inference to generate predictions. ML Enabler helps harness the power of expert human mappers because model predictions can be validated within the UI and validated predictions can be used to generate new training data and re-train the initial model.
However, a notable limitation of our approach is it relies on human validators for both the training data creation and school validation. As a result, we may introduce a bias for schools that follow common patterns that are recognizable from space. Using a human-in-the-loop and active learning process is critical.
At Development Seed we are engaged in active research and development of human-in-the-loop active learning methods that allow non-expert human mappers and AI to work more efficiently together and improve model prediction.

Authors and Affiliations

Development Seed Project Team: Zhuangfang NaNa Yi, Ruben Lopez Mendoza, Martha Morrissey, Nick Ingalls, Chuck Daniels, Jeevan Farias, Karito Tenorio, Pilar Serrano, Sajjad Anwar
UNICEF Project Team: Naroa Zurutuza, Do-Hyung Kim


Use cases & applications


Data collection, data sharing, data science, open data, big data, data exploitation platforms


1 - Principiants. No required specific knowledge is needed.

Language of the Presentation


Dr. Zhuang-Fang NaNa Yi is GeoAI Lead and Machine Learning Engineer at Development Seed, who has a Ph.D. in Ecological Economics and has applied geospatial analysis and satellite imagery processing in her various academic works since 2010. Before DevSeed, she was a research scientist in quantitative ecology.

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Martha is a Machine Learning Engineer at Development Seed. Outside of work Martha enjoys cycling, running, and taking her cat on walks.

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