UNICEF Innovation Office is in charge to map every school in Kenya, Rwanda, Sierra Leone, Niger, Honduras, Ghana, Kazakhstan, and Uzbekistan, applying scalable machine learning over high-resolution satellite imagery, with the aim of accelerating connectivity, online learning and other initiatives for children and their communities and driving economic stimulus under UNICEF's GIGA mission, particularly in low-income countries. Read more of the Project Connect technical report.

Development Seed's Data team support this project on the training data creation and the ML inference validation.

Training data creation

Development Seed's Data team generated a high-quality training dataset, for machine learning models, using as data sources; schools provided by UNICEF and schools data from OpenStreetMap from 9 countries, which were: Rwanda, Sierra Leone, Niger, Chad, Sudan, Mali, Honduras, Kazakhstan, and Kenya.

Finally, the Data team validated around 120k schools points, adding one of these tags: "dc_has_pattern_school=yes", "dc_has_pattern_school=unrecognized", "dc_has_pattern_school=no"; according the school patterns recognized in each of them.

Post inference validation

Development Seed's Data team performed a validation of the ML inferences from 8 countries (Kazakhstan, Kenya, Rwanda, Niger, Honduras, Uzbekistan, Sierra Leone, and Ghana), validating more than 113k schools tiles.