Developed for UNICEF


ML prediction validation

In the validation process, expert mappers validated each of predicted schools as either “confirmed”, “unrecognized”, and “not-school”, based on the learned school features from the cleaned school dataset. With the increase of the threshold, e.g. from 0.44 to 0.99, we would limit the false prediction but we will also lose an increasing proportion of correct prediction. When the threshold is set to 0.92, we have 73,717 tiles that are predicted school tiles for our expert mappers to go through. With the validation speed of 10,000 tiles per day, we were able to complete the predicted school validation within eight working days. See our the results in next section.

ML school validation
AI-assist human schools detection in Colombia. Blue dots are validated and confirmed as schools by the expert mappers