GeoPython 2021

ML-Enabler: Enabling Rapid Machine Learning Inference of School Mapping in Asia, Africa and South America
2021-04-22, 20:00–20:30, Track 1

ML-Enabler is an open source model inferencing tool with a UI that acts as a github for models, allows users to run inference at scale, validate model predictions, integrate with common OSM mapping tools like Map Roulette. We will discuss how Development Seed used ML-Enabler to facilitate model inference to detect previously unmapped schools over 71 million zoom 18 tiles over multiple countries in Africa, Asia, and South America as part of UNICEF’s Project Connect initiative.

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 the 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 around 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 Unfortunately, some national governments still don’t know where all the schools in their country are, or have out of date school maps.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.