Using AI to map Africa’s buildings

How will this improve planning?

There are many important ways in which this data can be used, including — but not limited to — the following:

Population mapping: Building footprints are a key ingredient for estimating population density. This information is vital to planning for services for communities. 

Humanitarian response: To plan the response to a flood, drought, or other natural disaster.

Environmental science: Knowledge of settlement density is useful for understanding the human impact on the natural environment. 

Addressing systems: In many areas, buildings do not have formal addresses. This can make it difficult for people to access social benefits and economic opportunities. Building footprint data can help with the rollout of digital addressing systems such as Plus Codes.

Vaccination planning: Knowing the density of population and settlements helps to anticipate demand for vaccines and the best locations for facilities. This data is also useful for precision epidemiology, as well as prevention efforts such as mosquito net distribution.

Statistical indicators: Buildings data can be used to help calculate statistical indicators for national planning, such as the numbers of houses in the catchment areas of schools and health centers, mean travel distances to the nearest hospital or demand forecast for transportation systems.

Google’s AI Center in Accra

This project was led by our team at the AI Research Center in Accra, Ghana. The center was launched in 2019 to bring together top machine learning researchers and engineers dedicated to AI research and its applications. The research team has already been improving Google Maps with AI, adding 120 million buildings and 228,000 km of roads across Africa to Maps in the last year. This work is part of our broader AI for Social Good efforts.