The latest map for Bangladesh can be downloaded on Facebook’s page on Humanitarian Data Exchange.

Facebook is applying the processing muscle of its compute power, its extensive data science skills and its expertise in AI to create the world’s most detailed and accurate maps of local populations.

Facebook also partners with Columbia University’s Center for International Earth Science Information Network (CIESIN (http://www.ciesin.org/)) to ensure that this effort leverages the best available administrative data for all countries involved, said a media release on Thursday.

By combining these publicly and commercially available datasets with Facebook’s AI capabilities, Facebook has created population maps that are 3X more detailed than any other source.

The high resolution population density maps will show an estimate of the number of people living within 30-meter grid tiles, and include the number of children under five, as well as the number of women of reproductive age, and other helpful demographics.

“Facebook’s population density maps can improve how nonprofits do their work, how researchers learn, and how policies are developed. Building data products from non-personal data sources like satellite imagery and census data allows Facebook to share its data science and compute power with the world while protecting privacy,” said the spokesperson.

High-resolution satellite imagery already exists for much of the world.

However, prior to Facebook’s mapping project, it would have required countless hours for volunteers to comb through millions of square miles of pictures to identify which contained a tiny town or remote village.

The Facebook team used AI to solve that problem, efficiently crunching through data at a petabyte scale.

For X country, for example, the computer vision system examined 11z.5 billion individual images to determine whether they contained a building. The team found approximately 110 million building locations in just a few days.

“Since I first started my humanitarian career in the Peace Corps up until just a few weeks ago speaking with experts at the 2019 World Health Assembly in Geneva, a common need is accurate population data,” says Alex Pompe, a research manager at Facebook.

“These maps showcase the power of collaboration between Facebook and top research institutions like Columbia University to combine public data sources and machine learning to empower more data-driven humanitarian projects around the globe.”

Using its machine learning capabilities, Facebook started developing population density maps to provide better tools to support connectivity efforts around the world.

No Facebook data is used in the project and the census and satellite data used contain no personally identifiable information.

To learn more about Facebook’s work on high resolution population density maps and other efforts in data for good, please visit the project website here.

Humanitarian Open Street Map has used the high resolution population density maps in a number of countries to support their work on the Missing Maps project.

“Facebook’s high-resolution population maps have supported Humanitarian OpenStreetMap and Missing Maps’ mission of putting the world’s most at-risk places on the map,” says Tyler Radford, executive director of the Humanitarian OpenStreetMap Team, which participates in the Missing Maps project.

“The maps from Facebook ensure we focus our volunteers’ time and resources on the places they're most needed, improving the efficacy of our programs.”

Crisis event management software vendor Kontur is also using population density maps in its Disaster Ninja platform to help the Humanitarian OpenStreetMap Team to be able to make faster decisions during times of natural disasters.

Darafei Praliaskouski, Head of Product from Kontur says, “When you're deploying a team of disaster responders to crisis site, you want to provide them with a map of the area they'll be operating in, especially in vulnerable regions throughout Asia. We've made Disaster Ninja with Facebook Population Density maps so that mappers don't have to manually inspect thousands of square kilometers and focus on mapping the AI-highlighted buildings and roads right away.”