This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Researchers at the Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) have released LandScan Mosaic, a next-generation global population distribution dataset that estimates where people are by modeling how buildings are used and occupied throughout the day. Built on the foundation of ORNL's widely used LandScan Global, one of the world's most accurate population distribution datasets, Mosaic's enhancements will improve disaster response, humanitarian assistance, infrastructure planning and national security missions worldwide.
LandScan Mosaic's research innovations include: While traditional approaches rely largely on pixels from satellite imagery to estimate populations across landscapes, LandScan Mosaic introduces a new building-centric modeling framework. Researchers implemented machine learning techniques to estimate missing building characteristics—including height, floor count, function and use type—enabling consistent global application even in regions where detailed building information is sparse. By bringing in additional information about buildings and land use, along with standardized occupancy distributions, the model represents how people occupy residential, commercial, industrial and other structures around the world.
The result is a high-resolution representation of ambient population—the average number of people present in a location over a 24-hour period—that captures daily movement between homes, workplaces, schools and other activity spaces. LandScan Mosaic also provides something unavailable in previous global population datasets: explicit measures of uncertainty that help users understand confidence in the underlying population estimates. Daniel Adams, R&D scientist at ORNL and lead author of a Scientific Reports paper describing LandScan Mosaic methodology, said that, to the team's knowledge, this is the first globally available population dataset release with these characteristics.
"Decision makers often need to act before perfect information is available," Adams said. "By pairing population estimates with transparent measures of uncertainty, LandScan Mosaic helps users understand not only where people are likely located, but also how confident they can be in each estimate." As they've incorporated AI into the LandScan program, the team has remained focused on ensuring users can trust the data to make decisions in rapidly changing situations such as disaster response as well as more stable conditions such as infrastructure planning. "We want to power everything we're doing, using state-of-the-art methodologies, but still preserve that trust piece," Adams said.
"This is a really important project for showcasing how AI and decision analysis can really be blended together in a trustworthy manner." Established in 1999, the LandScan program was created to address the need for more accurate population distribution data for emergency response, risk assessment and national security applications. LandScan Global pioneered the concept of ambient population, modeling the full activity space of people over a 24-hour period rather than relying solely on residential locations. Marie Urban, principal investigator for the LandScan program, said decades of population modeling expertise combined with cutting-edge high-performance computing and AI technologies uniquely position ORNL to perform this work.
"The multidisciplinary approach we take allows us to bring together computer scientists, data scientists, geographers, people with expertise from different backgrounds … We have a lot of great research staff bringing a lot of different skill sets in to help us take the program into another dimension," Urban said. Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. d research that matter—daily or weekly. To go forward, Urban said the team is looking backward.
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