Abstract
Oak Ridge National Laboratory (ORNL) annually develops the LandScan Global (LSG) dataset, a 30 arcsecond global gridded population dataset representing global ambient human population distribution. This multivariable dasymetric model disaggregates census counts within administrative boundaries using ancillary data. Each country’s distribution reflects cultural and socioeconomic patterns; manual validations yield a unique global dataset for assessing populations at risk. For over two decades, LSG has been a standard for estimating populations at risk, aiding U.S. federal government, academia and humanitarian organizations. During disasters such as the 2004 Indian Ocean tsunami and the 2010 Haiti earthquake and geopolitical crises such as the Syrian civil war and the 2022 Russian invasion of Ukraine, LSG supported scientific and operational communities in emergency response and recovery. In 2022, LSG datasets from 2000 onward were made publicly available through ORNL’s LandScan Portal. This data descriptor details our methodology and the application of geospatial science and machine learning to geographic and demographic data, highlighting uses in urban resiliency, emergency management, disaster response, and human health and security.
| Original language | English |
|---|---|
| Article number | 495 |
| Journal | Scientific Data |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2025 |
Funding
All past and present LandScan Global team members, including Jack Gonzales, Daniel Adams, Budhendra Bhaduri, Jerome Dobson, Richard Durfee, Brian Worley, Amy King, Jessica Moehl, Eric Weber, Jeanette Weaver, Melanie Laverdiere, Benjamin Swan, Lexie Yang, Aaron Myers, Eric Powell, Dalton Lunga, Mary Ann O’Shell, Nathan Trombley, Timmy Huynh, Scott Basford, Sarah Walters, Carson Woody, Jason Wencak, David Sheldon, Ed Tinnel, Ron Lee, Nagendra Singh, Heinrich Erica and many others who were part of various ORNL internships, post-BS or MS programs, or visiting scientists. The LandScan program is funded by the U.S. Department of Defense. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://www.energy.gov/doe-public-access-plan ).