Citation: Gill, J., Pope, A. "How open source AI can improve population estimates, sustainable energy, and the delivery of climate change interventions." Engineering at Meta, 3 October 2024, https://engineering.fb.com/2024/10/03/ml-applications/open-source-ai-population-maps-meta/

Citation: Karagiorgos, K., Georganos, S., Fuchs, S. et al. Global population datasets overestimate flood exposure in Sweden. Sci Rep 14, 20410 (2024). https://doi.org/10.1038/s41598-024-71330-5

Citation: Wang S, Wang L. A Novel Framework for Mapping Updated Fine-resolution Populations with Remote Sensing and Mobile Phone Data. J. Remote Sens. 2024;4:Article 0227. https://doi.org/10.34133/remotesensing.0227

The POPGRID Data Collaborative highlights case studies from end data users and decision-makers about practical applications of gridded population datasets. Learn how gridded population datasets are being used as a population sampling frame in a national vaccination coverage survey in Lao PDR, and for urban planning in Ndola, Zambia.

Citation: McKeen, T., Bondarenko, M., Kerr, D. et al. High-resolution gridded population datasets for Latin America and the Caribbean using official statistics. Sci Data 10, 436 (2023). https://doi.org/10.1038/s41597-023-02305-w

Citation: Pirowski, Tomasz, and Bartłomiej Szypuła. Dasymetric Population Mapping Using Building Data. Annals of the American Association of Geographers. 2024; 1–19. https://doi.org/10.1080/24694452.2024.2313500.