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Oak Ridge National Laboratory (ORNL)

Oak Ridge National Laboratory (ORNL) is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security.

ORNL’s diverse capabilities span a broad range of scientific and engineering disciplines, enabling the Laboratory to explore fundamental science challenges and to carry out the research needed to accelerate the delivery of solutions to the marketplace. ORNL supports DOE’s national missions of scientific discovery, clean energy, and security, in these four major areas of science and technology: Neutrons, Computing, Materials, Nuclear.

Data Products

  • LandScan Global

    • LandScan Global is a 30 arc-second (~1 km) resolution gridded population dataset representing an “ambient” (average over 24 hours) population count. The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary.  Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region.
    • Data Access:
  • LandScan USA

    • LandScan USA provides estimated population counts at 3 arc-second resolution for nighttime and daytime scenarios, for the Continental United States, Hawaii, and Alaska; nighttime population estimates at 3 arc-second resolution are also available for Puerto Rico.  Residents, prisoners, workers, students, and shoppers are modeled as baseline population estimates, capturing the diurnal variations of the U.S. population. 
    • Data Access:  HIFLD Secure
  • LandScan HD

    • LandScan HD provides population estimates at 3 arc-second resolution for selected countries and regions. LandScan HD modeling is tailored to the unique data conditions of individual countries or regions. Each LandScan HD dataset incorporates current land use and infrastructure data from a variety of sources; applies population density estimates from census sources, from ORNL’s Population Density Tables (PDT) project, and/or from microcensus field surveys; and leverages the high-resolution settlement and building layers created at ORNL. 
    • Data Access:  Not currently available to the public.
  • LandCast

    • LandCast comprises locally adaptive, spatially explicit population projections for the contiguous United States for 2030 and 2050 represented as gridded datasets. For each target year, the projected distribution models an ambient population at a spatial resolution of 30 arc-seconds (`~1 km) based on a business as usual scenario. LandCast builds on the intelligent dasymetric modeling techniques employed by LandScan Global and LandScan USA to down-scale national level population projections and model spatial population growth at the local level. The underlying model is both locally adaptive and spatially explicit to account for local subtleties unaccounted for in most large-scale projection models.
    • Data Access:  Please e-mail

Related Publications

Supporting Activities

  • LandScan Settlement and Building Layers are the result of advances in computer vision and access to high performance computing (HPC) at ORNL for identifying settled or built-up areas in support of high-resolution population modeling. Both texture-oriented machine learning as well as deep convolutional neural networks have been used in conjunction with ORNL HPC resources and a variety of high-resolution image sources to generate settlement and building areas for select areas ranging in resolution from built-up areas at 8 meters to precise building delineations at 0.5 meters. These layers form the spatial foundation for mapping the distribution of population in LandScan HD and LandScan USA.
Partner Type