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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2022

Improving the LandScan USA Non-Obligate Population Estimate (NOPE)

Where do people go when they have nowhere to be? Nonobligate activities are a significant part of our social and cultural lives, but there are no existing large scale data which characterize spatial variability in population allocation for these activities. As large scale population estimates have ever-finer resolutions, gaps in our ability to estimate this population segment have an increasingly large impact on high resolution population estimates. In this paper, we demonstrate an improved method for estimating the spatial allocation of the non-obligate population - people who are not at work, school, or in another residential institution. This method builds upon on anonymized and aggregate data on visits to public places, allocating the non-obligate population proportionally to worker population while accounting for the estimated ratio of visitors to workers in public places.

Brelsford, Christa↗
DOE OSTI2021

Effect of Image Classification Accuracy on Dasymetric Population Estimation

Dasymetric mapping involves the disaggregation of count data, usually relating to population/demographics, from census enumeration areas to smaller target zones with the aid of an ancillary layer related to population density. The ancillary layer is often a binary classification such as developed versus undeveloped, building versus non-building, and residential versus non-residential, in which one class is treated as populated and the other as unpopulated. While dasymetric mapping relies heavily on ancillary data, little research has been done to address the error in ancillary data and its effects on dasymetric mapping accuracy. This chapter reports our research effort to investigate the effect of image classification accuracy on dasymetric population estimates by developing a binomial classification of buildings from high-resolution remote sensor imagery. The classifier was systematically and iteratively manipulated to generate a series of outputs with variegated accuracy characteristics. Lastly, we generated a corresponding series of population estimates based on the mapped building area from each iteration to investigate the relationship between the accuracy of classification and population estimation.

McKee, Jacob↗
NASA NTRS2007

GEO Population Estimates using Optical Survey Data

Optical survey data taken using the NASA Michigan Orbital Debris Survey Telescope (MODEST) gives us an opportunity to statistically sample faint object population in the Geosynchronous (GEO) and near-GEO environment. This paper will summarize the MODEST survey work that has been conducted by NASA since 2002, and will outline the techniques employed to arrive at the current population estimates in the GEO environment for dim objects difficult to detect and track using current systems in the Space Surveillance Network (SSN). Some types of orbits have a higher detection rate based on what parts of the GEO belt is being observed, a straightforward statistical technique is used to debias these observations to arrive at an estimate of the total population potentially visible to the telescope. The size and magnitude distributions of these fainter debris objects are markedly different from the catalogued population. GEO debris consists of at least two different populations, one which follows the standard breakup power law and one which has anomalously high Area-to-Mass Ratios (1 to approx. 30 square meters per kilogram; a sheet of paper = approx. 13 square meters per kilogram). The Inter-Agency Space Debris Coordination Committee (IADC) is investigating objects in GEO orbits with anomalously high Area-to-Mass Ratios (AMRs). The ESA Space Debris Telescope discovered this population and has and its properties of inclinations (0 to 30 degrees), changing eccentricities (0 and 0.6), and mean motions (approx. 1 rev), will be presented. The accepted interpretation of this orbital behavior is that solar radiation pressure drives the perturbations causing time varying inclinations and eccentricities. The orbital parameters are unstable for this population and thus difficult to predict. Their dim visual magnitudes and photometric variability make observations a challenge. The IADC has enlisted a series of observatories (participating institutions: University of Michigan/CTIO, Astronomical Institute University of Bern, Boeing LTS / AMOS, Keldysh Institute of Applied Mathematics) at different longitudes. Complete observational coverage over periods of days to months will provide a better understanding of the properties, such as solar radiation pressure effects on orbital elements, size, shape, attitude, color variations, and spectral characteristics. Results from recent observational programs will be summarized, and includes a description of the orbit elements prediction processes, a summary of the metric tracking performance, and some photometric characteristics of this class of debris.

Barker, Edwin S.↗
DOE OSTI2024

At Risk Population Estimates for Belarus, Poland and Slovakia with Machine Learning

High-resolution gridded population modeling is crucial for various applications, including disaster response planning, infectious disease spread modeling, climate change impact estimation, policy development, and more. Multiple gridded population datasets have been developed, each tailored to meet specific objectives. Among them, LandScan Global dataset is designed to represent ambient and unwarned population distributions. However, this dataset relies on a statistical approach that requires manual adjustments, making it time consuming and labour intensive. Existing machine learning (ML) methods often train and test at different spatial resolutions, potentially leading to inflated results, and they rely on Census population totals for disaggregation. To address these limitations, in this study we developed population estimates using ML models trained and tested at a consistent 30 arc-second resolution (≈1 square kilometer), specifically using Random Forest (RF) and XGBoost. These models were trained on 2020 datum to predict for 2021 for three countries: Belarus, Poland, and Slovakia. Our findings show that both RF (MAE varies from 5.75 to 13.25) and XGBoost (MAE varies from 8.15 to 23.44) model performance is close to LandScan Global estimates. Furthermore, neither of the models performed the best across all grid cells: the RF model was more effective in areas with lower populations, while XGBoost excelled in more densely populated regions. The proposed approach can be used for countries where the Census data is not available.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
NASA NTRS1987

Estimating population size of Pygoscelid Penguins from TM data

An estimate was made toward a continent wide population of penguins. The results indicate that Thematic Mapper data can be used to identify penguin rookeries due to the unique reflectance properties of guano. Strong correlations exist between nesting populations and rookery area occupied by the birds. These correlations allow estimation of the number of nesting pairs in colonies. The success of remote sensing and biometric analyses leads one to believe that a continent wide estimate of penguin populations is possible based on a timely sample employing ground based and remote sensing techniques. Satellite remote sensing along the coastline may well locate previously undiscovered penguin nesting sites, or locate rookeries which have been assumed to exist for over a half century, but never located. Observations which found that penguins are one of the most sensitive elements in the complex of Southern Ocean ecosystems motivated this study.

Olson, Charles E., Jr.↗
NASA NTRS1989

A New Population Estimate for the Florida Scrub Jay on Merritt Island National Wildlife Refuge

The variable circular plot method was used to sample avifauna within different vegetation types determined from aerial imagery. The Florida Scrub Jay (Aphelocoma coerulescens coerulescens) population was estimated to range between 1,415 and 3,603 birds. Approximately half of the scrub and slash pine habitat appeared to be unused by Florida Scrub Jay, probably because the slash pine cover was too dense or the oak cover was too sparse. Results from the study suggest that the entire state population may be much lower than believed because the size of two of the three largest populations may have been overestimated.

Breininger, David R.↗
NASA NTRS1974

Estimating population from photographically determined residential land use types

The study reported was concerned with the development of a methodology for updating published census data during intercensal periods. The new approach utilizes aerial photographs as a primary data source. The methodology, which was devised for estimating the population size of four cities in California, consisted of a simple function relating the measured area of three dominant residential land use types and the characteristic spatial population densities associated with each.

Kraus, S. P.↗
DOE OSTI2024

Disturbance of hibernating bats due to researchers entering caves to conduct hibernacula surveys

Estimating population changes of bats is important for their conservation. Population estimates of hibernating bats are often calculated by researchers entering hibernacula to count bats; however, the disturbance caused by these surveys can cause bats to arouse unnaturally, fly, and lose body mass. We conducted 17 hibernacula surveys in 9 caves from 2013 to 2018 and used acoustic detectors to document cave-exiting bats the night following our surveys. We predicted that cave-exiting flights (i.e., bats flying out and then back into caves) of Townsend’s big-eared bats (Corynorhinus townsendii) and western small-footed myotis (Myotis ciliolabrum) would be higher the night following hibernacula surveys than on nights following no surveys. Those two species, however, did not fly out of caves more than predicted the night following 82% of surveys. Nonetheless, the activity of bats flying out of caves following surveys was related to a disturbance factor (i.e., number of researchers × total time in a cave). We produced a parsimonious model for predicting the probability of Townsend’s big-eared bats flying out of caves as a function of disturbance factor and ambient temperature. That model can be used to help biologists plan for the number of researchers, and the length of time those individuals are in a cave to minimize disturbing bats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

TOWARDS RAPID RESPONSE UPDATES OF POPULATIONS AT RISK

Understanding population at risks has been a focus of the LandScan program through its development of population estimates. With advancements in computer vision, deep learning technologies and access to High Performance Computing (HPC) and high resolution imagery, population estimates are now modeled at the building level. However, when those patterns are disrupted, rapid updates to population distribution estimates are needed to support humanitarian aid and response. Oak Ridge National Laboratory (ORNL) recently adapted an existing deep learning building footprint extraction model in development of a scalable approach to Building Damage Assessments (BDA). This new opportunity opens the possibility of automating BDA to support rapid population distribution estimate updates for geographic areas involved in geopolitical conflicts or natural events for humanitarian aid and response or where to focus recovery efforts. In addition, incorporate social surveys to further model human behavior under conflict or other scenarios that disrupt normal patterns of life.

Urban, Marie↗
NASA NTRS1981

Estimating cell populations

An important step in the diagnosis of a cervical cytology specimen is estimating the proportions of the various cell types present. This is usually done with a cell classifier, the error rates of which can be expressed as a confusion matrix. We show how to use the confusion matrix to obtain an unbiased estimate of the desired proportions. We show that the mean square error of this estimate depends on a 'befuddlement matrix' derived from the confusion matrix, and how this, in turn, leads to a figure of merit for cell classifiers. Finally, we work out the two-class problem in detail and present examples to illustrate the theory.

White, B. S.↗
DOE OSTI2025

LandScan Mosaic

The LandScan program at Oak Ridge National Laboratory (ORNL), in collaboration with the National Geospatial-Intelligence Agency (NGA), continues to deliver the most accurate and up to date global, high resolution gridded population data. Additionally, the latest advancements in the LandScan HD methodology led to reduced latency in development of rapid updates for geopolitical events. With momentum towards reporting more up to date population estimates, feedback from the user community expressed interest in reporting population estimates in ranges - whether to express a level of uncertainty or confirm to leadership and stakeholders the modeled data are estimates. Building upon the need to understand uncertainty or confidence in the modeled data and report ranges at the global scale, LandScan Mosaic was developed. LandScan Mosaic represents the next generation of high-resolution population modeling, building upon the established success of previous LandScan HD iterations. While LandScan HD employed a deterministic big data fusion approach, LandScan Mosaic enhances this methodology by integrating advanced machine learning techniques to impute missing, yet crucial, population model parameters. This advancement allows for probabilistic modeling of building occupancy and population distribution, incorporating uncertainty quantification through Monte Carlo sampling methods. By combining big data fusion with machine learning-driven imputation and stochastic modeling, LandScan Mosaic provides a more comprehensive and robust representation of population dynamics. LandScan Mosaic will be following the in the footsteps of its longstanding counterpart LandScan Global and releasing a global gridded population raster, at the 3-arcsecond resolution. This technical report documents the current stage of development of LandScan Mosaic, detailing the methodologies and data sources behind the modeling. Stakeholders are encouraged to use this document as an authoritative reference for insight into Mosaic’s data development processes. However, readers should note that LandScan Mosaic remains in a late-stage research and development phase, and methodologies and data presented here are subject to refinements ahead of the anticipated global release in Summer 2025. Feedback and inquiries from users and stakeholders are welcomed as we continue to refine and enhance this important population resource.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

A remote sensing analysis of Adelie penguin rookeries

The Adelie penguin (Pygoscelis adeliae) makes up the vast majority of bird biomass in the Antarctic. As a major consumer of krill, these birds play an important role in the Antarctic food web, and they have been proposed as an indicator species of the vitality of the Southern Ocean ecosystem. This study explores the terrestrial habitat of the Adelie penguin as a target for remote sensing reconnaissance. Laboratory and ground-level reflectance measurements of Antarctic materials found in and around penguin rookeries were examined in detail. These analyses suggested data transformations which helped separate penguin rookeries from surrounding areas in Landsat Thematic Mapper imagery. The physical extent of penguin rookeries on Ross and Beaufort Islands, Antarctic, was estimated from the satellite data and compared to published estimates of penguin populations. The results suggest that TM imagery may be used to identify previously undiscovered penguin rookeries, and the imagery may provide a means of developing new population estimation methods for Antarctic ornithology.

Schwaller, Mathew R.↗
DOE OSTI2024

Post-Release Survivorship in a New Population of Blanding's Turtles Established Using Headstarted and Directly Released Turtles

Blanding’s Turtles are facing a variety of anthropogenic threats that decrease population viability. We initiated a project to establish a new population of Blanding’s Turtles on a National Wildlife Refuge in Massachusetts using hatchlings obtained from a nearby robust donor population. We released 440 head-started individuals and 401 directly-released hatchlings between 2007–2013 and conducted multiple years of post-release monitoring via aquatic trapping to estimate survival. Between 2014–2021 we released an additional 824 head-started and 433 directly-released individuals and conducted one year of aquatic trapping in 2021 to estimate population size. First year post-release survival of head-started turtles was six times that of directly-released hatchlings (0.72 vs. 0.12, respectively), but annual survival of both groups was 0.78–0.90 in subsequent years. Only 19% of turtles released at <60 mm carapace length (CL) were subsequently recaptured compared to 60% of those released at ≥60 mm CL, supporting 60 mm CL as a target minimum release size. The mean population estimates using two different study designs were 164 and 202, with 91 individuals encountered. Now that turtles released early in the project are approaching reproductive maturity, we recommend resampling and expanding trapping efforts to include more wetlands to assess long-term survival, abundance, and distribution of Blanding’s Turtles in this newly established population. Furthermore, our results, combined with other evaluations of head-starting across the species’ range, add credence to the use of head-starting as a population recovery tool for Blanding’s Turtles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach

High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Estimating the population level impact of a gonococcal vaccine candidate: Predictions from a simple mathematical model

Neisseria gonorrhoeae cross-protection was suggested in a New Zealand meningitis B vaccine. We modeled the potential impact of similar vaccines on gonorrhea prevalence in heterosexuals in the United States. Here, our mathematical model incorporated infection, behavior, and vaccination dynamics. Approximate Bayesian Computation calibrated our model to US prevalence. Primary analyses assumed New Zealand vaccine characteristics: 30% efficacy and 2-year duration of protection. We estimated impact under two vaccine coverages (20%, 50%). Reduction in gonorrhea prevalence ranged from 4.8 to 39.4%, depending on vaccine coverage. Vaccine impact was correlated with both size of the highly sexually active subpopulation and sexual mixing between high and low activity subpopulations. A meningitis vaccine providing low efficacy cross-protection against gonorrhea acquisition and short duration of protection could result in a large reduction in gonorrhea prevalence in the United States. Potential dual protective effects can be considered when making vaccine recommendations.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1991

Estimating Occupancy of Gopher Tortoise (Gorpherus polyphemus) Burrows in Coastal Scrub and Slash Pine Flatwoods

One hundred twelve plots were established in coastal scrub and slash pine flatwoods habitats on the John F. Kennedy Space Center (KSC) to evaluate relationships between the number of burrows and gopher tortoise (Gopherus polyphemus) density. All burrows were located within these plots and were classified according to tortoise activity. Depending on season, bucket trapping, a stick method, a gopher tortoise pulling device, and a camera system were used to estimate tortoise occupancy. Correction factors (% of burrows occupied) were calculated by season and habitat type. Our data suggest that less than 20% of the active and inactive burrows combined were occupied during seasons when gopher tortoises were active. Correction factors were higher in poorly-drained areas and lower in well-drained areas during the winter, when gopher tortoise activity was low. Correction factors differed from studies elsewhere, indicating that population estimates require correction factors specific to the site and season to accurately estimate population size.

Breininger, David R.↗