# Unlocking Earth AI’s planetary geospatial foundation models for global public health > Source: > Published: 2026-10-06 15:05:11+00:00 October 6, 2026 Arbaaz Muslim, Software Engineer, Google Research, and Gautam Prasad, Software Engineer, Google Research With Google Earth AI’s Population Dynamics Foundation Model (PDFM), we can address the data gaps and temporal reporting lags of existing epidemiological workflows. In our latest work, we present five partner-driven case studies demonstrating how this model exemplifies the planetary geospatial foundation model paradigm for global public health. Public health decisions rely heavily on timely, granular data. For health conditions, such as cardiovascular disease or postpartum depression, that data shows us where to focus resources and support. For acute disease outbreaks, such as dengue or cholera, it can help inform urgent operational protocols and resource allocation. However, conventional [epidemiological surveillance is often hindered by limitations](https://www.nature.com/articles/s41586-024-08564-w) in data: multi-year reporting lags, data siloed by rigid geopolitical boundaries, and data sparsity. Even without these limitations, traditional modeling approaches require extensive task-specific data collection and custom data engineering pipelines that are difficult to deploy during rapid outbreaks or in resource-constrained settings. To address these systemic bottlenecks, we [introduce](https://blog.google/innovation-and-ai/technology/health/google-earth-ai/) a [new paradigm](https://arxiv.org/abs/2610.05699) in public health leveraging planetary geospatial foundation models. Using [Google Earth AI’](https://ai.google/earth-ai/)s [Population Dynamics Foundation Model](https://research.google/blog/insights-into-population-dynamics-a-foundation-model-for-geospatial-inference/) (PDFM) as a proof-of-concept, we demonstrate how self-supervised, pre-trained representations of "place" can be integrated directly into existing health sciences and epidemiological workflows as plug-and-play inputs — enhancing the statistical and machine learning (ML) models epidemiologists already use, rather than building new pipelines from scratch. PDFM compresses privacy-preserving search trends, human mobility, built-environment density, and environmental determinants into location embeddings. Without requiring task-specific fine-tuning, these off-the-shelf location embeddings matched or improved on conventional inputs across a wide variety of disease domains, geographic settings, and epidemiological tasks. Part of Google Earth AI — our suite of geospatial models connecting satellite imagery, weather, anonymous search trends, human mobility, and other population dynamics — PDFM uses self-supervised learning to synthesize the following diverse, privacy-preserving signals into compact, versatile embeddings that serve as “fingerprints” for locations refreshed at a monthly cadence: Rather than requiring researchers to collect and process these raw data streams themselves, PDFM embeddings can be easily plugged into existing ML workflows to provide ready-to-use geospatial context. [Prior work](https://arxiv.org/abs/2411.07207) showed that these embeddings are task-agnostic. Because these everyday signals capture the underlying social, behavioral, and environmental determinants of health, the same embeddings showed strong performance on filling gaps in [a wide variety of CDC health metrics](https://www.cdc.gov/places/index.html). However, establishing a new paradigm for global health carries a higher burden of proof. To meet this standard, we set out to demonstrate the value of PDFM embeddings across a wider variety of health challenges, and in different environments across the globe. To achieve this, our global health research partners facilitated independent evaluations across five distinct public health challenges — spanning diverse epidemiological tasks, resource settings, and disease types: Our evaluations show that while epidemiological models are constrained by sovereign borders, the outcomes they track are not. For example, in the 146 U.S. counties situated within 150 km of the Canadian border, domestic-only models often struggle to predict local vaccine uptake. By supplementing U.S. county embeddings with [Canadian Forward Sortation Area embeddings](https://www150.statcan.gc.ca/n1/en/catalogue/92-179-X), researchers at the [Mount Sinai Health System](https://icahn.mssm.edu/) and [Boston Children’s Hospital](https://www.childrenshospital.org/) developed models that captured cross-border behavioral and mobility spillovers. To help public health officials pinpoint communities at risk of measles outbreaks, the researchers used this added cross-border context to increase the share of variation in MMR vaccination coverage explained by the models from 16% to 22% (a 36% increase) and refine coverage estimates by at least 3 percentage points for 4.7 million border residents, revealing local patterns that domestic-only models may miss. Cardiovascular disease (CVD) [claims over 916,000 lives annually in the U.S.](https://www.ahajournals.org/doi/epub/10.1161/CIR.0000000000001412) However, unsuppressed official county-level mortality data from the [National Vital Statistics System](https://www.cdc.gov/nchs/nvss/deaths.htm) (NVSS) typically lags by 1–2 years, and [American Community Survey](https://www.census.gov/programs-surveys/acs.html) (ACS) covariates reflect conditions up to 2–3 years in the past. PDFM is available in more places and has much less of a lag, allowing for the ability to improve our capacity to model disease. Our partners at [NYU Grossman School of Medicine](https://med.nyu.edu/) tested whether PDFM could stand in for these census-based inputs when estimating current-year CVD deaths across roughly 3,100 U.S. counties. They found no statistically significant differences between PDFM and traditional census data on nowcasting, with PDFM being much fresher and available in far more places: These results suggest that PDFM can enable health departments to guide prevention resources using current conditions rather than waiting on multi-year survey cycles. Time is especially of the essence when addressing outbreaks of vector-borne diseases like dengue. In partnership with public health researchers at the [University of Oxford](https://www.ox.ac.uk/) and [Tecnológico de Monterrey](https://www.tecsalud.mx/), we coupled PDFM with [TimesFM 2.0](https://research.google/blog/a-decoder-only-foundation-model-for-time-series-forecasting/) (our open time-series foundation model) to forecast dengue case counts across ~2,450 Mexican municipalities between 2020 and 2025. The model’s greatest performance gains occurred when predicting one month ahead, improving forecast accuracy in up to 72% of active dengue transmission municipalities, yielding total error reductions 3.4X larger than degradations. This performance occurred precisely where timely vector control and clinical staffing decisions matter most, especially considering the [demonstrated utility](https://www.nature.com/articles/srep33707) of short term forecasts. Screening for maternal mental health conditions like postpartum depression (PPD) typically relies on clinical intake information, which rarely captures the broader community conditions that shape a mother’s risk and access to care. To test whether geospatial foundation models can help bridge this gap, researchers at the [University of Washington](https://www.washington.edu/) evaluated PDFM embeddings across 332,970 participants in the [CDC PRAMS](https://www.cdc.gov/prams/index.html) national survey. In addition to PDFM capturing community-level socioeconomic conditions (R2=0.45), adding the embeddings provided a consistent, statistically significant boost to predicting which mothers were at risk (AUC +0.0020 in seen states; +0.0038 in unseen states). Crucially, this signal held up in states the model had never seen during training—acting as complementary local context that recovers about 15% of the predictive signal of a mother’s own income and insurance records when those details are unavailable. This transferable context made the biggest difference in how screening resources are directed in new states. In simulations where a health system can follow up with the 20% highest-risk mothers, adding PDFM helped reach 5,640 more rural mothers with postpartum depression each year. Alternatively, in systems aiming to catch 80% of all cases, PDFM cut 17,723 false alarms annually—showing how geospatial embeddings can help health systems either broaden rural outreach or improve follow-up efficiency. Early warning for waterborne epidemics like cholera is critical for prepositioning oral cholera vaccines and clean water supplies. Fortunately, outbreaks are rare: in any given week, fewer than 1 in 100 of the country's 403 health zones sees one begin. Unfortunately, this very same rarity makes them hard to anticipate. To address a use case defined by the [World Health Organization Regional Office for Africa](https://afro.who.int/) using national surveillance data from the Democratic Republic of the Congo’s Integrated Disease Surveillance and Response (IDSR) reporting, we tested whether a lightweight, low-resource version of PDFM (adapted for regions with sparse internet connectivity) could help forecast cholera hotspots. The benefit depended on how far ahead we looked. One or two weeks out, recent case counts told most of the story and PDFM did not significantly enhance the accuracy. Four to eight weeks out, when there is still time to move supplies, it helped produce: These findings demonstrate that while short-term tracking can rely on recent clinical data, foundation model embeddings capture underlying environmental, connectivity, and population determinants that supplement historical data, allowing for proactive planning one to two months in advance. Geospatial foundation models enable moving from reactive, localized modeling to proactive and time-sensitive health intelligence at planetary scale. Current limitations, such as static snapshots, are driving active research into temporally dynamic embeddings and geographic transfer learning for under-connected regions. By integrating planetary contextual data into existing public health workflows, we can help take steps to improve how healthcare resources, interventions, and outbreak alerts reach the communities that need them most. Read the [paper](https://arxiv.org/abs/2610.05699) for more details. PDFM embeddings are [commercially available](https://mapsplatform.google.com/lp/geospatial-analytics-signup/) in Preview as [Population Dynamics Insights](https://mapsplatform.google.com/resources/blog/from-static-maps-to-geospatial-ai-announcing-population-dynamics-insights/?utm_experiment=13103223), a geospatial embeddings dataset from Google Maps Platform. Academics and public health researchers can also [request](https://docs.google.com/forms/d/e/1FAIpQLSeap_CWON0qG82ht8O4dK_2_ffwrAC4HgHBXLqAg3m54qp4Fw/viewform?usp=dialog) no-cost access for select, non-operational research use cases.