ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Social-Ecological Urban Systems
This article is part of the Research TopicHeat Stress and Public Health Issues: Impacts, Adaptation, and MitigationView all 10 articles
Toward AI-Enabled Urban Climate Twins: A Foundation-Model GeoAI Framework for Continuous Heat and Health Risk Mapping
Provisionally accepted
- Indiana University, Purdue University Indianapolis, Indianapolis, United States
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Urban heat represents one of the most critical and inequitable manifestations of climate change, with mounting impacts on human health, energy systems, and urban sustainability. This study introduces a multimodal Geospatial Artificial Intelligence (GeoAI) pipeline that fuses atmospheric, Earth observation, and socioeconomic data to generate hourly, super-resolved land-surface temperature (LST) estimates for urban heat and health-risk assessment. The pipeline integrates three complementary foundation models, Prithvi-WxC, Prithvi-EO, and Granite-LST, to capture interactions between atmospheric dynamics and surface morphology. Implemented over Indianapolis, Indiana, the system produces continuous temperature fields at 10–30 m resolution with sub-2 °C error, reproducing realistic diurnal heat-island dynamics. Beyond technical performance, the framework demonstrates how foundation-model fusion can bridge environmental monitoring and health analytics, offering a scalable tool for exposure mapping, early-warning systems, and equitable climate adaptation. This work establishes a reproducible blueprint for AI-enabled urban climate twins, advancing the integration of environmental intelligence into public health resilience planning. By fusing reanalysis, satellite, and socio-environmental data through large geospatial foundation models, this helps bridge the gap between observation and inference, enabling all-weather, continuous urban heat mapping.
Keywords: climate adaptation, Environmental Health, Explainable AI, Foundation models, geoAI, Land-surface temperature, reanalysis data (ERA5), Urban Heat Island
Received: 17 Dec 2025; Accepted: 29 Jan 2026.
Copyright: © 2026 Johnson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Daniel Patrick Johnson
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