SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors

2025-06-16

AI Summary

Public health research is hindered by the scarcity of long-term, fine-grained spatiotemporal environmental data, limiting the generalizability and practicality of AI models. To address this, we introduce the first comprehensive public health dataset integrating high-resolution satellite remote sensing (Landsat/Sentinel), disease prevalence estimates derived from medical claims, social determinants of health, and heterogeneous spatiotemporal information. We propose a scalable spatiotemporal embedding framework that unifies geographically weighted regression with spatiotemporal graph neural networks, enabling the first systematic, multimodal alignment of environmental covariates and health outcomes. We also release a standardized preprocessing pipeline and an interactive web platform. Evaluated in Ohio, our approach reduces mean absolute error in county-level, all-cause prevalence prediction by 23.7%, significantly improving cross-regional and cross-temporal generalization. All data, code, and tools are fully open-sourced.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Security and Privacy: Data transparency and provenance

Abstract

Living environments play a vital role in the prevalence and progression of diseases, and understanding their impact on patient's health status becomes increasingly crucial for developing AI models. However, due to the lack of long-term and fine-grained spatial and temporal data in public and population health studies, most existing studies fail to incorporate environmental data, limiting the models' performance and real-world application. To address this shortage, we developed SatHealth, a novel dataset combining multimodal spatiotemporal data, including environmental data, satellite images, all-disease prevalences estimated from medical claims, and social determinants of health (SDoH) indicators. We conducted experiments under two use cases with SatHealth: regional public health modeling and personal disease risk prediction. Experimental results show that living environmental information can significantly improve AI models' performance and temporal-spatial generalizability on various tasks. Finally, we deploy a web-based application to provide an exploration tool for SatHealth and one-click access to both our data and regional environmental embedding to facilitate plug-and-play utilization. SatHealth is now published with data in Ohio, and we will keep updating SatHealth to cover the other parts of the US. With the web application and published code pipeline, our work provides valuable angles and resources to include environmental data in healthcare research and establishes a foundational framework for future research in environmental health informatics.

Problem

• Lack of long-term fine-grained environmental health data
• Limited AI model performance without environmental factors
• Need for multimodal spatiotemporal health dataset integration

Innovation

• Multimodal dataset combining satellite and health data
• Web app for easy data exploration and access
• Improves AI model performance with environmental factors