Dataset and Results for: Multimodal Fusion of Remote Sensing and Agricultural Data for High-Resolution Life Expectancy Prediction

This repository contains the data and machine learning outputs accompanying the manuscript: "Multimodal Fusion of Remote Sensing and Agricultural Data for High-Resolution Life Expectancy Prediction: A County-Level Analysis Across the Continental United States." Repository Contents: full_clean_engineered_dataset_with_LE.csv.zip: The primary multimodal dataset containing 62,160 county-year observations spanning 3,108 CONUS counties from 2000–2019. It integrates 450 physically sensed and agriculturally derived features from 11 data streams (MODIS, Sentinel-1/2, Landsat, USDA CDL, ESA CCI soil moisture, JRC, Copernicus DEM, and FAO livestock densities) alongside IHME life expectancy targets. Archive.zip: Contains the final production machine learning outputs, including the 5-fold cross-validation predictions, spatial error metrics, and exact SHAP (SHapley Additive exPlanations) feature importance scores used to generate the manuscript's figures and tables. Code Availability: The Google Earth Engine extraction scripts and Python machine learning pipelines used to generate and process this data are available on GitHub at: https://github.com/albertfaiz/Multimodal_geo_fusion_FM