Fig. 2: 1 km × 1 km resolution prediction of many tasks across the continental US using daytime images processed once, before tasks were chosen.

100,000 daytime images were each converted to 8,192 features and stored. Seven tasks were then selected based on coverage and diversity. Predictions were generated for each task using the same procedure. Left maps: 80,000 observations used for training and validation, aggregated up to 20 km × 20 km cells for display. Right maps: concatenated validation set estimates from 5-fold cross-validation for the same 80,000 grid cells (observations are never used to generate their own prediction), identically aggregated for display. Scatters: Validation set estimates (vertical axis) vs. ground truth (horizontal axis); each point is a ~1 km × 1 km grid cell. Black line is at 45^∘. Test-set and validation set performance are essentially identical (Supplementary Table 2); validation set values are shown for display purposes only, as there are more observations. The tasks in the top three rows are uniformly sampled across space, the tasks in the bottom four rows are sampled using population weights (Supplementary Note 2.1); grey areas were not sampled in the experiment.