Global 1 km Reconstruction of Historical and Future Land Use with Machine Learning

Land use change is a significant source of anthropogenic carbon emissions, making it a critical yet often underrepresented component in climate projections. As next-generation Earth System Models move toward kilometer-scale resolutions to capture fine-scale land-atmosphere interactions, existing land use projections (typically provided at ≈30 km resolution) are insufficient to represent the spatial heterogeneity these models require.

Relying on coarse datasets can result in a loss of 31–54% of spatial information, introducing substantial biases in simulated terrestrial carbon sequestration and surface fluxes. To address this, we present a deep learning framework designed to downscale coarse Land-Use Harmonization 2 (LUH2) data into high-resolution 1 km mosaics covering the historical and future period from 1850 to 2100.

Our methodology employs a U-Net architecture to integrate transient anthropogenic drivers from LUH2, high-resolution environmental conditions using Köppen-Geiger climate classifications, and high-resolution population density with a suite of high-resolution static geophysical features (elevation, 2D depth-weighted soil composition, terrain characteristics). 

A key technical advancement is our distributed inference pipeline using Gaussian-weighted patch aggregation. By normalizing overlapping predictions, this approach eliminates blockiness and edge artifacts, ensuring seamless global transitions across the 1 km mosaic. Validation against the HILDA+ dataset demonstrates high fidelity, achieving a global accuracy of 94.5% and a mean Intersection over Union (mIoU) of 0.799 for primary land use classes. These results provide a continuous boundary condition that enhances the realism of carbon, water, and energy fluxes in next-generation climate simulations and digital twin infrastructures.