Scalable Deep Learning Framework for Global High-Resolution Land Use Reconstruction

• URL: http://arxiv.org/abs/2606.11793v2
• Date: Thu, 11 Jun 2026 10:27:02 GMT
• Title: Scalable Deep Learning Framework for Global High-Resolution Land Use Reconstruction
• Abstract summary: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections.<n>We present a data-driven framework AI4Land for generating high-resolution historical reconstructions.<n>By delivering realistic and evolving land surface conditions on demand, this work aims to reduce critical uncertainties and improve the predictive power of next-generation climate simulations.
• Score: 1.6329256865831512
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models. To address this limitation, we present a data-driven framework AI4Land, for generating high-resolution historical reconstructions and future projections of key land surface variables. The framework follows a two-phase approach using a U-Net architecture. In the first phase, which is the focus of this work, it reconstructs annual land use and land cover by integrating coarse-resolution scenario data with static geophysical features. In a planned second phase, the resulting high-resolution maps will be used to predict dynamic biophysical variables, particularly leaf area index, at finer temporal scales. Trained on Earth observation data, the models learn to reproduce spatially explicit and physically consistent land surface patterns, extending temporal coverage to periods lacking direct observations. AI4Land was developed and trained on MareNostrum5, demonstrating how GPU-accelerated HPC infrastructure enables global-scale climate AI pipelines. The final product is a suite of open-source emulators designed for real-time coupling with digital twin platforms, such as those developed under the Destination Earth initiative. By delivering realistic and evolving land surface conditions on demand, this work aims to reduce critical uncertainties and improve the predictive power of next-generation climate simulations.

Related papers

• Earth-o1: A Grid-free Observation-native Atmospheric World Model [81.55899748753434]
  We present Earth-o1, an observation-native atmospheric world model that overcomes structural limitations.<n>By integrating diverse sensor inputs into a unified, grid-free dynamical field, the model autonomously advances the atmospheric state in space and time.<n>We show that this fundamentally distinct paradigm enables direct, real-time forecasting and cross-sensor inference without the overhead of explicit numerical solvers.
  arXiv  Detail & Related papers  (2026-05-07T14:27:48Z)
• StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions [8.261844935991348]
  Traditional impact models struggle with spatial generalization due to limited observations and concept drift.<n>We introduce StefaLand, a generative earth foundation model centered on landscape interactions.<n>To our knowledge, this is the first geoscience land-surface foundation model that demonstrably improves dynamic land-surface interaction predictions.
  arXiv  Detail & Related papers  (2025-09-22T16:05:45Z)
• Detection and Simulation of Urban Heat Islands Using a Fine-Tuned Geospatial Foundation Model [0.0]
  This study fine-tunes a geospatial foundation model to predict urban land surface temperatures under future climate scenarios.<n>The model achieved pixel-wise downscaling errors below 1.74 degC and aligned with ground truth patterns, demonstrating an extrapolation capacity up to 3.62 degC.
  arXiv  Detail & Related papers  (2025-09-20T10:41:33Z)
• Geospatial Diffusion for Land Cover Imperviousness Change Forecasting [0.0]
  We propose a new paradigm exploiting Generative AI (GenAI) for land cover change forecasting by framing LULC forecasting as a data problem conditioned on historical and auxiliary data-sources.<n>We train a diffusion model for decadal forecasting of imperviousness and compare its performance to a baseline that assumes no change at all.<n> evaluation across 12 metropolitan areas for a year held-out during training indicate that for average resolutions $geq 0.7times0.7km2$ our model yields MAE lower than such a baseline.
  arXiv  Detail & Related papers  (2025-08-14T13:45:10Z)
• CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer [47.65152457550307]
  We propose the geometric-inspired Circular Transformer (CirT) to model the cyclic characteristic of the graticule.<n>Experiments on the Earth Reanalysis 5 (ERA5) reanalysis dataset demonstrate our model yields a significant improvement over the advanced data-driven models.
  arXiv  Detail & Related papers  (2025-02-27T04:26:23Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [27.60569643222878]
  We propose Earthformer, a space-time Transformer for Earth system forecasting. The Transformer is based on a generic, flexible and efficient space-time attention block, named Cuboid Attention. Experiments on two real-world benchmarks about precipitation nowcasting and El Nino/Southerntemporaltion show Earthformer achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-07-12T20:52:26Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.