arXiv:2606.11793v1 Announce Type: new 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
The increasing availability of high-resolution satellite data and advancements in deep learning make such large-scale land use reconstruction feasible and necessary for refined climate models.
Accurate, high-resolution land use data is crucial for improving climate projections, assessing the terrestrial carbon cycle, and informing policy decisions related to land management and sustainable development.
The ability to generate granular, historical, and predictive land use models using AI will significantly reduce uncertainty in climate science and enhance our capacity for environmental planning.
- · Climate scientists
- · Environmental policy makers
- · Agricultural technology companies
- · Earth system model developers
- · Traditional, less data-intensive land use modeling approaches
- · Sectors reliant on outdated or less precise land data
Improved accuracy in carbon cycle modeling and climate change impact assessments.
Better informed land management policies, potentially leading to more efficient resource allocation and conservation efforts.
New markets for AI-driven environmental intelligence and predictive analytics across various industries.
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Read at arXiv cs.LG