AI4Land is a project that uses artificial intelligence to generate detailed digital maps of Earth’s landscapes and plants. By combining satellite photos and environmental data, the project creates high-resolution representations of how land and ecosystems are structured today and how they can change in the future. These maps help climate scientists understand how the Earth’s surface evolves over time and how it influences future climate scenarios.

Problem

Many climate studies still use static or low-resolution maps of the Earth’s surface. These maps fail to portray how landscapes and plants change over time, and they frequently lack the precision required to adequately represent real-world situations. As a result, climate models and carbon cycle forecasts contain significant uncertainty. Without precise and dynamic land information, it is difficult to estimate future climate scenarios and develop effective environmental and mitigation solutions.

Solution

AI4Land addresses this issue by using artificial intelligence to turn low-resolution environmental data into high-definition digital twins of the Earth’s surface. The project collects and standardizes data from satellite images, soil parameters, and global climate scenarios, and merges it into a unified digital library designed for large-scale computation. AI models are trained to identify the complex patterns that cause changes in landscapes and vegetation over time. Using this data, the system reconstructs past land conditions and predicts how land use and ecosystems will change in the future, allowing scientists to improve climate models and environmental strategies.

Success Story: 

• Industry: Climate Research & High-Performance Computing (HPC).
• Results: AI4Land successfully generated the first global land-use digital twins at 1 km resolution, achieving over 94% accuracy in reconstructing complex landscape patterns worldwide.
• Impact: These high-resolution digital twins are being integrated into climate models and are expected to significantly improve the accuracy of future climate projections, supporting better environmental planning and policy design.