PLANET: hyPer Local climAte driven Tool
Climate change, with its accompanying global warming and increasingly severe weather conditions, presents substantial risks to agricultural sustainability worldwide. This is driving the agri-food sector to adapt through the use of new technologies and structural changes to production. Adaptation strategies include everything from the introduction of weather-resistant crops and new livestock breeds to altering crop rotation patterns and building protective structures for livestock. Despite these efforts, the complexities of climate change mean the impacts vary across regions and over time. Policymakers are also working to support the sector, but challenges such as a lack of observational data on agricultural climate shocks, insufficient stakeholder participation in policy-making, and slow policy performance evaluations contribute to suboptimal policy formulation. Given this context, the immediate development of methodologies and tools that facilitate better climate adaptation decision-making for all agri-stakeholders is vital. Addressing this need, the PLANET project proposes to harness technologies such as Artificial Intelligence, Big Data Analytics, and the wealth of geospatial earth observation data from the EU Copernicus and Eumetsat Agencies. The objective is to create an intelligent, automated tool that provides on-demand, hyperlocal climate-driven land-use suitability services.
What is the challenge that is being addressed?
We are addressing the Climate Risk assessments with a Decision Support Tool for Land suitability: A process for evaluating the potential impacts of climate change on a particular region, sector, or activity.
What is the AI solution the project has implemented?
A Generative Adversarial Network that downscales the climate information and a feedforward neural network that classifies the land suitability for specific crops based on the refined input data.
Who helped implement the AI solution?
This solution is implemented in the context of PLANET, a winning project from the AI4Copernicus 3rd Open Call, by the Neuralio A.I. company. The consortium consists of [company] and [company], while the results of the project are available here.