Researching and applying the potential of Artificial Intelligence to foster climate resilience at the regional and local levels

Expected Outcome: AI has the potential to support or facilitate virtually every aspect of regions’ climate adaptation efforts. From risk assessment to climate forecasting, from infrastructure planning to resource management, and more . Contributing to the objectives of the AI Continent Action Plan and the EU Mission on Adaptation to Climate Change, projects are expected to contribute one of the following: AI is used to improve actionable climate adaptation knowledge for European regions and local authorities by integrating climate data into decision-making processes. Specific sectors in the selected regions become more resilient to climate change thanks to the use of AI to improve their processes or technologies, while simultaneously advancing their digital transformation. Scope: Rationale Over the past few years, a rapid and disruptive acceleration of progress in Artificial Intelligence has occurred, driven by significant advances in widespread data availability, computing power and machine learning [1] . While the potential of Artificial Intelligence is being uncovered each day and its possibilities are expanding exponentially, such technological revolution can significantly accelerate Europe’s efforts towards climate resilience and contribute to the objectives of the Adaptation Mission. Considering the ever-changing nature of AI growth, proposals should demonstrate that they go beyond state of the art, they should identify a specific gap that can be addressed by an AI-powered tool, and explain why it would improve existing models, tools or applications or, alternatively, justify the need to develop entirely new solutions. In particular, the proposal should address one of the following two objectives: Objective 1: “ AI for more accessible data ” AI can quickly process and identify patterns from big datasets that would otherwise be too complex. Proposals should explore how AI can be further integrated, including via AI techniques such as deep learning, to make data more accessible and understandable, to facilitate informed decision-making by regions and local authorities. Improvements should be tested with at least 3 region al and local authorities to ensure that they provide a concrete added value to end-users (i.e. decision-makers). Moreover, proposals are expected to apply such analysis to concretely improve data integrity and accessibility [2] . Objective 2: “ AI for sectoral adaptation ” Use and application of machine learning and AI tools to help regions and local authorities optimize their management of resources and improve adaptation technologi…