Project description
AI to uncover drivers of forest fires
Understanding the causes of increasing forest fires is essential for effective regional risk management. While advances in satellite monitoring provide accurate fire maps, they do not reveal the underlying drivers, leaving potential insights largely untapped. With the support of the Marie Skłodowska-Curie Actions programme, the ForestFireAI project will use multi-source and multi-temporal Earth Observation data to develop AI algorithms for estimating the causes of forest fires. It will create a benchmark dataset of European forest fire drivers, serving as ground truth for evaluating these algorithms. The project will enhance spatial resolution and temporal accuracy through AI techniques while establishing efficient methods to identify drivers such as human activities, high temperatures, fuel availability, and dryness.
Objective
With the increasing frequency and intensity of forest fires, it is essential to better understand the drivers causing them. Identifying forest fire drivers offer valuable insights that can enhance our comprehension of forest fire variability and guide targeted regional risk management strategies. Recent advancements in satellite remote sensing and machine learning data processing techniques have significantly improved fire monitoring. However, while these efforts have resulted in accurate fire maps, they do not provide information about the underlying causes. Consequently, the full potential of Earth Observation data, along with advanced data processing and modelling techniques for studying the forest fire drivers, remains largely unexplored. The ForestFireAI project aims to leverage the availability of multi-source and multi-temporal Earth Observation data to propose new AI algorithms for estimating forest fire drivers. This includes creating a benchmark dataset of forest fire drivers in Europe, which will serve as a ground truth data for evaluating developed advanced AI algorithms. Moreover, the project will focus on developing AI techniques to improve the spatial resolution of data, use multi-source data and their temporal resolution, and establish efficient processing schemes for detecting forest fire drivers, such as human activities, high temperature, fuel, and dryness. To ensure the reliability, efficiency, and scalability of the developed algorithms, uncertainty-aware, explainable, and hybrid physical/data-driven techniques will be incorporated. Through this multidisciplinary approach—bringing together expertise in remote sensing, computer science, and forest ecology—ForestFireAI will take important steps toward developing the algorithms necessary for better understanding forest fire drivers. This knowledge could contribute in reducing the risk of extreme forest fires and will accelerate the advancement of Dr Benyamin Hosseiny’s research.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology mechanical engineering vehicle engineering aerospace engineering satellite technology
- engineering and technology environmental engineering remote sensing
- social sciences sociology governance crisis management
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences data science data processing
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2024-PF-01
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EX4 4QJ Exeter
United Kingdom
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