Collaborative Research: FIRE-MODEL: Advanced AI Framework to Improve Understanding and Prediction of Wildland Fire

About This Grant

Understanding and predicting wildland fire are critical for effective response, resource allocation, and risk mitigation. However, this remains a major challenge due to the complexity of fire dynamics and the limitations of current data sources and modeling approaches. This project will develop innovative large-scale artificial intelligence (AI) and machine learning (ML) algorithms for the detection and forecasting of wildland fires using multimodal and longitudinal geoscientific data. The research team will address key computational challenges in large-scale geoscientific data mining. The project will develop and validate an advanced AI framework to curate geoscientific data, detect wildland fire, integrate multimodal data sources, enable longitudinal data-based forecasting, and support collaborative geoscientific data analysis and model learning. This project will generate broad societal and educational benefits through outreach, data dissemination, and curriculum development. It will produce an open-source, integrated dataset to support the broader research community in benchmarking and developing new methods. The project will also engage with different stakeholders, including federal, state, and local agencies, such as National Aeronautics and Space Administration (NASA) centers, the U.S. Forest Service, and the National Park Service, to ensure that the developed tools align with operational needs for fire tracking and management. The research objective of this project is to answer urgent needs in wildland fire research by developing a new advanced AI framework for geoscientific data analysis aimed at addressing important computational challenges through several key efforts. First, the team will integrate data from multiple platforms and modalities to create a comprehensive dataset that combines multi-instrument satellite observations from both geostationary (GEO) and low-Earth orbit (LEO) platforms, atmospheric reanalysis data, historical fire records, surface characteristics, and fuels information. Second, an advanced AI framework will be developed via fully utilizing the potentials of multimodal and longitudinal geoscientific datasets for wildland fire detection and forecasting. The proposed advanced AI framework includes: an interpretable multimodal transformer to integrate diverse data and ensure interpretability in fire-related feature extraction; incomplete multimodal learning model to leverage both GEO and LEO satellite observations during training and remain robust when only partial data are available during inference; a temporally structured deep learning model for future wildland fire forecasting; and a federated learning platform for collaborative geoscientific data analysis and AI model learning. The innovative integration of large-scale machine learning and data-intensive computing for heterogeneous geoscientific data mining holds great promise for wildland fire early forecasting. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.