Our group, WIldfire REsearch - Analytics Initiative (WIRE-AI), has been working on an NSERC Alliance Mission project since 2022. Our goal is to assist forest fire researchers and practitioners with machine learning/deep learning (ML/DL) models, tools, frameworks, and advanced datasets. These resources enable the application of data-driven techniques to model forest fire growth, predict forest fire risk, and estimate burnt areas. Our research objectives also include the use of IoT-based sensors, drones, crowd-sourced data, and state-of-the-art communication technologies for remote forest monitoring, forest fire suppression planning, and ensuring public safety.

We are a team of developers, data scientists/engineers, machine learning engineers and simulation modellers focused on building open-source tools for geospatial analysis and machine learning. The collaborative team is from University of Waterloo, Dalhousie University, and Carleton University along with our indestrial partners from Cistel Technology and Hegyi Geomatics.

• 🤖 Federate Learning Framework: A federated learning framework for forest fires across Canada.
• 🌳 Vegetation Land Cover: Visual insights into the Canadian land cover data over the years.
• ⛽ Forest Fuel Types: Heuristics and visual insights into the canadian forest fuel type

We welcome issues, feature requests, and pull requests.

To reach us and for questions, email us at mutakabbir@cmail.carleton.ca.