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Dubois, Gabriel - ID: 40209252
Hilout, Yasmine - ID: 40214158
Fetanat, Ali - ID: 40158208
Frattolillo, Philip - ID: 40192245
Villemure, Louis - ID: 40210315
Wong, Samuel - ID: 40209013
Daigle, Liam - ID: 40207583
Keating, Kade - ID: 40166656
Cheng, Justin - ID: 40210279
Guertin, Xavier - ID: 40213525
Oliel, Eden - ID: 40211989
Our project is a Wildfire Visualization Platform designed to support data scientists at the Computer Research Institute of Montreal (CRIM) in the study and analysis of wildfire events. The platform consolidates data from multiple sources—including satellite imagery, radar, weather, and topographical maps—into a user-friendly interface that enables in-depth, data-driven exploration of wildfire dynamics. By focusing on retrospective analysis, the platform enables CRIM researchers to explore the progression and impact of past wildfires, identify patterns, and support advanced research into wildfire behaviour and contributing factors.
- Historical Replay Mode: Enables analysis of past wildfire events through reconstructed data, useful for identifying trends and understanding fire progression.
- Wildfire Synthetic Environment (W-SE): A virtual environment for simulating wildfire scenarios, supporting experimentation and development of new analytical techniques.
- Open Architecture for Interoperability: Designed for extensibility and integration with additional data sources, tools, and research systems.
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User-Friendly Interface: Intuitive design with visual tools like heatmaps and geospatial mapping for efficient exploration and analysis of complex wildfire data.
The platform will be primarily used by CRIM data scientists to study and analyze past wildfire events. By integrating multiple data sources into a single interface, it enables detailed exploration of fire behavior, contributing to wildfire modeling and research. The Historical Replay Mode and Wildfire Synthetic Environment support scenario analysis and experimentation, helping researchers uncover patterns and evaluate the effectiveness of predictive models. The platform’s extensible design also allows CRIM’s development team to continue enhancing its features to support evolving research needs.
The platform’s predictive models and simulations go beyond traditional wildfire research tools by enabling CRIM’s data scientists to analyze complex fire behavior and test hypotheses at scale. The inclusion of a Wildfire Synthetic Environment (W-SE) allows for the creation of realistic, data-driven scenarios—enabling experimentation, validation of models, and exploratory research. This approach brings simulation and analysis techniques commonly used in the defense sector into the wildfire research domain, introducing new possibilities for innovation and disruption.
- Data Inaccuracy: Relying on remote sensing and satellite data introduces the risk of incomplete or outdated information. We mitigate this by incorporating multiple data sources and backup systems to verify data accuracy.
- Interoperability Issues: Ensuring our platform integrates with existing systems across jurisdictions can be complex. To mitigate this, we will follow open standards and develop flexible APIs.
- Browser Compatibility: Since this platform must be used by diverse teams across various jurisdictions, compatibility with different browsers is essential. To mitigate this, we will perform thorough cross-browser testing and ensure the platform is optimized for widely-used browsers (e.g., Chrome, Firefox, Edge, Safari) to guarantee accessibility and performance consistency.
- Big Data Handling: The platform will be required to process and manage vast amounts of data from diverse sources (e.g., satellite imagery, weather data, radar). This introduces risks related to latency, data storage, and processing power. We plan to mitigate this by using cloud-based solutions for scalable data storage and processing, as well as optimized algorithms to ensure real-time data processing.
- User Adoption: CRIM’s data scientists will benefit from an intuitive interface, clear documentation, and onboarding materials. The platform is designed for easy extension, allowing CRIM’s developers to enhance it after project handoff.
Search terms: list the terms you used in your search
- wildfire monitoring
- wildfire management
- wildfire management software
- data visualization wildfire
- natural disaster predictions
- Canadian Safety and Security Program
- remote sensing data
- wildfire forecasting
Number of pages examined: 27
OroraTech
OroraTech offers wildfire detection and monitoring using satellite-based thermal imagery to alert authorities and track wildfires in real-time.
Novelty: While OroraTech focuses on satellite remote sensing, our platform integrates multiple data sources, including meteorological and topographical data, with predictive modeling and training simulations for a more comprehensive approach to wildfire management.
Esri
Esri provides GIS-based solutions for wildfire management, offering spatial data visualization and fire behavior modeling to assist in operational planning.
Novelty: Our platform expands on Esri’s GIS capabilities by integrating real-time data from diverse sources and incorporating interoperability with existing wildfire systems, as well as a synthetic training environment for wildfire commanders.
Technosylva
Technosylva’s Wildfire Analyst™ predicts fire spread and offers risk assessments using weather data and terrain analysis for decision-making in real-time.
Novelty: While Technosylva excels in fire spread prediction, our platform adds real-time data integration, synthetic training environments, and predictive tools that focus on both active fire management and long-term training, offering a broader scope.
Company :
The Computer Research Institute of Montreal (CRIM)
The Computer Research Institute of Montreal (CRIM) is a nonprofit organization that specializes in applied research and the development of cutting-edge information technologies. With a strong track record of collaborating on innovative projects in the public interest, CRIM brings deep expertise in data processing, geospatial analysis, and disaster-related research. Their team of data scientists will use the Wildfire Visualization Platform to conduct in-depth analyses of past wildfire events, enabling new insights into wildfire behavior, patterns, and risk factors.
CRIM is also providing the technical support and infrastructure needed to develop the platform, and their development team plans to continue building on and improving the application after it is handed off at the conclusion of the project. This ensures the long-term sustainability and adaptability of the platform for future research needs.
Homepage | OroraTech, https://ororatech.com/. Accessed 13 September 2024.
CRIM | Computer Research Institute of Montreal, https://www.crim.ca/en/. Accessed 11 September 2024.
“Apply for funding 2024.” Science, 3 June 2024, https://science.gc.ca/site/science/en/canadian-safety-and-security-program/call-proposals-2024#1. Accessed 12 September 2024.
“Canadian Safety and Security Program.” Science, 24 July 2024, https://science.gc.ca/site/science/en/canadian-safety-and-security-program. Accessed 11 September 2024.
“Wildfire Analyst.” Technosylva, https://technosylva.com/products/wildfire-analyst/. Accessed 13 September 2024.
“Wildfire Software | GIS for Wildland Fire Mapping and Analysis.” Esri, https://www.esri.com/en-us/industries/wildland-fire/overview. Accessed 13 September 2024.