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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
Our proposed project is a Wildfire Visualization Platform aimed at supporting Incident Management Teams (IMTs) in wildfire management and response. The platform is developed in direct response to the 2023 Call for Proposals (CFP) from the Canadian Safety and Security Program (CSSP), with CRIM (Computer Research Institute of Montreal) acting as our external stakeholder, providing technical support and resources. The platform will provide real-time, data-driven insights by consolidating data from multiple sources into a user-friendly interface, enabling faster, informed decision-making. By integrating data from satellite imagery, radar, weather, and topographical maps, the platform will deliver a comprehensive view of wildfire conditions, addressing the specific needs outlined in the CSSP's CFP.
- Predictive Modeling: Utilizes algorithms to forecast fire behavior based on weather, terrain, and vegetation, helping commanders anticipate fire spread.
- Historical Replay Mode: Enables analysis of past wildfire events through reconstructed data, useful for both strategy review and training purposes.
- Wildfire Synthetic Environment (W-SE): A virtual environment for commanders to simulate wildfire scenarios, improving training and operational preparedness.
- Open Architecture for Interoperability: Ensures compatibility with existing systems used by wildfire agencies, promoting cross-jurisdiction collaboration.
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User-Friendly Interface: Intuitive design with visual tools like heatmaps and geospatial mapping to allow quick access to critical information.
The platform will be primarily used by IMT commanders to streamline decision-making during wildfire incidents. By integrating multiple data sources into one platform, it will enable faster response times and improve coordination. The platform will also benefit emergency coordinators, geospatial analysts, and field teams, ensuring all stakeholders have access to the same real-time data. Additionally, the Wildfire Synthetic Environment will enhance training, allowing for realistic simulations without real-world risks, ultimately improving preparedness and response capabilities across multiple agencies.
- The platform's predictive models and simulations go beyond current wildfire management tools by offering incident commanders automated recommendations for resource allocation and strategic decision-making.
- The inclusion of a "Wildfire Synthetic Environment" (W-SE) enables realistic training scenarios and "war games," a feature typically used in defense sectors, disrupting traditional wildfire management training practices.
- 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: IMT commanders may resist transitioning to a new system. To address this, the platform will offer an intuitive user interface, in-depth training modules, and strong support for legacy system compatibility.
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)
CRIM is a nonprofit organization specializing in advanced research and development of information technology. Their expertise lies in applying innovative technological solutions to public sector challenges, including disaster management. CRIM is committed to providing the technical support and resources needed to develop our platform.
Customer :
The Canadian Safety and Security Program (CSSP)
The CSSP is a federally-funded program which supports governmental bodies across Canada by supplying funding for innovative science and technology projects which aim to improve the safety and security of Canadians. One of the key aspects targeted by the CSSP is the “ability to anticipate, prevent, mitigate, prepare for, respond to, and recover from natural disasters” (“Canadian Safety and Security Program”).
Customer Interest :
The CSSP recently launched a number of new science and technology initiatives that aim to advance public safety and security and one of the domains targeted is “Wildfire intelligence”. Their desire is to have an open-source platform which can take data from a variety of sources, process it, and present this information to IMT commanders so that they could make informed decisions on how to deal with wildfires. This system would need to be usable by all wildfire agencies across Canada and it must strive to operate with existing systems.
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.