Deployment Plan and Infrastructure

1. Project Description:

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.

Key Features:

• Real-time Data Integration: Aggregates live data from satellite, weather, and topographical sources to monitor wildfires and resource availability.
• 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.
• 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.

2. Objectives:

• Deploy the software to a production environment with a focus on stability, scalability, and real-time performance.
• Provide essential features for wildfire monitoring, simulation, and prediction.
• Ensure seamless integration with existing agency systems through open architecture.
• Provide cross-platform support (Windows, MacOS, Linux), with additional offline mode.
• Deliver a user-friendly interface that supports quick decision-making and real-time data visualization.
• Set up an automated deployment pipeline for future updates.

3. Deployment Phases:

3.1 Phase 2: Environment Setup

Load and Stress Testing: Simulate high-traffic conditions to evaluate system response under stress, especially during emergencies. Assess the system's ability to handle multiple simulations in W-SE while processing real-time data.

Automated Testing: Implement CI/CD pipelines (Github Actions) to automate testing of real-time data integration and geospatial mapping.

3.2 Phase 3: Deployment to Production and Subsequent Releases

Release Strategy:

Phase 1: Pre-Release Activities (Alpha Testing)

Alpha Release:

Release a closed beta version of the software to a select group of users, which may include CRIM data analysts (stakeholder), or other software engineering professionals within the CRIM.

Internal Testing:

Conduct internal Quality Assurance (QA) testing, checking for any bugs, and assuring the application flow is as intended. Furthermore, we will test the application with prepared datasets to ensure accuracy and effectiveness.

Alpha Objectives:

• Test data integration
• Gather feedback on usability, particularly in the context of the user-friendly interface and heatmap tools.
• Allow users to test the interoperability features with existing systems.
• Support: Provide dedicated technical support to address issues or bugs raised by alpha users, and ensure any mission-critical features are stable.

Phase 2: Pre-Release Activities (Beta Testing)

Beta Release:

Release a closed beta version of the software to more users, which may include CRIM data analysts and software engineers (stakeholders), peers (other students), and other software engineering professionals

Beta Objectives:

• Find and fix early bugs and issues within the code
• Ensure the flow of the UI and tools are to specification
• Focus on system performance under load, larger datasets, many users

Phase 3: Full Release

Full Release:

Release a full version of the Wildfire Visualization Project. Core features include:

• Data integration with a variety of data sources (STAC or WFS).
• Historical Replay Mode for reviewing past wildfire events.
• Wildfire Synthetic Environment (W-SE) for simulations and training.
• Open architecture for interoperability with existing systems.
• User-friendly interface with geospatial mapping and heatmaps.

4. Deployment:

(LOUIS CHANGES:)

Deploy a version of the Wildfire Visualization Project to the cloud using services such as AWS (TBD). Ensure that the software infrastructure can scale as needed to handle high traffic during wildfire emergencies, with cloud auto-scaling configured.

CI/CD -> Docker -> Backend -> DB -> Frontend

5. Risk Management:

Data Latency: Implement redundancy in data pipelines to ensure continuous real-time data flow from satellites and other data sources.

Scalability Concerns: Use auto-scaling features in NX Cloud to ensure seamless scaling during high-traffic periods, especially during wildfire seasons.

Security Threats: Perform regular security audits, including penetration testing, to safeguard sensitive wildfire and resource data.