AI-Powered Wildfire Prediction & Mitigation Project

Project Overview

We’re building a machine learning application that uses satellite imagery, sensor networks, and weather data to enhance wildfire detection and mitigation capabilities. Our goal is to reduce wildfire detection time by 40% and improve prediction accuracy by 30% through AI-enhanced detection systems, spread prediction, and resource optimization.

Scope → Community → Team → Research → Ideate

Who / What / Initial Concept and Idea

  • Who: Wildfire management agencies, local communities in fire-prone regions, emergency responders
  • What: AI-powered wildfire detection and prediction system using multi-source data integration
  • Initial Concept: Create a system that combines satellite imagery, ground sensor data, and weather models to detect wildfires earlier and predict their spread with greater accuracy

Key Questions to Answer:

  1. How can we reduce the latency in satellite-based fire detection systems?
  2. What methods can improve detection through smoke and cloud cover?
  3. How can we integrate data from multiple sources (satellites, ground sensors, UAVs) effectively?
  4. What machine learning techniques will best predict fire spread based on terrain and environmental conditions?

Establish User Stories / Visuals of UIs

User Stories:

  1. As a fire manager, I want early detection of potential wildfires so I can deploy resources before fires become unmanageable
  2. As an emergency coordinator, I want accurate fire spread predictions to plan evacuations effectively
  3. As a firefighting team leader, I want optimal resource allocation recommendations based on fire behavior
  4. As a local resident, I want real-time notifications about nearby fire risks
  5. As a city planner, I want historical fire pattern data to improve community preparedness

UI Mockups Needed:

  • Real-time fire detection dashboard with satellite and ground sensor integration
  • Fire spread prediction visualization with time-based projections
  • Resource allocation map showing optimal positioning of firefighting assets
  • Mobile alert application for community notifications
  • Historical fire pattern analysis interface

API Endpoints that Correspond to User Stories

Proposed Endpoints:

  1. /api/detect-fires - GET - Returns current fire detections from satellite and sensor data
  2. /api/predict-spread - POST - Takes fire location and returns predicted spread patterns
  3. /api/historical-data - GET - Returns historical fire patterns for specified regions
  4. /api/resource-optimization - POST - Recommends optimal resource allocation
  5. /api/sensor-status - GET - Returns status of ground-based sensors and cameras
  6. /api/alert-zones - GET/POST - Manages community notification zones

Database Model / Draw.io Diagrams to Support APIs

Database Tables:

  1. satellite_data - Store processed satellite imagery and fire detections
  2. sensor_readings - Store ground sensor network data
  3. weather_conditions - Store real-time and forecast weather data
  4. terrain_data - Store topographical information for fire spread modeling
  5. historical_fires - Store information about past wildfire events
  6. resources - Store information about available firefighting resources

Relationships:

  • Satellite_data has many fire_detections
  • Sensors have many sensor_readings
  • Fire_detections connect to weather_conditions
  • Fire_spread_predictions reference terrain_data
  • Draw.io diagram needed to visualize these relationships

Machine Learning or Other Key Technical Features

ML Components:

  1. Enhanced Satellite Imagery Processing:
    • Super-resolution techniques for improving satellite image quality
    • Transfer learning for detecting fires through smoke/clouds
    • Multi-spectral image analysis for early fire signature detection
  2. Fire Spread Prediction Model:
    • Graph Neural Networks to model topographical fire spread
    • Reinforcement Learning for dynamic fire behavior prediction
    • Integration of real-time weather data to adjust predictions
  3. Resource Allocation Optimization:
    • Multi-agent reinforcement learning for coordinating firefighting resources
    • Predictive modeling of containment effectiveness
    • Optimization algorithms for personnel and equipment deployment

Technical Requirements:

  • Real-time data integration pipeline
  • Edge computing for ground sensor networks
  • Cloud-based high-performance computing for ML model training
  • Mobile notification system architecture

Repository Preparations (1 Point)

  • Fork from our Disaster Response Framework repository
  • Already includes:
    • Data visualization components
    • Basic alert notification system
    • Sensor data integration API
    • Geospatial analysis tools

Additional Components to Add:

  • Set up satellite imagery processing pipeline
  • Configure ML model training infrastructure
  • Implement real-time data fusion architecture
  • Add reinforcement learning simulation environment

Titanic to Pilot City Tinker (1 Point)

0.80 Part 1

  • Adapt our existing Titanic survival prediction model framework for fire detection
  • Create frontend UI where users can input location parameters
  • Develop “What’s your wildfire risk?” interactive tool
  • Use transfer learning to adapt the prediction model to fire detection
  • Implement basic integration with satellite data sources

0.90 or Greater, Part 2

  • Integrate real multi-source data (satellite, sensors, weather)
  • Develop complete API structure for fire detection and prediction
  • Connect to wildfire simulation environment for ML training
  • Create meaningful frontend: “How much faster can we detect fires with our system?”
  • Implement comparative visualization showing traditional vs. AI-enhanced detection
  • Add resource allocation optimization simulation

Action Items

  1. Set up project repository with initial structure
  2. Collect and analyze sample satellite imagery and sensor data
  3. Create initial ML model prototype based on adapted framework
  4. Design database schema and API endpoints
  5. Build basic UI mockups for frontend dashboards
  6. Schedule weekly team sync meetings
  7. Establish metrics for measuring the 40% detection time reduction goal

Team Assignments

  • Data Science Lead: Rohan Bojja
  • Backend Developer: Aarush Gowda
  • Frontend Developer: Vasanth Rajasekaran
  • ML Engineer: Nikhil Maturi
  • QA/Testing: Nathan Tejidor
  • Project Manager: Pranav Santhosh