InfernoWatchAI Specification

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InfernoWatchAI Specification

From Orbit to Alert in Seconds.

Project Identity

InfernoWatchAI is an open-source, AI-powered wildfire detection and intelligence platform designed for the United States.

InfernoWatchAI combines satellite observation, artificial intelligence, multispectral analysis, environmental intelligence, geospatial processing, sensor fusion, predictive modeling, public alerts, and open data services into a modular platform for detecting, analyzing, monitoring, and forecasting wildfire activity.

The platform is designed for emergency organizations, researchers, governments, communities, developers, universities, citizen scientists, and other authorized users who need timely wildfire intelligence.

InfernoWatchAI is designed as a modular system so that satellite providers, AI models, sensors, datasets, forecasting methods, alert systems, and visualization components can be added or replaced without requiring the entire platform to be redesigned.

Design Principles

Open-source development
Modular architecture
Interoperable data sources
Vendor-neutral integrations
Transparent detection methodology
Explainable confidence scoring
Human verification where appropriate
Reproducible research workflows
Privacy-conscious data handling
Geographic scalability
Real-time processing where data availability permits
Historical analysis
Extensible artificial intelligence
Public data accessibility
Clear separation between automated intelligence and authoritative emergency instructions

Core Modules

Satellite Intelligence Module

The Satellite Intelligence Module provides the primary remote sensing layer for wildfire detection.

Capabilities include:

Integration with geostationary satellite observations
Integration with polar-orbiting satellite observations
Thermal infrared analysis
Shortwave infrared analysis
Multispectral imagery processing
Satellite observation timestamp management
Satellite coverage monitoring
Cross-satellite observation comparison
Historical satellite observation processing
Satellite data quality assessment
Satellite data provenance tracking
Extensible satellite provider interfaces

The module must support multiple satellite sources without requiring the detection system to depend on a single provider.

Fire Detection Module

The Fire Detection Module identifies potential fire activity from satellite and sensor observations.

Capabilities include:

Thermal anomaly detection
Hotspot identification
Early ignition detection
Nighttime fire detection
Temporal anomaly analysis
Spatial anomaly analysis
Cross-observation confirmation
Detection event creation
Detection timestamping
Geographic coordinate assignment
Detection source identification
Detection confidence generation

The module must distinguish between observed anomalies and confirmed incidents.

AI Vision Module

The AI Vision Module provides computer vision capabilities for interpreting satellite and aerial imagery.

Capabilities include:

Thermal image classification
Multispectral image classification
Smoke detection
Smoke segmentation
Cloud discrimination
Fire signature recognition
Industrial heat-source classification
Agricultural burn classification
Prescribed burn classification
Image-based anomaly recognition
Multi-temporal image comparison
Model confidence estimation

The module must support interchangeable AI models and must retain model provenance for each inference.

Smoke Intelligence Module

The Smoke Intelligence Module detects, maps, and analyzes smoke associated with potential wildfire events.

Capabilities include:

Smoke plume detection
Smoke segmentation
Plume boundary estimation
Plume direction analysis
Wind-assisted plume forecasting
Smoke movement modeling
Smoke impact mapping
Smoke confidence scoring
Historical smoke analysis
Air-quality data correlation

Smoke detection must remain distinguishable from confirmed fire detection.

Multi-Sensor Fusion Module

The Multi-Sensor Fusion Module combines independent observations to improve detection confidence.

Supported information may include:

Satellite thermal observations
Satellite imagery
Smoke detections
Lightning observations
Weather observations
Wind conditions
Temperature
Humidity
Vegetation conditions
Fuel conditions
Terrain
Elevation
Ground sensors
Aerial observations
Community reports
Authoritative incident information

The module must maintain the provenance of contributing observations and provide an explainable basis for calculated confidence.

Confidence and Verification Module

The Confidence and Verification Module evaluates the reliability of detected events.

Capabilities include:

Detection confidence scoring
Multi-source confirmation
Evidence weighting
False-positive assessment
Human verification
Community verification
Verification status tracking
Conflicting observation handling
Duplicate detection identification
Model confidence calibration
Confidence history
Verification provenance

Suggested detection classifications include:

Possible anomaly
Probable fire
High-confidence fire detection
Externally confirmed incident

Automated detection must not be represented as authoritative confirmation unless an authoritative source has independently confirmed the incident.

False Positive Intelligence Module

The False Positive Intelligence Module identifies recurring sources of non-wildfire thermal anomalies.

Potential classifications include:

Industrial facilities
Refineries
Power generation facilities
Agricultural burns
Prescribed burns
Construction activity
Land management operations
Gas flares
Other persistent thermal sources

The module may maintain historical spatial signatures to reduce repeated false detections.

Weather Intelligence Module

The Weather Intelligence Module incorporates meteorological conditions relevant to fire detection and behavior.

Capabilities include:

Wind speed
Wind direction
Temperature
Relative humidity
Atmospheric conditions
Precipitation
Weather history
Forecast integration
Weather anomaly analysis
Fire-weather correlation
Smoke movement inputs
Fire spread model inputs

Weather observations must be timestamped and geographically associated with the corresponding fire intelligence event.

Lightning Intelligence Module

The Lightning Intelligence Module identifies lightning observations that may correlate with wildfire ignition.

Capabilities include:

Lightning event ingestion
Geographic correlation
Temporal correlation
Lightning-to-fire association
Ignition probability analysis
Historical lightning ignition analysis
Lightning density mapping

Lightning correlation must be treated as supporting evidence rather than definitive proof of ignition.

Vegetation and Fuel Intelligence Module

The Vegetation and Fuel Intelligence Module evaluates environmental conditions affecting wildfire ignition and behavior.

Capabilities include:

Vegetation classification
Vegetation condition analysis
Fuel condition analysis
Fuel dryness indicators
Historical vegetation comparison
Geographic fuel mapping
Fire behavior model inputs
Risk assessment inputs

Terrain Intelligence Module

The Terrain Intelligence Module provides geospatial information for understanding fire behavior.

Capabilities include:

Elevation analysis
Slope analysis
Aspect analysis
Terrain classification
Geographic barriers
Fire spread model inputs
Infrastructure proximity analysis
Wildland-urban interface analysis

Fire Risk Intelligence Module

The Fire Risk Intelligence Module evaluates conditions associated with potential wildfire activity.

Capabilities include:

Ignition risk analysis
Environmental risk scoring
Weather risk analysis
Fuel condition analysis
Lightning correlation
Historical fire frequency
Geographic risk mapping
Wildland-urban interface risk
Risk trend analysis

Risk predictions must be clearly separated from active fire detections.

Fire Spread Forecasting Module

The Fire Spread Forecasting Module estimates potential short-term fire movement.

Capabilities include:

Fire growth modeling
Short-term spread projections
Weather-driven modeling
Terrain-aware modeling
Fuel-aware modeling
Historical behavior comparison
Multiple forecast scenarios
Forecast confidence
Forecast uncertainty
Forecast update cycles

Forecasts must identify their prediction time, data inputs, model version, confidence, and uncertainty.

Historical Fire Intelligence Module

The Historical Fire Intelligence Module provides long-term analysis of wildfire activity.

Capabilities include:

Historical hotspot analysis
Fire frequency analysis
Fire duration analysis
Ignition pattern analysis
Regional fire comparisons
State-level analysis
Geographic clustering
Historical smoke analysis
Historical spread analysis
Environmental correlation
Lightning ignition research
Long-term trend analysis

Geospatial Intelligence Module

The Geospatial Intelligence Module provides geographic processing throughout the platform.

Capabilities include:

Coordinate normalization
Geographic queries
Spatial indexing
Geographic boundaries
State and regional analysis
Wildland-urban interface mapping
Infrastructure proximity
Population exposure analysis
Fire perimeter visualization
Geographic event clustering
Map layer management

Alert and Notification Module

The Alert and Notification Module distributes wildfire intelligence to subscribed users and integrated systems.

Capabilities include:

Real-time fire alerts
Geographic alert zones
Smoke alerts
Fire spread alerts
Rapid-change notifications
Detection updates
Verification updates
Alert history
Alert provenance
Configurable notification channels
Geographic subscriptions
Coordinate-based subscriptions
Regional subscriptions

Alerts must clearly identify whether information represents an automated detection, prediction, or independently confirmed incident.

Public Dashboard Module

The Public Dashboard Module provides an interactive interface for viewing wildfire intelligence.

Capabilities include:

National wildfire map
Regional wildfire maps
Active detection display
Confidence visualization
Smoke plume visualization
Satellite imagery layers
Thermal anomaly layers
Weather layers
Lightning layers
Vegetation layers
Fuel layers
Terrain layers
Fire risk layers
Fire perimeter layers
Historical fire layers
Fire spread projections
Detection timelines
Time-lapse playback
Data-source filtering
Confidence filtering
Historical analysis

API Module

The API Module provides programmatic access to InfernoWatchAI intelligence.

The API should support services for:

Live fire detections
Historical fire detections
Detection confidence
Smoke intelligence
Fire risk
Fire spread forecasts
Incident timelines
Geographic queries
Historical analysis
Data provenance
Model metadata

The API should support both request-based and event-streaming interfaces where appropriate.

Streaming Module

The Streaming Module distributes real-time intelligence to connected applications.

Capabilities include:

Fire detection streams
Smoke detection streams
Alert streams
Verification updates
Forecast updates
Geographic event streams
Model event notifications

Community Intelligence Module

The Community Intelligence Module allows authorized users to submit observations that may assist detection and verification.

Capabilities include:

Fire reports
Smoke reports
Geographic observations
Field verification
Photographic evidence
Video evidence
Observation timestamps
Geographic metadata
Duplicate report detection
Moderation
Verification scoring

Community information must remain distinguishable from satellite detections and authoritative emergency information.

Research Module

The Research Module provides tools for scientific investigation and reproducible analysis.

Capabilities include:

Dataset exploration
Historical analysis
Model evaluation
Fire behavior research
Smoke research
Lightning ignition research
Environmental correlation
Geographic analysis
Data export
Reproducible research workflows
Research metadata

Data Provenance Module

The Data Provenance Module records the origin and processing history of information used by the platform.

Capabilities include:

Data-source identification
Observation timestamps
Processing timestamps
Model identification
Model version tracking
Transformation history
Detection provenance
Forecast provenance
Verification provenance
Dataset references

Model Management Module

The Model Management Module manages AI and analytical models used throughout the platform.

Capabilities include:

Model registration
Model versioning
Model metadata
Model provenance
Model validation
Performance tracking
Confidence calibration
Model comparison
Model retirement
Model replacement
Human review workflows

Security Module

The Security Module protects platform infrastructure, data, APIs, models, and alerting systems.

Capabilities include:

Authentication support
Authorization
API rate limiting
Input validation
Data integrity checks
Audit logging
Model integrity monitoring
Alert integrity monitoring
Abuse prevention
Security event tracking
Responsible vulnerability reporting

Privacy Module

The Privacy Module limits unnecessary collection and processing of personal information.

Capabilities include:

Data minimization
Privacy-aware community reporting
Removal or restriction of unnecessary personal information
Access controls
Retention policies
Privacy documentation
Protection of sensitive location information where applicable

Transparency Module

The Transparency Module communicates system behavior and limitations to users.

Capabilities include:

Detection methodology documentation
Confidence explanations
Data-source identification
Model identification
Known limitations
Data latency indicators
Coverage limitations
False-positive documentation
Uncertainty reporting
Verification status
System status information

Integration Module

The Integration Module provides standardized interfaces for external systems.

Potential integrations include:

Government data services
Fire agency information systems
Emergency management platforms
Geographic information systems
Weather services
Satellite providers
Research platforms
Public information systems
Notification services

Integrations must remain modular so that an external service can be replaced without redesigning the core platform.

Optional Plugin Modules

Additional Satellite Plugin

Provides additional satellite imagery and observation sources without modifying the core satellite intelligence module.

Ground Sensor Plugin

Supports fixed or mobile ground-based fire, temperature, smoke, weather, or environmental sensors.

Drone Intelligence Plugin

Provides optional ingestion and analysis of authorized drone imagery and observations.

Aircraft Intelligence Plugin

Provides optional integration of aerial imagery and aircraft-based observation systems.

Advanced AI Model Plugin

Allows additional computer vision, transformer, temporal, multimodal, or other machine learning models to be deployed as interchangeable detection components.

Advanced Smoke Modeling Plugin

Provides alternative smoke dispersion and plume forecasting models.

Advanced Fire Behavior Plugin

Provides additional fire spread and behavior models.

Air Quality Plugin

Integrates air quality observations and forecasts for enhanced smoke impact analysis.

Emergency Alert Plugin

Connects InfernoWatchAI with compatible emergency notification systems.

Mobile Application Plugin

Provides optional mobile interfaces for alerts, maps, field verification, and community observations.

GIS Plugin

Provides additional geographic information system capabilities and external GIS integrations.

Local Government Plugin

Provides optional integration with municipal, county, state, or regional data services.

Fire Agency Plugin

Provides optional integration with participating fire agencies and authorized incident information systems.

Community Reporting Plugin

Provides expanded public reporting, verification, moderation, and citizen-science capabilities.

Research Dataset Plugin

Provides optional research datasets and specialized analytical collections.

Edge Processing Plugin

Allows supported detection and analysis workflows to operate on remote or local computing systems where network connectivity is limited.

Offline Field Plugin

Provides optional offline access to selected maps, observations, verification workflows, and cached intelligence for field users.

Data Requirements

InfernoWatchAI should support data that is:

Geographically referenced
Time referenced
Source identified
Machine-readable where possible
Documented
Reproducible
Versioned where applicable
Legally available for the intended use

Each major observation should retain sufficient metadata to establish when, where, and from which source the observation originated.

Detection Lifecycle

A standard detection lifecycle should include:

Observation acquisition
Data validation
Preprocessing
Anomaly detection
AI classification
Cross-source comparison
False-positive assessment
Confidence scoring
Geographic association
Verification
Alert generation
Public visualization
Historical storage
Continuous updating

Forecast Lifecycle

A standard forecasting lifecycle should include:

Collection of current observations
Collection of environmental conditions
Terrain and fuel analysis
Selection of applicable forecasting model
Generation of forecast scenarios
Confidence and uncertainty assessment
Forecast publication
Forecast visualization
Continuous model updates
Comparison between forecast and observed conditions

Alert Lifecycle

A standard alert lifecycle should include:

Detection or forecast creation
Confidence assessment
Geographic determination
Alert classification
Notification generation
Distribution
User acknowledgment where supported
Subsequent observation updates
Verification updates
Alert closure
Historical retention

Human Verification

Human verification may be used when automated evidence is uncertain, conflicting, or operationally significant.

Verification workflows should provide:

Detection information
Source observations
Confidence information
Satellite imagery
Environmental context
Historical context
Available community observations
Available authoritative information
Verification status
Reviewer notes

Reproducibility

InfernoWatchAI must document the methods used to produce significant analytical results.

Documentation should identify:

Data sources
Processing methods
Model versions
Important configuration parameters
Processing timestamps
Geographic scope
Known limitations
Validation methods

Safety Requirements

InfernoWatchAI is an intelligence and detection platform and must not represent automated predictions as guaranteed outcomes.

Public-facing interfaces should distinguish between:

Detection
Probable detection
Prediction
Risk assessment
Community report
Human verification
Authoritative incident information

Emergency instructions should originate from appropriate emergency authorities rather than from automated InfernoWatchAI predictions.

Contribution Requirements

Contributors should:

Propose significant features before implementation.
Document changes.
Preserve required attribution.
Document new data sources.
Document new models.
Explain changes to detection or forecasting logic.
Update relevant technical documentation.
Identify changes affecting security or privacy.
Preserve reproducibility.
Avoid introducing unnecessary personal information.
Follow project licensing requirements.

Specification Branding License (SBL)

Standard

Fully AGPL-3.0+ compliant system
Copyleft enforced for network deployments
Required attribution
- Roxanne Ardary
- roxanneardary.com

Optional

Specification Branding License (SBL)
Attribution-free commercial deployment
Pricing based on scale, usage, and deployment scope
https://roxanneardary.com/infernowatchai/

License & Notice Requirements

InfernoWatchAI is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

By contributing to this project, you agree that your contributions will also be released under this license.

Please note the following:

All contributions must comply with the AGPL-3.0+ terms.
Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to Roxanne Ardary and roxanneardary.com.
InfernoWatchAI specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
The project’s notice.md file tracks attribution requirements and contributor acknowledgments. Any update that adds new contributors or modifies attribution should also update notice.md.
When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.

Notice – InfernoWatchAI

Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.

Contributors

This file tracks contributors and their specific contributions to the project.

Roxanne Ardary, roxanneardary.com – March 25, 2026
Created the repository for InfernoWatchAI. Developed the initial open-source U.S. wildfire detection platform integrating real-time satellite intelligence (GOES-16/18, VIIRS, MODIS, Landsat), AI-powered thermal anomaly and smoke detection, multi-sensor fusion, fire-spread forecasting, public dashboard, and open APIs.
[Add other contributors here] – [Date]
[Describe contribution in one sentence]

License – InfernoWatchAI

This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

Key Points

You are free to use, modify, and distribute the code.
All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
The software is provided “as is,” without warranty of any kind.

For the full license text, see GNU AGPL-3.0 License.