Deadline: 14-Jul-2026
The Department of National Defence and Canadian Armed Forces (DND/CAF) are seeking AI-driven solutions that transform existing urban infrastructure into distributed, passive sensing networks. These systems must deliver real-time situational awareness and anomaly detection by processing ambient urban data. The goal is to enhance defence and security operations by converting cities into intelligent, sensor-enabled environments.
Program Purpose and Core Objectives
This challenge focuses on building scalable, AI-enabled urban intelligence systems that support defence, security, and critical infrastructure protection.
Key objectives include:
- Converting urban infrastructure into passive, distributed sensing networks
- Enabling real-time situational awareness through AI analytics
- Detecting anomalies such as:
- Unauthorized drone (UAS) activity
- Abnormal movement patterns
- Unusual RF (radio frequency) emissions
- Environmental irregularities
- Supporting urban operations and mission readiness
- Strengthening sovereign capability in AI-driven ambient intelligence systems
The program positions AI as a force multiplier for defence environments, especially in complex urban and coalition operations.
Core Technology Focus Areas
Proposed solutions must integrate advanced AI and data systems across multiple domains.
Key technical areas include:
- AI middleware systems for sensor integration
- Urban intelligence and situational awareness platforms
- Real-time data analytics and stream processing
- Sensor fusion from heterogeneous data sources
- Anomaly detection algorithms
- Edge computing architectures
- Adaptive sensor ingestion pipelines
- Rapid deployable AI systems
- Counter-unmanned aerial systems (Counter-UAS)
- Environmental and infrastructure monitoring systems
- Privacy-preserving and legally compliant AI systems
Data Sources and Use Cases
The challenge emphasizes repurposing existing urban infrastructure rather than deploying entirely new sensor networks.
Potential data sources include:
- Cameras and visual surveillance systems
- Acoustic sensors
- RF spectrum monitoring systems
- Environmental sensors (air quality, temperature, vibration)
- Infrastructure-integrated IoT systems
Example use cases:
- Counter-UAS systems combining camera, acoustic, and RF detection
- Infrastructure health monitoring using vibration and environmental data
- Early warning systems for environmental or security anomalies
- Detection of unusual urban movement or behavioral patterns
System Requirements
Solutions must meet strict functional and operational requirements.
Core system expectations:
- Real-time processing of multi-source heterogeneous data
- AI-driven anomaly detection and classification
- Generation of machine-readable alerts for operator review
- Scalable deployment across diverse urban environments
- Compatibility with standard data protocols and systems
- Rapid deployment in new operational contexts
- Adaptive integration with evolving sensor inputs
Compliance requirements:
- Full adherence to legal frameworks
- Strong privacy protection and data governance
- Alignment with national security and defence standards
Funding Structure
Funding is allocated based on Technology Readiness Level (TRL), supporting solutions from early concept to advanced prototype stages.
Funding tiers include:
- Early-stage solutions: up to $250,000
- Mid-stage development: up to $1.5 million
- Advanced prototypes and validated systems: up to $5 million
Key implication:
- Funding scales with maturity, validation, and deployment readiness
- Higher funding requires demonstrated operational capability
Eligible Applicants
The program is open to a broad innovation ecosystem.
Eligible participants include:
- Individual innovators and researchers
- Academic institutions
- Not-for-profit organizations
- Industry and private sector companies
- Government organizations (non-crown entities)
Ineligible entities:
- Federal crown corporations
- Provincial crown corporations
How the Challenge Works
The program follows a structured innovation and evaluation process:
- Step 1: Submission of proposed AI solution
- Step 2: Evaluation based on technical feasibility and relevance
- Step 3: Assessment of data integration, scalability, and compliance
- Step 4: Review of privacy, legal, and security considerations
- Step 5: Funding allocation based on Technology Readiness Level
- Step 6: Development and prototyping phase
- Step 7: Validation and potential operational integration
Key Innovation Principles
The challenge is guided by several core principles:
- Repurposing existing urban infrastructure instead of building new sensor networks
- AI-first architecture for real-time decision-making
- Multi-modal data fusion across heterogeneous sources
- Privacy-aware and legally compliant system design
- Edge-enabled and distributed computing approaches
- Interoperability with defence and coalition systems
Why This Program Matters
This initiative advances next-generation defence and urban intelligence capabilities by:
- Turning cities into intelligent sensing environments
- Improving early detection of security and environmental threats
- Enhancing critical infrastructure protection
- Supporting counter-drone and urban surveillance capabilities
- Strengthening national sovereignty in AI-enabled defence systems
- Enabling faster, data-driven operational decision-making
It represents a shift toward ambient intelligence systems where existing infrastructure becomes part of a unified sensing ecosystem.
Common Mistakes to Avoid
Technical mistakes:
- Designing systems that rely on single data sources only
- Ignoring real-time processing requirements
- Weak or non-scalable AI architectures
- Lack of sensor fusion capability
Compliance mistakes:
- Insufficient attention to privacy and legal requirements
- Failure to address data governance concerns
- Ignoring defence-grade security standards
Design mistakes:
- Overly complex systems without deployment feasibility
- Lack of edge computing or distributed architecture
- Poor interoperability with existing systems
Tips for a Strong Proposal
Strong submissions typically include:
- Clear multi-sensor data fusion architecture
- Real-time anomaly detection capability
- Demonstrated scalability and deployment readiness
- Strong edge computing integration
- Privacy-by-design and compliance-first approach
Best practices:
- Show real-world urban deployment scenarios
- Clearly define anomaly detection logic and thresholds
- Demonstrate interoperability with standard protocols
- Provide evidence of Technology Readiness Level progression
Frequently Asked Questions (FAQ)
What is the purpose of this challenge?
- To develop AI systems that convert urban infrastructure into distributed sensing networks for defence and situational awareness.
Who can apply?
- Individuals, academic institutions, private companies, nonprofits, and government organizations (excluding crown corporations).
What is the funding range?
- From $250,000 to $5 million depending on Technology Readiness Level.
What technologies are required?
- AI, sensor fusion, real-time analytics, edge computing, and anomaly detection systems.
What types of data must be processed?
- Camera, acoustic, RF, environmental, and infrastructure sensor data.
What are key compliance requirements?
- Privacy protection, legal compliance, and alignment with national defence standards.
What is the main innovation goal?
- To create scalable AI systems that transform cities into intelligent, real-time sensing environments.
Conclusion
The DND/CAF AI Urban Sensing Challenge is a high-impact defence innovation initiative focused on transforming urban infrastructure into intelligent, AI-powered sensing networks. By combining sensor fusion, real-time analytics, and edge computing, the program aims to enhance situational awareness, improve infrastructure protection, and strengthen national security capabilities. It represents a strategic move toward scalable, privacy-aware, and deployable urban intelligence systems for modern defence environments.
For more information, visit Government of Canada.