DEMO

A2-D2 — Aerial Analysis & Drone Detection

A real-time airspace security platform that streams live drone telemetry, applies AI-powered threat scoring via IBM watsonx.ai, and automatically dispatches tiered alerts as contacts approach a protected zone.

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1 Review use case

Unauthorised drone activity in restricted airspace creates a growing security challenge for critical infrastructure operators, public venues, government facilities, and public safety organisations. Traditional detection systems can identify an aerial contact but may not provide enough context to determine the aircraft type, payload indicators, approach behaviour, potential risk, or appropriate response. Security operators and response teams need a unified operational picture that helps them understand which contacts require attention and why.

A2-D2 combines aerial telemetry, contact intelligence, computer vision, and IBM watsonx.ai within a real-time operator interface. The solution tracks active contacts, evaluates proximity, trajectory, speed, altitude, payload status, and historical activity, and produces an explainable threat assessment with a numerical risk score, key risk factors, and recommended actions. Configurable alert workflows can notify designated teams through voice, SMS, and email when a contact reaches defined zone or threat thresholds.

Designed for security operators, incident commanders, response teams, and infrastructure protection leaders, A2-D2 supports faster prioritisation while keeping operational decisions under human control. The interactive dashboard provides live contact tracking, protected-zone awareness, visual and flight intelligence, historical flight records, and an auditable record of assessments and notifications. This helps organisations move from detection to informed response through a single, traceable operational workflow.

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2 Try the interactive application

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3 Explore how it was built

A2-D2 ingests live drone telemetry from a sensor network, analyses threats using IBM watsonx.ai, stores flight data in MongoDB, and automatically alerts response teams when risks are detected. Live telemetry flows from a Sensor Network into the A2-D2 Backend, which feeds the A2-D2 Dashboard for real-time monitoring. IBM watsonx.ai evaluates proximity, trajectory, speed, and payload indicators to generate a risk score and threat level. Telemetry and flight history are stored in MongoDB. When high-risk events are detected, automated SMS and email notifications are dispatched to response teams. The end-to-end flow is: Sensor Data → watsonx.ai Analysis → Threat Assessment → Automated Alerts.

Architecture

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Source Code

A2-D2: Aerial Analysis and Drone Detection

Step 1 — Set the Scene

Introduce the operational challenge: security teams must identify, assess, and respond to aerial activity quickly, but the relevant telemetry, visual intelligence, risk indicators, and response workflows are often spread across disconnected systems.

Step 2 — Open A2-D2 and Introduce the Interface

Open A2-D2 and introduce the operational or security persona. Point out the monitored airspace, protected-zone boundaries, active aerial contacts, contact list, and Risk Assessment panel within the unified interface.

Step 3 — Explain Live Contact Processing

Explain that the platform continuously processes each contact’s position, altitude, speed, heading, and proximity to the protected area, giving operators an immediate view of developing aerial activity.

Step 4 — Select a High-Risk Contact

Select a contact in the Yellow or Red zone. Show how the map focuses on the contact, then walk through its flight information, current zone, distance, bearing, movement profile, aircraft details, detection method, and payload status.

Step 5 — Show the Visual View

Open the Visual view and highlight how supporting imagery and computer vision analysis enrich the contact profile with additional payload indicators and aircraft context.

Step 6 — Introduce the Risk Assessment Panel

Direct attention to the Risk Assessment panel. Show how IBM watsonx.ai evaluates the contact’s operational context, including proximity, trajectory, speed, altitude, estimated arrival time, payload indicators, aircraft classification, and relevant history.

Step 7 — Walk Through the AI Assessment Output

Walk through the AI-generated risk score, threat level, risk factors, and recommended actions. Emphasise that the assessment is explainable, allowing the operator to understand the factors behind the score and validate them against the available operational information.

Step 8 — Clarify AI-Assisted Decision Support

Explain that IBM watsonx.ai provides AI-assisted decision support rather than making autonomous engagement decisions. Authorised personnel remain responsible for determining the appropriate operational response.

Step 9 — Show Zone-Based Notification Workflow

Show the zone-based notification workflow. Explain how alert policies can trigger voice notifications, SMS messages, and email escalation based on the contact’s zone and assessed severity, carrying relevant information such as distance, estimated arrival time, risk score, and key risk factors.

Step 10 — Highlight Integrated Response Coordination

Point out the notification status in the interface and explain how A2-D2 connects detection, assessment, and response coordination within the same operational workflow, reducing the effort required to manually interpret events and notify response teams.

Step 11 — Review Inactive Contacts and Audit Trail

Open the Inactive contacts view and select a previous contact. Show its available flight path, telemetry history, zone transitions, incidents, AI assessments, and notification activity to demonstrate persistent operational intelligence and auditability.

Step 12 — Summarise the Unified Solution

Highlight that A2-D2 brings together aerial monitoring, contact intelligence, computer vision, IBM watsonx.ai assessment, configurable alerting, and historical analysis in a single solution while existing sensor and operational systems continue to provide their specialised capabilities.

Step 13 — Close with Broader Value

Close with the broader value: A2-D2 helps organisations move from aerial detection to explainable assessment and coordinated response faster, while preserving human control and a traceable operational record.

Step 14 — Extend the Pattern

Explain that the same operational AI pattern can be extended to perimeter security, critical-infrastructure protection, maritime awareness, logistics disruption, cybersecurity triage, and other use cases where decisions depend on rapidly changing operational information.