Conservation Edge AI Lab: Design a Real-Time Wildlife & Threat Alert System
Sensing Threats. Saving Species
Skills you will gain:
About Program:
The Conservation Edge AI Lab focuses on building real-time wildlife and threat alert systems using edge AI, camera traps, acoustic sensors, and IoT networks in remote field conditions. Participants learn to detect poaching, habitat intrusion, and animal distress events as they occur, and convert raw sensor data into actionable alerts for rangers and conservation teams. The lab blends hands-on AI engineering with ethical, field-driven conservation needs—truly “Sensing Threats. Saving Species.
Aim: To develop an AI-driven, real-time wildlife and threat alert system using edge devices and sensor networks that can automatically detect, classify, and flag potential risks (poaching, habitat encroachment, animal distress) to enable faster, data-driven conservation decisions and on-ground intervention.
Program Objectives:
- Introduce participants to real-world challenges in wildlife conservation, including poaching, human–wildlife conflict, and bycatch detection.
- Demonstrate how camera traps, sensors, and basic AI/ML models can be combined into a practical, field-ready detection workflow.
- Equip participants to prototype simple, interpretable alert rules that can run on edge devices with limited connectivity and power.
- Build system-thinking skills by mapping actors, data flows, devices, and policies for a conservation area.
- Embed ethics, governance, and community considerations into the design of AI-enabled conservation systems.
- Foster collaboration between ecologists, conservation practitioners, and technologists working on data-driven conservation.
What you will learn?
📅 Day 1 – Camera Traps, Sensor Data & Detection Basics
- Context: poaching, human–wildlife conflict, bycatch detection, and why early detection matters.
- Hands-on: load a small set of camera-trap images (classes:
human,animal,empty). - Apply a pre-trained object detection model or simple image classifier to tag images.
- Analyse outputs: count events such as “human at night”, “animal near boundary”, and “empty frame”.
- Discuss detection errors (missed detections, false alarms) and the limits of off-the-shelf models.
👉 Outcome: Basic wildlife detection pipeline + 01_camera_trap_detection_demo.ipynb.
📅 Day 2 – Simple Alert Rules & Edge Constraints
- Concept: edge vs cloud – latency, patchy connectivity, energy limits, and device constraints.
- Hands-on: build a simple rule-based alert layer over the detection outputs.
- Example rule: if
personis detected in a sensitive zone within a risky time window → raise a “high-priority alert”. - Log alerts into a small table with
timestamp,location,alert_type, andconfidence. - Prioritisation: distinguish high, medium, and low-priority alerts for rangers and control rooms.
👉 Outcome: Minimal threat alert engine + 02_conservation_alert_logic.ipynb.
📅 Day 3 – System Design Canvas & Governance
- Ethics & governance: privacy of local communities, false positives, ranger fatigue, and safety risks.
- Hands-on: fill a system design canvas for one park/reserve (actors, data flows, devices, policies).
- Define alert routing: who receives which alerts, via which channel (SMS, app, radio), and in what format.
- Design escalation and response: expected response times, hand-offs, and log/feedback loops.
- Group sharing and refinement: compare designs and stress-test them with “what if” scenarios.
👉 Outcome: Deployable concept note for a conservation area + 03_conservation_edge_ai_system_canvas.docx.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Undergraduate and postgraduate students in Wildlife Biology, Ecology, Environmental Science, Forestry, Geography, GIS/Remote Sensing, Computer Science, ECE, AI/ML, or related fields.
- PhD scholars, postdocs, and early-career researchers working on conservation, human–wildlife conflict, biodiversity monitoring, or environmental data analytics.
- Professionals from conservation NGOs, wildlife trusts, government departments, forest/wildlife agencies, protected area management, and impact-driven startups.
- Data scientists, engineers, and technologists interested in deploying AI/ML solutions in low-resource, edge-computing environments for conservation.
- Basic familiarity with programming and data handling (preferably in Python) is recommended, but no prior deep learning expertise is required.
Career Supporting Skills
Program Outcomes
- Understand how camera traps, sensors, and basic AI models can support wildlife monitoring and anti-poaching operations.
- Build a simple end-to-end wildlife detection pipeline using camera-trap images and a pre-trained model.
- Design and implement rule-based alert logic for prioritising threats (e.g., human intrusion, animals near boundaries).
- Gain practical insight into edge vs cloud deployment constraints: latency, connectivity, energy, and device limits.
- Develop a system design canvas for a real or hypothetical conservation area, including data flows, actors, and policies.
- Integrate ethics and governance into system design, addressing privacy, false positives, ranger workload, and safety.
- Take away reusable artefacts: Jupyter notebooks, alert logic templates, and a draft concept note for a deployable edge-AI conservation system.