Wildlife Monitoring

Use AI to automate wildlife species identification and real-time conservation monitoring

Business impact

• Operational efficiency — Automated analysis reduces manual labor and speeds up data processing
• Data processing speed — AI models rapidly analyze large volumes of images and sensor data
• Research throughput — Faster species identification increases volume and quality of research outputs
• Conservation effectiveness — Improved monitoring enables timely interventions and better species protection
• Detection accuracy — AI improves precision in identifying species and behaviors from diverse data
• Response time — Real-time alerts enable quicker reaction to threats or anomalies
• Habitat protection coverage — AI enables monitoring of larger areas with fewer resources

Data requirements

• Camera trap images (Image) — Primary visual data for species identification and behavior analysis
• Satellite imagery (Image) — Wide-area monitoring for population counts and habitat changes
• Acoustic sensors (Audio) — Capture animal vocalizations for species detection and behavior monitoring
• GPS tracking data (Numeric) — Track animal movements and migration patterns in real time
• Field reports (Text) — Supplement AI data with human observations and contextual information
• Video footage (Video) — Enable multi-animal tracking and behavior analysis over time

AI methods and techniques

• Predictive AI — Forecast animal movements and potential threats based on historical data
• Generative AI — Generate synthetic data or augment datasets for improved model training
• Symbolic AI — Incorporate domain knowledge for rule-based anomaly detection and alerts

AI models and model families

SpeciesNet, U-Net, YOLOv8, Foundation Models, Vision-Language Models, VoCap

Industries

Real-world evidence

6 documented case studies on record.

Companies using this: Conservation X Labs, Orora Tech, Rouxcel Technology, Snapshot Serengeti, University Oxford, Wildlife Protection Services.