AI-Powered Biodiversity Monitoring Network
AI-driven camera traps and acoustic sensors across a forest corridor, monitoring wildlife and feeding open biodiversity data to conservation partners.
The Challenge
Equipment theft, connectivity gaps in remote forest zones, and AI identification accuracy at early calibration stages.
What We Are Trying To Do
Deploy AI-driven camera traps and acoustic sensors across a forest corridor to monitor wildlife, detect poaching threats in real time, and publish open biodiversity data to inform evidence-based state policy.
Who It Serves
Local communities, forest departments, conservation NGOs and wildlife trusts around a 500+ sq km forest corridor.
Key Activities
- Install solar-powered camera traps and acoustic sensors across protected forest zones
- Build Azure ML models for real-time species identification (mammals, birds, reptiles)
- Develop a live biodiversity dashboard for forest departments and partner NGOs
- Train 30 local community rangers as data stewards and equipment managers
- Publish quarterly biodiversity reports and state-level policy briefs
Targets
Species catalogued
50+ species (Year 1 target)
Area monitored
500+ sq km target
Poaching reduction
30% reduction target
Rangers trained
30 (target)
Open biodiversity reports
4 (target)
Training
30 community rangers trained as data stewards and equipment managers.
Connects with the Conservation & Biodiversity, Wildlife & Nature and Research & Field Science schools in Learn & Train.
Volunteering
20 volunteer data scientists proposed via the Microsoft Change Agent Program.
Partnership Opportunities
Microsoft Ask: Azure AI credits ($50K), AI for Earth biodiversity grant ($50K), 20 volunteer data scientists via Change Agent Program, Microsoft IoT hardware support.
Discuss This ProgrammeNo verified results have been recorded for this programme yet. Targets above represent proposed goals, not achieved outcomes.