Early detection of invasive zebra mussels, built on lake-bed footage labeled by students and volunteer divers.
Read the story- underwater frames
- ~52,000
- zebra mussel detector
- Working
CVAT for Environmental Monitoring AI
CVAT turns camera-trap, underwater, and drone imagery into validated training data for wildlife monitoring, invasive species detection, and pollution tracking, trusted by teams like Robonotic.
Use cases
Turn Every Camera in the Wild Into Ground Truth
Wildlife counts and camera traps
Detect and count animals across camera traps and drone surveys.
Underwater and reef monitoring
Spot invasive species, fish, and coral bleaching in dive footage.
Waste and recycling
Classify waste fractions and contamination on every belt and bin.
Marine and coastal health
Map plastic debris, oil sheen, and erosion along coastlines.
Algal blooms and water pollution
Flag blooms, runoff, and spills from drones and fixed cameras.
How it Works
Integrate Seamlessly
Start labeling without changing how you work
Connect your field cameras, survey archives, existing labels, and review stages as they are. Nothing to re-plumb.
Book a demoProjects/lake-survey-2026
Search sourcesAdd source
SourceStatus
1s3://camera-traps/site-14Syncing…
2gs://dive-footage/2026Syncing…
3azure://drone-surveys/wetlandsSyncing…
4labels_v1 · COCO 1.0Syncing…
5Annotation → Validation → AcceptanceSyncing…
6webhook update:job → trainingSyncing…
7species_guide_v3.pdfSyncing…
Accelerate Validation
Clear labeling backlogs faster
AI pre-labels every image. Your ecologists or our experts fix what it misses.
Book a demoJobs/Dive 12 · Frame 377
mussel_cluster #1
fish #2
mussel_cluster #3
mussel_cluster #4
mussel #5
Ship with Confidence
Catch label errors before they reach training
Every job is scored against ground truth, so only clean data reaches training.
Book a demoTasks/Quality control
Mean annotation quality
98.4%
Target 95%
Honeypots
CVAT is how ML teams ship environmental AI.
