“We annotate forestry imagery to deliver world-class tree analysis worldwide, and CVAT has been key to making that possible at scale.”
CVAT for Geospatial & Remote Sensing AI
CVAT turns satellite, aerial, and drone imagery into validated training data for mapping, monitoring, and inspection, trusted by teams like Arboair.
Use cases
Turn Imagery From Above Into Ground Truth
Building and property mapping
Outline roofs, footprints, and parcels across neighborhoods and cities.
Land cover and crop analysis
Segment fields, forests, water, and bare ground at scale.
Infrastructure and utility mapping
Trace roads, rail, power lines, and pipelines across whole networks.
Forestry and tree inventory
Count individual trees and map canopy across entire stands.
Damage assessment and change detection
Compare captures over time to map damage after fires, floods, and storms.
How it Works
Integrate Seamlessly
Start labeling without changing how you work
Connect your imagery archive, existing labels, and review stages as they are. Nothing to re-plumb.
Book a demoProjects/regional-survey-q3
Search sourcesAdd source
SourceStatus
1s3://drone-surveys/orthomosaicsSyncing…
2gs://sentinel-2/l2a-tilesSyncing…
3azure://aerial/county-2026Syncing…
4labels_v2 · COCO 1.0Syncing…
5Annotation → Validation → AcceptanceSyncing…
6webhook update:job → trainingSyncing…
7mapping_guidelines_v2.pdfSyncing…
Accelerate Validation
Clear labeling backlogs faster
AI pre-labels every tile. Your team or our experts fix what it misses.
Book a demoJobs/Tile 412
building #1
vehicle #2
solar_array #3
building #4
shed #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 geospatial AI.
