Rivac LabsGeoAI/ML Engineer
Manually digitizing land cover, buildings, or infrastructure from satellite and aerial imagery doesn't scale past a handful of tiles, and clients with country- or region-scale imagery need automated extraction that still holds up to scrutiny. This service builds and trains models for feature extraction, object detection, and land-cover classification directly on your imagery, calibrated against your accuracy requirements rather than a generic benchmark. It solves the real bottleneck in most remote-sensing programs: turning petabytes of imagery into structured, usable vector data without an army of manual digitizers. We validate model outputs against ground truth before anything is treated as production data.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Diagnose imagery sources, resolution, and target feature classes to define an achievable accuracy target
- 2
Pilot a model on a labeled sample tile set and benchmark precision and recall against ground truth
- 3
Review misclassifications with domain experts and retrain on the specific failure cases found
- 4
Build the production inference pipeline and operate it with ongoing accuracy monitoring on new imagery
What You Get
Deliverables from this engagement
- Trained feature-extraction or classification model
- Validated vector outputs (buildings, land cover, or target objects) from source imagery
- Accuracy and confusion-matrix report against ground truth
- Production inference pipeline for processing new imagery batches
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about GeoAI/ML Engineer