Oil spill detection: Datasets & Models

Manual oil spill monitoring means analysts squinting at satellite tiles and camera feeds, often catching leaks hours or days after they start, when cleanup costs and environmental damage have already compounded. In offshore drilling, pipeline networks, and refinery operations, that lag translates directly into regulatory exposure and expensive remediation. Automating spill segmentation gives you continuous, consistent screening that surfaces incidents the moment they appear and quantifies their footprint without waiting on human review. The pixel-accurate masks also feed downstream area calculations and response planning that bounding boxes alone can't support.

Instance segmentation delivers precise spill extent and area estimates, not just an alert, so responders can prioritize based on real severity. It runs on the satellite, drone, and camera infrastructure energy operators already deploy, and scales from a single platform to fleet-wide, basin-wide monitoring. Because the model outputs structured masks, results integrate cleanly with GIS tools, incident dashboards, and automated alerting.

1. Upload an image: Drop any photo into the widget above to try oil spill detection for free, right on this page.
2. Preview instant results: The model detects oil slick, oil sheen, water surface, vessel, pipeline within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR (instance segmentation) on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.