Road extraction from satellite: Datasets & Models

Manually digitizing road networks from satellite imagery is slow, tedious work that requires analysts to trace every road by hand across thousands of tiles, and the results drift out of date as new construction happens. Automating road extraction with segmentation lets geospatial teams keep basemaps current, fill gaps in coverage for remote or rapidly developing regions, and support routing, logistics, and disaster-response applications where accurate road data is critical. Instance segmentation captures connectivity and road boundaries far more precisely than manual sampling or coarse classification. The result is faster map production and consistent output that doesn't depend on individual analyst judgment.

Pixel-level masks give you road geometry precise enough to vectorize directly into GIS layers, cutting the hours normally spent on manual digitization. The workflow runs on the satellite, aerial, and drone imagery you already collect, and scales across large areas by tiling and batching inference. As new imagery arrives, you can re-run extraction to keep road networks continuously updated.

1. Upload an image: Drop any photo into the widget above to try road extraction from satellite for free, right on this page.
2. Preview instant results: The model detects road, highway, intersection, dirt road, bridge 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.