Crop field mapping: Datasets & Models

Manually digitizing field boundaries from imagery is slow, tedious, and inconsistent, an analyst tracing parcels by hand across thousands of acres introduces errors and can't keep pace with each new flight or seasonal capture. Automating crop field mapping with segmentation gives you repeatable, pixel-accurate boundaries that update as new imagery arrives, so you can track planted area, detect changes between flights, and feed downstream yield or irrigation models. For drone and geospatial teams, this replaces subjective hand-drawn polygons with a model that processes entire orthomosaics in a single pass. It also scales cleanly from a single farm to regional survey coverage without adding headcount.

You get consistent, georeferenced field masks that plug directly into existing GIS and drone processing pipelines, eliminating hours of manual annotation per survey. The workflow runs on the imagery you already collect, whether from UAV missions or satellite tiles, and scales from individual plots to large agricultural regions. A crop field mapping dataset combined with an instance segmentation model lets you retrain and adapt to new geographies, crop types, and sensor resolutions as your coverage grows.

1. Upload an image: Drop any photo into the widget above to try crop field mapping for free, right on this page.
2. Preview instant results: The model detects crop field, field boundary, bare soil, road, water body 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.