Rooftop detection: Datasets & Models

Manually tracing rooftops across large geographic areas is slow, tedious, and inconsistent, especially when solar installers, insurers, and urban planners need to assess thousands of buildings at scale. Automating rooftop detection with a segmentation model turns raw drone and geospatial imagery into structured roof outlines in minutes, enabling accurate area estimates for solar potential, roof condition assessment, property inventory, and change detection over time. It removes the bottleneck of hand-digitizing footprints from a rooftop dataset and keeps results repeatable across campaigns. The pixel-precise masks also feed directly into GIS pipelines for measurement and analysis.

Instance segmentation with RF-DETR delivers precise roof boundaries that support reliable area and geometry calculations, not just approximate detections. The workflow runs on imagery you already collect from drones, planes, or satellites, so it integrates with existing geospatial pipelines and GIS tooling. It scales from a single neighborhood to region-wide surveys, letting teams process large tile sets consistently and re-run analysis as new imagery arrives.

1. Upload an image: Drop any photo into the widget above to try rooftop detection for free, right on this page.
2. Preview instant results: The model detects rooftop, building, solar panel, chimney, skylight 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.