Solar potential rooftop segmentation: Datasets & Models
Segment rooftop surfaces from aerial and satellite imagery to estimate usable area for solar panel installation. Identifies roof planes, existing panels, and obstructions that reduce viable coverage.
Solar potential rooftop segmentation
This segmentation workflow analyzes aerial and satellite imagery to perform rooftop solar detection, outlining individual roof planes at the pixel level to estimate usable area for photovoltaic installation. It accepts drone captures, satellite tiles, and orthomosaics as inputs, and detects roof surfaces, existing solar panels, and obstructions such as chimneys, vents, and skylights that reduce viable coverage.
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Why automate solar potential rooftop segmentation
Rooftop solar detection and site assessment are traditionally done by manually tracing roofs in GIS software or sending crews for on-site surveys, both of which are slow, inconsistent, and hard to scale across a city or portfolio. Segmentation models automate the tedious step of measuring roof geometry and available surface, turning thousands of aerial images into structured data in a fraction of the time. For solar installers, utilities, and geospatial teams, this means faster feasibility screening and prioritization without dispatching people to every address.
Key benefits
Instance segmentation delivers precise per-roof masks that translate directly into square-meter estimates and shading or obstruction accounting, improving the accuracy of solar potential calculations. The workflow runs on imagery you already collect from drones or satellite providers, so it slots into existing geospatial pipelines. Because it is model-driven, it scales from a single neighborhood survey to region-wide rooftop analysis without proportional increases in manual labor.
How to use it
- Upload an image: Drop any photo into the widget above to try solar potential rooftop segmentation for free, right on this page.
- Preview instant results: The model detects rooftop, solar panel, chimney, skylight, roof vent 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.
- 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.
- 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.
Recommended model for solar potential rooftop segmentation
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for solar potential rooftop segmentation, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Solar Potential Rooftop Segmentation project to Roboflow Agent.