Building detection from aerial imagery: Datasets & Models
Automatically detect and segment building footprints from aerial and satellite imagery using instance segmentation. Outputs per-building polygon masks suitable for mapping, planning, and change detection workflows.
Building detection from aerial imagery
This segmentation workflow performs building footprint detection by generating pixel-level masks for each individual structure in a scene, isolating buildings from roads, vegetation, and other surfaces. It accepts aerial orthophotos, drone captures, and high-resolution satellite tiles as input and returns per-building polygons that can be exported to GIS formats for mapping and analysis.
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Why automate building detection from aerial imagery
Manually digitizing building footprints in GIS software is slow, tedious, and inconsistent across analysts, and it does not scale to city- or region-wide coverage or repeat surveys. In drones and geospatial work, teams need up-to-date footprint layers for urban planning, disaster response, tax assessment, and population estimation, and hand-tracing every rooftop simply cannot keep pace with imagery volumes. Automating building detection turns hours of manual annotation into a repeatable pipeline that processes thousands of tiles unattended. It also enables change detection, flagging new construction or demolition between imagery captures.
Key benefits
Instance segmentation delivers clean, per-building polygons that drop directly into existing GIS and mapping stacks, so you spend less time cleaning up geometry and more time on analysis. The workflow runs on imagery you already collect from drones or satellites, scales to large area-wide surveys, and produces consistent results across analysts and capture dates. Retraining on your own aerial datasets improves accuracy for specific regions, resolutions, and roof types.
How to use it
- Upload an image: Drop any photo into the widget above to try building detection from aerial imagery for free, right on this page.
- Preview instant results: The model detects building, roof, house, warehouse, shed 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 building detection from aerial imagery
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 building detection from aerial imagery, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Building Detection From Aerial Imagery project to Roboflow Agent.