Wildfire detection from aerial: Datasets & Models
Segment fire and smoke regions in aerial imagery from drones and satellites to locate active wildfires and map their spread. Turns overhead camera feeds into pixel-level burn and smoke masks.
Wildfire detection from aerial
This segmentation workflow analyzes aerial imagery to perform wildfire detection, producing pixel-level masks that outline active flames, smoke plumes, and burned areas across the scene. It accepts inputs from drone cameras, fixed-wing aircraft, and satellite tiles, letting you locate fire regions and track how they spread over time.
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Why automate wildfire detection from aerial
Manual monitoring of vast forested regions relies on lookout towers, sparse sensor networks, and human analysts scanning imagery frame by frame, which is slow and misses early ignition. Automating wildfire detection with segmentation lets teams flag smoke and flame within seconds of a drone pass and continuously track how a perimeter evolves. For drone and geospatial teams covering large, remote areas, this means catching fires earlier and prioritizing where to send crews. Pixel-level masks also feed directly into GIS tools for area and spread calculations that bounding boxes cannot provide.
Key benefits
Instance segmentation delivers precise fire perimeter and burned-area measurements, not just presence flags, so downstream mapping and resource planning stay accurate. The models run on imagery from cameras you already fly, integrate with existing drone and satellite pipelines, and scale from a single UAV to fleet-wide monitoring. Retraining on your own aerial data sharpens performance across varied terrain, altitude, and lighting conditions.
How to use it
- Upload an image: Drop any photo into the widget above to try wildfire detection from aerial for free, right on this page.
- Preview instant results: The model detects fire, smoke, flame front, burn scar, vegetation 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 wildfire detection from aerial
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 wildfire detection from aerial, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Wildfire Detection From Aerial project to Roboflow Agent.