Multi-modal versatility
Work across drone photos, full-motion video, and LiDAR point clouds in one project to cover terrain at any altitude.
Aerial
Build aerial perception models on data you can trust.
A professional platform for annotating aerial imagery into precise, ML-ready training datasets.
Applications
Precision agriculture
Monitor crop health, pinpoint stress zones, and optimize irrigation and fertilization from ML-ready aerial datasets.
Technical edge
AI-powered automation
Pre-label recurring objects like rooftops, roads, and vehicles, then let interpolation carry them across video frames.
Quality control
Validation and multi-level review flag mislabeled parcels and missed structures before data ships.
Collaborative workflow
Split large survey areas between annotators and reviewers; role-based access keeps big mapping jobs in sync.
Customization & integration
Add custom classes for your survey targets and push finished data straight to your GIS or ML stack via API.
Enterprise-grade security
Keep sensitive site and infrastructure imagery confidential, with on-premise deployment when data can't leave your servers.
Annotation types
Bounding box
Rectangular boxes for detecting and classifying objects such as vehicles, buildings, and infrastructure in aerial imagery.
Oriented bounding box
Rotated rectangles that capture the position, size, and orientation of objects like aircraft, ships, or angled structures.
Polygon
Flexible shapes that outline irregular objects like building footprints, land parcels, water bodies, and construction sites.
Points
Single points that mark small objects or key locations such as poles, vehicles, and reference landmarks across large aerial scenes.
Lines & multilines
Trace linear structures such as roads, railways, rivers, power lines, and field boundaries.
Cuboid
3D boxes that estimate the volume and position of buildings, vehicles, or drones in aerial scenes.
3D point cloud
Label LiDAR and photogrammetry data to build accurate 3D models of terrain, urban areas, and infrastructure.
Semantic segmentation
Pixel-level classification of aerial imagery into classes such as land, vegetation, water, roads, and urban areas.
Instance segmentation
Pixel-wise separation of individual objects in the same class, so each building or tree is marked separately.
Bitmap
Precise raster masks for complex shapes like coastlines, forests, and dense urban textures.
Mesh
3D surface reconstruction for detailed digital twins of cities, terrain, and large environments.
Custom
A mix of annotation methods built around your specific aerial mapping or inspection task.
Put your aerial data to work
Book a demo and we'll scope your aerial dataset with you.
Custom solution? hello@keylabs.ai
