Pixel-perfect scene understanding for the most demanding AI applications. We deliver semantic and instance segmentation labels for autonomous driving, medical imaging, satellite analysis, and robotics — with full class hierarchy support.
Semantic segmentation assigns a class label to every single pixel in an image, giving your model a complete understanding of the scene. Unlike bounding boxes, segmentation reveals the exact shape and extent of every object — essential for scene-aware AI.
We support semantic segmentation (all pixels of a class share one label), instance segmentation (each individual object instance is uniquely identified), and panoptic segmentation (combines both for full scene coverage).
Our annotators are trained on industry-specific class ontologies for AV, medical, and satellite domains. We deliver masks in PNG, TIFF, COCO RLE, or custom formats.
Full scene segmentation for drivable area, lane markings, kerbs, road signs, and obstacles. We annotate to Cityscapes and custom AV taxonomies, including rare classes like construction zones, animals, and debris.
Land-use classification, vegetation mapping, and infrastructure detection from high-resolution satellite and aerial imagery. We handle multispectral, hyperspectral, and RGB inputs with GeoTIFF-compatible output.
Tumour, organ, and tissue boundary annotation in CT, MRI, PET, and histopathology slides. Our biomedical-trained annotators follow radiologist guidelines to produce clinical-grade segmentation masks for diagnostic AI.
Workspace understanding and object manipulation labels for robotic perception systems. We segment graspable objects, surface materials, and workspace boundaries to help robots navigate and interact safely.
Crop health mapping from multispectral drone and satellite imagery. We delineate healthy crop, stressed vegetation, bare soil, water bodies, and field boundaries to power yield prediction and intervention models.
Infrastructure monitoring and urban scene analysis for city management AI. We segment roads, footpaths, buildings, green spaces, and utility infrastructure from drone and street-level imagery at city scale.
We work with your team to finalise the class taxonomy, colour map, and edge-case guidelines. For AV projects we align to Cityscapes or nuScenes ontologies; for medical projects we follow your radiologist-defined label schema.
Sample masks are reviewed for boundary accuracy, class consistency, and edge handling before full production. We calculate pixel-level IoU against your reference annotations and iterate until thresholds are met.
Trained annotators use polygon and brush tools with inter-rater calibration sessions to maintain boundary precision at scale. Domain specialists are assigned to medical, AV, and satellite batches respectively.
Boundary accuracy checks, class distribution validation, and pixel-coverage audits are performed before every delivery. Final masks are exported in your target format — PNG, TIFF, COCO RLE, GeoTIFF, or custom schema.
Boundaries are traced to individual pixels using calibrated annotation tools and zoom-level protocols. We report mean IoU per class with every delivery so you have full visibility into mask quality.
Teams trained on AV, medical, satellite, and agricultural class ontologies. We don't use generalist annotators for specialist tasks — your domain gets domain-matched experts.
Ramp from hundreds to millions of segmented images without quality drop. Our parallel team structure and QA pipeline maintain consistent standards regardless of batch size.
ISO 27001 processes protect sensitive medical and proprietary imagery. All data is handled under NDA, with encrypted transfer, secure storage, and strict role-based access controls.
PNG, TIFF, COCO RLE, GeoTIFF, and custom outputs are all supported. We include format validation and schema documentation with every delivery at no extra charge.
Regular calibration sessions keep annotation quality aligned as guidelines evolve. We proactively flag edge cases and propose ontology refinements — acting as a true annotation partner, not just a vendor.
Get a custom quote within 24 hours. Our team is ready to discuss your dataset requirements and timeline.