High-fidelity polygon labelling for objects that resist rectangular boxes — rooftops, agricultural plots, anatomical structures, and irregular industrial components.

Polygonal annotation traces the exact boundary of non-rectangular objects using a series of vertices connected into a polygon. This produces far richer training data than bounding boxes.
Our teams excel at high-vertex-count polygons for agricultural boundaries, building footprints, and fine-grained medical annotation.

We support annotation in standard polygon formats and integrate directly with GIS tools like QGIS and ArcGIS. All polygons validated for topology correctness.
Building footprint annotation for solar potential, insurance risk, and urban planning AI.
Crop field boundaries from satellite and drone imagery for yield monitoring and resource allocation.
Anatomical structure delineation for radiology and pathology AI models in CT, MRI, and histopathology.
Land parcel, road network, and water body boundary annotation for GIS and cadastral AI systems.
Component and defect shape annotation for manufacturing quality assurance AI.
Forest cover, deforestation, and wetland extent mapping from satellite imagery.
Vertex count, topology rules, and boundary snapping tolerances defined per object class.
Sample images annotated and reviewed for boundary accuracy and topological validity.
Scalable annotation with per-polygon QA and inter-rater agreement monitoring.
Self-intersection checks, gap detection, and topology validation on every batch.
Polygons follow the true object outline with minimised vertex count and maximised fidelity.
Native support for GeoJSON, Shapefile, GeoPackage. Direct integration with QGIS and ArcGIS.
Certified workflows for diagnostic AI with biomedical-trained annotators.
ISO 27001 certified. Satellite, aerial, and medical imagery stays confidential.
Millions of polygons per month with consistent quality.
Premium polygon quality at 40-60% lower cost than US/EU alternatives.
Get a custom quote within 24 hours. Our team is ready to discuss your dataset requirements and timeline.