Large Geospatial Image Support
Annotate massive satellite, aerial, and geospatial imagery without downscaling or splitting images manually.
Bounding boxesPolygon boundariesSegmentation masks
Geospatial Annotation Platform
Geospatial Annotation Platform for Satellite & Aerial Imagery
Annotate satellite and aerial imagery for remote sensing, land-cover segmentation, and large-raster workflows with precise tools, geospatial context, and governed quality review.
No credit card required
Geospatial annotation features
Everything You Need for Complex Geospatial Annotation
Label large aerial images, segment complex regions, trace roads and boundaries, and apply consistent ontologies without losing visual context.
Deep Zoom & Multi-Resolution Viewing
Navigate seamlessly from large-area overviews to fine object-level details across multiple resolution levels.
Geospatial Coordinate Support
Preserve spatial coordinates and georeferencing while creating and exporting annotations.
Ontologies
Hierarchical classes, nested attributes, and structured metadata for geospatial imagery.
AI-Assisted Auto-Labeling
Accelerate repetitive geospatial labeling with Find Similar, Magic Touch, and automated annotation.
Boundary editingPixel level
Pixel-Perfect Segmentation
Precisely delineate buildings, roads, land parcels, vegetation, infrastructure, and irregular geographic regions.
Precise boundariesPolygon and mask tools
Built for AI Data at Scale
Annotate complex geospatial datasets faster with AI-assisted automation, scalable workflows, and lower operational costs.
15×
Faster geospatial data annotation
Batch, Find Similar, Prompt Auto-Labeling, and automated workflows reduce repetitive manual geospatial labeling.
95%
Automated geospatial annotation
On average, 95% of geospatial labels are pre-labeled automatically, then reviewed and refined by humans.
15×
Lower Training Data Costs
The cost per accepted label can be up to 15× lower as curation, annotation, and QA are automated.
Supported geospatial annotation types
All geospatial annotation types in one platform.
Create bounding boxes, segmentation masks, polygons, skeletons, polylines, keypoints, and 3D cuboids across aerial and geospatial images.
01
Bounding box
Detect vehicles, structures, and assets with precise rectangular regions.
02
Segmentation
Create pixel-accurate masks for roads, water, vegetation, buildings, or damage.
03
Polygon
Outline parcels, rooftops, fields, and site boundaries with precise vertices.
04
Skeleton
Use connected landmarks only for visual targets that require a defined point structure.
05
Line / polyline
Trace roads, paths, utility corridors, coastlines, and other linear features.
06
Point / keypoint
Mark poles, signs, landmarks, inspection targets, and other precise locations in an image.
07
Cuboid / 3D box
Represent supported visual targets with depth-aware boxes where perspective makes 3D extent useful.
08
Item Properties
Describe a complete image with source, capture conditions, quality, and scene-level attributes.
09
Relations
Connect buildings, roads, vehicles, and other annotated objects with contextual relationships.
Geospatial annotation quality assurance
Build quality into every geospatial annotation.
Compare labels on the same aerial or satellite imagery, check approved references, and resolve boundary issues in spatial context. Keep every quality decision connected to the source tile.
Geospatial Annotation Consensus
Compare independent polygons and classes on the same source tile. Surface disagreements in building footprints, roads, and land-cover boundaries for review.
Source-tile contextAgreement checksReview disagreements
Quality Gate (Honeypot)
Check geospatial labels against approved reference annotations hidden from annotators. Route submissions using configured criteria for supported geometry and classes.
Validation & Issue Resolution
Inspect required properties and boundary issues with the source imagery in view. Refine the affected geometry and resolve reviewer feedback before approval.
Geospatial QA Analytics
Track benchmark results, annotation agreement, and review outcomes across geospatial tasks. Prioritize tiles and feature classes that need closer inspection.
Benchmark resultsReview outcomes
Geospatial dataset curation & annotation workflows
Curate geospatial data and automate annotation workflows.
Search, version, and inspect geospatial datasets, connect AI models, and move annotations through review and approval.
Dataset Versions
Create and manage dataset versions as geospatial data evolves. Track changes, assign work, and keep every version auditable and production-ready.
Semantic Search
Explore large geospatial datasets through semantic understanding instead of manual filters. Find matching aerial images across diverse conditions.
Embedding View
Visualize dataset structure, identify outliers and labeling issues, and improve geospatial-data quality before training.
Integrated Workflows
Build workflows that connect models, annotation, review, and quality assurance in one continuous loop. Reduce handoffs and keep datasets moving from labeling to approval.
Bring Your Geospatial Model
Connect your own AI models for pre-labeling and model-assisted annotation. Improve accuracy and iterate faster on real-world geospatial datasets.
Solutions
Geospatial annotation solutions for spatial AI
Turn satellite, aerial, and drone imagery into structured datasets for land, agriculture, infrastructure, and environmental intelligence.
Land Use Classification
Annotate buildings, roads, vegetation, water bodies, and land-cover classes across satellite and aerial imagery.
Explore land use classification
Agricultural Monitoring
Label crop regions, field boundaries, irrigation patterns, vegetation conditions, and agricultural infrastructure.
Explore agriculture solutions
Infrastructure & Urban Mapping
Annotate roads, buildings, parcels, utilities, and construction areas for mapping, planning, and infrastructure intelligence.
Explore infrastructure & urban mapping
Environmental Monitoring
Label vegetation disturbance, flood extent, burned areas, and shorelines in satellite and aerial imagery for environmental analysis datasets.
Explore environmental monitoringQuestions, answered
Geospatial Annotation Platform FAQs
Answers about satellite and aerial image annotation, remote sensing, large geospatial rasters, land-cover segmentation, coordinates, ontologies, quality workflows, and model integration in Unitlab.
Talk with the Unitlab teamWhat geospatial annotation types does Unitlab support?+
Unitlab supports bounding boxes, segmentation masks and brushes, polygons, skeletons, lines and polylines, points and keypoints, cuboids and 3D boxes, relations, class properties, and Item Properties. Teams define labels and rules in a reusable ontology for consistent geospatial training data.
Geospatial annotation documentationHow can AI assist geospatial image labeling?+
Use built-in foundation models or bring your own model to generate visual predictions for supported image annotation types. Annotators review and refine model output so human approval remains part of the quality process.
Auto-labeling documentationCan Unitlab support large aerial images and precise visual inspection?+
Yes. Unitlab applies its visual annotation workspace to aerial imagery so teams can inspect small structures while maintaining broader image context. Use boxes, polygons, masks, polylines, and keypoints as appropriate for each image.
Geospatial inspection documentationHow do ontologies and properties work in geospatial annotation?+
Reusable ontologies define classes, attributes, Item Properties, and relations. For example, a Building can have Type and Condition attributes, a Road can have Type and Surface attributes, and the two objects can be connected with an adjacent-to relation.
Properties and relations documentationHow does Unitlab manage geospatial annotation quality and review?+
Configurable workflows route geospatial tasks through annotation, review, rework, and approval. Instructions, assignments, comments, issues, annotation history, dataset versions, and releases keep quality decisions traceable for individual experts and enterprise teams.
Annotation and review documentationCan teams use their own AI models for geospatial pre-labeling?
Yes. Unitlab can bring custom models into the annotation workflow to generate pre-labels and predictions. Annotators review and correct model output instead of starting from zero, while human approval remains part of the governed quality process.
Model integration documentationCan Unitlab curate, annotate, and version geospatial datasets in one platform?
Yes. Teams can curate geospatial data with metadata filters, semantic search, embeddings, similarity, and outlier discovery, then annotate selected samples, review results, and publish controlled dataset versions for reproducible AI development.
Dataset management documentationGEOSPATIAL ANNOTATION PLATFORM
Build Production-Ready Geospatial Datasets with Unitlab
Annotate, review, and manage complex aerial and satellite imagery in one AI-assisted workspace. Move from raw images to controlled, production-ready dataset versions with built-in quality workflows.
