Geospatial Annotation

Annotate satellite, aerial, and large-raster imagery with deep zoom, spatial coordinates, structured ontologies, and AI-assisted segmentation.

Unitlab supports geospatial annotation across large satellite, aerial, and drone imagery. Teams can move from area overview to object detail, preserve geospatial context, label land cover and infrastructure, and review model-assisted geometry within governed workflows.

Use this guide when: you are building remote-sensing, land-cover, agriculture, mapping, infrastructure, environmental, or disaster-response datasets.

See geospatial annotation in action

The demo shows current large-image navigation and spatial annotation for geospatial data.

Open the demo in a new tab.

Before you begin

1. Create or select a project whose data and ontology match this modality.

2. Confirm the project instructions define the unit of annotation, boundary or timing policy, required properties, and review route.

3. Open the project and enter the assigned item from the project data view or queue. The Workbench loads the modality-native editor inside the shared Unitlab shell.

See Annotation Workbench for navigation, saving, item state, comments, issues, and workflow actions.

Understand the geospatial work surface

The geospatial experience applies Unitlab’s visual Workbench to large spatial rasters. Deep zoom preserves a continuous image while annotation geometry, ontology values, comments, history, and workflow actions remain available around the active view.

Satellite mosaic shown at overview, regional, and object-detail scales

Large-image support keeps broad spatial context available while labeling small objects and boundaries.

Supported annotation model

Annotation type	Use it for
Bounding box	Vehicles, structures, assets, and other localized objects.
Segmentation mask	Roads, water, vegetation, buildings, damage, and land-cover regions.
Polygon	Parcels, rooftops, fields, sites, and irregular boundaries.
Line or polyline	Roads, paths, utilities, coastlines, and other linear features.
Point or keypoint	Poles, signs, landmarks, and inspection targets.
Skeleton	Defined landmark structures where a connected point model is required.
Cuboid	Perspective-aware 3D-like extent for supported aerial targets.
Item Property	Source, capture condition, sensor, scene, and quality context for the complete image.
Relation	Connections among buildings, roads, vehicles, parcels, and other objects.

Write spatial rules for tile edges, partial objects, minimum mapping unit, occlusion, shadows, seasonal change, coordinate reference, and whether repeated features require exhaustive coverage.

Coordinate-aware spatial context

Keep georeferencing and spatial coordinates attached to the large image and its annotations. Zoom or pan should change only the view, not the underlying coordinate meaning. Confirm coordinate expectations before export, especially when downstream systems require a specific reference or projection.

Spatial context retained across overview, focus, and detail

The same labeled feature remains grounded in the source image’s spatial context.

Geospatial ontologies and relations

Use hierarchical classes for buildings, roads, land cover, crops, utilities, and project-specific targets. Add required properties such as type, condition, surface, confidence, or source; use Item Properties for capture and scene context; connect objects with governed relations such as adjacent-to or connected-to.

Geospatial ontology with Building and Road classes, attributes, relations, and Item Properties

Structured ontology values turn shapes into consistent spatial training data.

AI-assisted masks and repetitive features

Use supported model assistance, Magic Touch, or Find Similar to propose visual labels, then inspect every boundary and object in source context. Repetitive rooftops, roads, fields, or vegetation can accelerate well, but seasonal variation, shadows, occlusion, small structures, and domain shift require human correction.

AI-assisted geospatial masks with Find Similar, Magic Touch, and review

Assisted geospatial labels remain editable proposals inside the human review workflow.

Annotate one production item

1

1. Survey the image

Inspect the full area, source, capture condition, orientation, spatial reference, and project coverage rule before labeling details.

2

2. Navigate to the target scale

Use deep zoom to locate the region or object while keeping enough surrounding context to classify it correctly.

3

3. Create spatial geometry

Choose the ontology class and draw the required box, mask, polygon, line, point, skeleton, or cuboid.

4

4. Refine and connect

Correct boundaries at the specified resolution, complete properties, add Item Properties, and create ontology-defined spatial relations.

5

5. Review at multiple scales

Inspect object detail, neighboring context, tile or image edges, missed instances, and class consistency across the wider area.

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6. Save, route, and export

Resolve validation, save, submit through the workflow, and export only after confirming coordinate and format requirements.

Quality review

Review focus	What to check
Spatial reference	Preserve image identity, coordinate meaning, and expected export reference.
Coverage	Apply the same exhaustive or sampled labeling rule across the full area.
Boundary policy	Check shadows, occlusion, partial objects, seasonal change, and the minimum mapping unit.
Topology	Review connected lines, adjacent polygons, overlaps, gaps, and object relations.
Scale	Confirm geometry at detail resolution and semantic correctness at regional context.

A saved annotation is not automatically a production-ready annotation. Required values, boundary or timing policy, cross-item consistency, and the configured review stage still apply.

Move from labels to governed data

Geospatial outputs should preserve source raster identity, spatial coordinates, geometry, ontology values, relations, version, and reviewer provenance. Use curated cohorts, dataset versions, and releases to separate geography, season, sensor, and domain conditions for reproducible model development.

Integrated Unitlab workflow connecting model assistance, annotation, review, and quality assurance

Use workflows to keep model output, human correction, review, and approval in one traceable operating path.

Next steps

• Use Detect Anything (SAM 1–SAM 3) to calibrate interactive and batch assistance.

• Use Multimodal overview when related files or views must stay in one task.

• Curate difficult cases and review cohorts in Data curation.

• Read the current geospatial annotation product overview for the feature overview and current media.