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From the Blog
Guides on LiDAR and point-cloud annotation from the Taskmonk blog.
High-quality ground truth labels for satellite, drone, and LiDAR models to power mapping, monitoring, and change detection.
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We did the heavy lifting in 2025. Here’s what it
added up to ->
View Details.
Capabilities of Taskmonk's geospatial data labeling
Purpose-built annotation across satellite, drone, LiDAR, and GIS data, with QA that holds up across tiles, regions, and sensors.
Satellite imagery annotation
Polygon and segmentation labels for buildings, roads, land cover, and critical infrastructure, ensuring remote sensing models remain accurate across tiles, regions, and seasons.
Drone and aerial image labeling
High-resolution bounding boxes, polygons, and masks for inspection, construction, mining, and site monitoring, following consistent guidelines to reduce edge-case errors.
LiDAR point cloud annotation
3D cuboids and point-level segmentation of vehicles, pedestrians, terrain, and obstacles, built for navigation and perception workflows.
GIS, vector, and change detection labeling
Polylines and boundaries for parcels, roads, and utilities, plus time-series change tags for damage, growth, and compliance, with attributes and topology checks.
Geospatial Annotation Types We Deliver
Named types behind the capabilities above, matched to the objects your models need to recognize.
Geo Fencing
Mark a virtual boundary around an area of interest to flag entry, exit, or presence within that zone.
Vector Annotation
Polylines and boundaries for parcels, roads, and utilities.
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Pixel Segmentation
Polygon and segmentation labels for buildings, roads, and land cover.
Point of Interest
Point markers for landmarks, facilities, and other fixed locations that need to be identified individually within a scene.
Geo-Tagging Tools Built Into the Platform
Basemaps, precision tools, and native geospatial formats, without exporting to a separate GIS tool first.
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LiDAR tooling : beyond the standard 2D toolset
Frame interpolation across annotated frames, including segmented point clouds. Cylinder tool with Merged Point Cloud View.
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Formats
Native GPKG (GeoPackage) import/export.
GeoJSON & GeoTIFF standard output.
GeoJSON & GeoTIFF standard output.
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Basemaps & View Controls
Google Maps, Esri, and OpenStreetMap basemaps.
Measurement, polyline, polygon, rectangle & location tools.
Measurement, polyline, polygon, rectangle & location tools.
Precision Tools
Representative Scale selector, Center on Area & Default Zoom reset. Orthogonalise tool that snaps hand-drawn polygons to right angles.
Geospatial data annotation use cases
From city planning to disaster response, ground truth built for how each industry actually uses geospatial data.
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Urban Planning
Polygon and segmentation labels for buildings, parcels, and land use that keep city-scale mapping accurate across tiles, regions, and seasonal shifts.
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Autonomous Vehicles
Point-level segmentation and 3D object labels for terrain, obstacles, vehicles, and pedestrians, built for perception pipelines.
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Agriculture
Annotate fields, crop areas, and vegetation patterns for yield estimation and risk scoring, even with haze, shadows, and mixed pixels.
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Disaster Response
Label flood extent, debris, and structural damage, using consistent time-series ground truth across before-and-after imagery.
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Mapping and Navigation
Polyline and segmentation labels for roads, trails, and intersections to improve routing reliability and connectivity graphs.
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Environmental Monitoring
Before-and-after labels to track deforestation, growth, degradation, and compliance events.
Geospatial data annotation workflow
A repeatable pilot-to-production process, with QA built into every sprint, not bolted on at the end.
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Pilot and feasibility check
To start, we run a small pilot on your geospatial sample data to validate classes, edge cases, output formats, and acceptance criteria before starting work.
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Guidelines and label taxonomy
We finalize a clear taxonomy, attribute schema, and edge-case rules so every annotator labels consistently across regions, sensors, seasons, and tile boundaries.
Geospatial data labeling sprints
Work is shipped in planned batches with daily throughput tracking, issue flags, and quick feedback loops, keeping timelines predictable while maintaining annotation consistency.
QA, review, and corrections
Multi-pass review with sampling plans, gold sets, and topology checks catches boundary noise, duplicates, and class confusion before your dataset is approved.
Delivery and versioned exports
You receive clean exports with metadata and dataset versions, making it easy to retrain, compare experiments, and audit what changed across releases.
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Continuous improvement loop
We monitor recurring failure modes, update guidelines, and tune QA checks, so label quality improves over time as your model and data evolve.
Geospatial Annotation at Production Scale
A dedicated geospatial team, built for production volume, not a one-off pilot.
50
Geospatial annotation headcount
300k/mo
Transactions
3+ years
Running Geospatial ops
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Qualified Human Expertise
Clear sprints, predictable handoffs, and versioned exports keep your training data on schedule. Dedicated review steps and process adjustments ensure the quality of production data.
Platform-Powered Workflows
Domain-vetted annotators follow strict guidelines and edge-case playbooks, producing high-quality ground truth labels that hold up in real-world production.
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Automation with Pre-Labeling
Foundation-model-assisted pre-labeling handles the first pass on satellite and aerial frames, so reviewers dont start from scratch.
Geospatial data labeling services with expert execution services from Taskmonk
Selectively trained geospatial data annotators and QA workflows help you scale satellite, drone, GIS, and LiDAR labeling projects reliably.
Geospatial project management
Scope and design classes, receive weekly deliveries, and collaborate with a dedicated contact through a documented workflow.
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Geospatial labeling expertise
Teams experienced in polygons, polylines, masks, point clouds, and change detection, using gold sets and edge-case rules.
Secure, scalable, cost-predictable
Access controls, audit logs, optional restricted environments, and transparent reporting on throughput, quality, and rework.
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Fast proof of concept
Share a sample dataset and label spec; get labeled examples with a QA snapshot to validate fit before scaling.
Beyond Geospatial
The modality pages your geospatial stack will actually touch.
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FAQ
What is geospatial data annotation?
Geospatial data annotation is the process of labeling location-based datasets, such as satellite imagery, drone imagery, GIS vector layers, and LiDAR point clouds, so that machine learning models can learn to perform mapping, monitoring, and navigation tasks accurately.
Which annotation types are most common for satellite imagery annotation?
Most projects use polygons and segmentation masks for buildings and land cover, polylines for roads and rivers, points for landmarks, and classification and attribute tagging to produce richer geospatial training data.
What geospatial formats do you support for input and output?
Common inputs include GeoTIFF and Cloud Optimized GeoTIFF (COG), tiled imagery, multispectral imagery, LiDAR point clouds, and GIS vector layers. Outputs include GeoJSON, segmentation masks, vector polygons and polylines, plus metadata.
How do you ensure quality in geospatial annotation at scale?
We use clear labeling guidelines, edge-case playbooks, multi-pass QA, gold sets, and topology checks to prevent broken polygons, duplicate features across tiles, and class drift across regions and seasons.
How do we start a geospatial data annotation pilot with Taskmonk?
Share a small sample dataset, label taxonomy, and target outputs. We return labeled examples with a QA snapshot and a scale plan, then move into production sprints with predictable deliveries.
What tools does Taskmonk provide for geospatial labeling?
Taskmonk's geo-tagging interface supports configurable RGB band mapping, so false-colour composites like NIR and SWIR display correctly for vegetation, moisture, and burn-scar work, and cumulative-cut contrast stretch, applied per band, to make low-contrast satellite imagery legible without leaving the tool, alongside native GPKG and GeoJSON export, and Google Maps, Esri, and OpenStreetMap basemaps with an Orthogonalise tool that snaps hand-drawn polygons to right angles.
Get geospatial ground truth you can ship
Start with a 24-48h pilot for geospatial data annotation and validate quality before scaling.
