Collect. Annotate. Validate. Build Better AI
Small targets in vast scenes, shifting seasons, cloud and shadow, and mixed sensor types make geospatial models hard to label consistently. We annotate satellite, LiDAR, SAR, and map data through a multi-stage QA process, so your model trains on labels it can trust.
Geospatial imagery covers huge areas in which the objects that matter are small. A building can be a few pixels wide, a road can vanish under tree cover, and the same field looks different across seasons, sun angles, and sensors. Multispectral, SAR, and LiDAR data add bands and returns that look nothing like an ordinary photograph, and map data demands geometry that is clean, closed, and correctly connected.
Labelling errors become missed structures, broken road networks, crop boundaries that drift, and change detection that reports noise as change.
At Qualitas Global, we deliver satellite image annotation, LiDAR point cloud annotation, SAR and multispectral labelling, and map annotation, from land cover segmentation to topology and geometry validation. Annotators are trained on your class definitions, resolution rules, and geometry standards before production begins. Every batch passes internal and external quality checks, and we can work in your own annotation platform. When labels are precise at every pixel and vertex, your model can be trusted across a whole region.
We classify pixels and polygons into land cover classes such as water, forest, cropland, built-up area, and bare ground, and separate land use categories such as residential, industrial, and agricultural, following your class hierarchy and minimum mapping unit.
We outline buildings, sheds, towers, bridges, and storage tanks as polygons or boxes, and tag type, roof material, or condition wherever your schema asks for it.
We trace roads, rail lines, runways, and paths as connected polylines, label road class and surface type, and keep segments joined through shadow, tree cover, and overpasses.
We delineate field boundaries and label crop type, growth stage, and field condition across seasons, so your model can separate neighbouring parcels and follow crops through the year.
We segment forest, shrubland, grassland, and wetland, label canopy and vegetation condition, and mark water bodies and burned or cleared areas for environmental analysis.
Our object detection annotation uses bounding boxes and oriented boxes around vehicles, vessels, aircraft, containers, and equipment seen from above, including small and densely packed objects.
We label what has changed between two or more dates, such as new construction, cleared land, shoreline movement, and water extent, while separating real change from seasonal and lighting differences.
We grade building and infrastructure damage after floods, fires, storms, and earthquakes, and label flooded areas, burned areas, and blocked roads against your damage scale.
We label stockpiles, excavation areas, haul roads, equipment, and site progress across repeat captures, so your model can measure activity and change on site.
We label built-up zones, green space, impervious surface, and parcel boundaries to support planning, valuation, and land records analysis.
We label point clouds and elevation models by class, such as ground, vegetation, buildings, power lines, and water, and mark 3D objects with cuboids, working with PCD, BIN, LAS, and LAZ files.
We annotate SAR amplitude images and multispectral bands, using band composites where they help, to label water, vegetation, built-up areas, and objects in scenes that cloud cover hides from optical imagery.
We annotate objects and features across large satellite tiles and mosaics, keeping labels consistent at tile edges and across resolutions, so detection models hold up over entire regions.
We trace roof-edge footprints as clean, closed polygons, and handle shadows, dense settlements, and the building offset caused by off-nadir views.
Our semantic segmentation annotation produces pixel-level masks for land cover classes, with written rules for mixed pixels and for boundary zones between classes.
We label crop rows, field extents, and crop condition on time-series imagery, tagging stressed, fallow, and harvested areas so models can follow a field through a season.
We compare image pairs and image series, labelling deforestation, water body shifts, urban expansion, and vegetation loss, and tag the type and extent of each change.
We locate and label pylons, substations, solar farms, pipelines, ports, and rail yards, with attributes for type and visible condition, to build and refresh asset inventories.
We digitise road centerlines, label road class, one-way direction, and surface, and connect segments at junctions, so the network is ready for routing.
We digitise district, municipal, ward, and zone boundaries from scanned maps, cadastral sheets, and imagery, and align them to your reference layers.
We place and categorise points of interest such as schools, hospitals, fuel stations, and landmarks, and label features such as rivers, lakes, and ridgelines, with names verified against your taxonomy.
We map power lines, water and gas pipelines, telecom routes, poles, and manholes from imagery and drawings, preserving the way each asset connects to the next.
We draw closed, non-overlapping parcel polygons from cadastral maps and imagery, label parcel IDs and land use, and keep shared edges exact.
We check delivered geometry for gaps, overlaps, dangling lines, self-intersections, duplicate vertices, and disconnected network segments, and correct each error against your topology rules.
Explore our data collection services.
Domain-trained expert annotators and daily throughput tracking against SLA targets.
A rigorous multi-level quality assurance process that ensures a 97%+ quality benchmark, validated on every project.
A risk-free 2-day pilot before full production, and team deployment within 24 business hours of sign-off. You see our work before you commit to volume.
Every engagement is tailored to your goals, with annotation guidelines, QA, and delivery cadence adapted to your project.
Pay-per-verified-productive-hour billing. You are not paying for our learning curve or mistakes, our internal rework, or idle bench time.
ISO/IEC 27001:2022, ISO 9001:2015, TISAX Level 3, HIPAA, and GDPR compliance. Biometric access, 24*7 CCTV monitoring, and policies and procedures that prohibit cameras and unauthorised storage devices on the floor.
100% in-house delivery. No outsourcing, no work-from-home access to client data, no exceptions.
Geospatial image annotation is the structured labeling of data so a machine-learning or analytics system can learn from examples or be evaluated against defined criteria. For geospatial & satellite: imaging & mapping, the work begins with a written label taxonomy, clear inclusion and exclusion rules, and representative sample data. The exact scope depends on the model objective and the client's acceptance criteria.
Depending on the approved scope, geospatial & satellite: imaging & mapping projects may involve satellite and aerial imagery, roads, buildings, land-cover classes, infrastructure, map features, and other client-defined geographic objects. The team should define each label, the unit being annotated, how to treat uncertain cases, and whether the task is frame-level, object-level, document-level, or text-level. These examples describe possible project requirements, not a guarantee that every modality or use case is currently offered.
Output formats and tooling should be confirmed during discovery rather than assumed from a general industry page. The project brief should specify the required schema, field names, class IDs, metadata, export structure, and any client platform access. Qualitas Global should confirm compatibility with the requested tools before the pilot begins; no specific format or third-party platform is promised here.
A quality plan should define the annotation guideline, reviewer responsibilities, sampling or review coverage, error categories, escalation rules, and acceptance criteria before production. Multi-level quality checks are a stated part of the intended service approach, but the exact workflow and quality SLA need internal confirmation. Disagreements should be documented, corrected, and reflected in updated guidance where the same issue could recur.
Data access, storage, transfer, retention, deletion, and physical work arrangements should be agreed before any sensitive files are shared. The project owner should confirm the applicable security controls and whether any certification or regulatory commitment applies to the specific engagement. Do not assume a particular ISO standard, HIPAA status, GDPR status, or other certification without current internal evidence.
Pricing should be scoped around the task definition, data complexity, annotation guideline, expected review effort, language or domain requirements, and the delivery arrangement. The stated commercial model is based on productive hours, with scope and acceptance criteria agreed in advance. A quote should clarify what counts as productive work, how rework is treated, and which activities are included, without relying on an unverified public price.
Yes, the recommended first step is to discuss a representative sample and agree on a pilot scope before committing a larger dataset. The pilot can test the label definitions, edge-case handling, reviewer workflow, output structure, and acceptance criteria. Start dates, sample size, turnaround, staffing, and scale-up capacity should be confirmed by the project team after reviewing the data and requirements.
Common challenges include Different image resolutions, seasonal change, cloud cover, shadows, dense urban scenes, and alignment between labels and geographic features. A clear guideline should show positive and negative examples, explain ambiguous cases, and define when annotators should escalate rather than guess. To get started, share the intended model or business use, data types, approximate volume, required outputs, security constraints, and a sample if permitted. Qualitas Global can then confirm fit and propose a pilot.
We audit, correct and validate auto-annotated aerial data against your guidelines before it reaches your training pipeline.
Whether you need precisely annotated training data, real-world data collection, a dedicated in-house team, or other staffing solutions, Qualitas Global delivers all three under one roof. Backed by over a decade of experience, we back every engagement with a risk-free 2-day pilot before you commit.
