Collect. Annotate. Validate. Build Better AI
Maritime models fail on glare, waves, blurred underwater images, and sparse sensor returns. We annotate camera, sonar, radar, and LiDAR data through a multi-stage QA process, so your model trains on labels it can trust.
The accuracy of your labels is the accuracy of your AI model, and our process is built around that.
The sea is a difficult place to collect clean data. Surface glare hides vessels, underwater images are blurred, and sonar and radar return shapes that look nothing like camera views. Marine species and algae vary widely and are hard to tell apart.
Labeling errors become missed vessels, unreliable collision alerts, and species counts that cannot be used for research.
We have annotated very blurred underwater images, marine life, and algae using polygons at scale for a US client. Annotators are trained on your classes before production begins, and every batch passes internal and external quality checks.
Our object detection annotation and 2D bounding box annotation label ships, boats, tankers, and ferries in camera, aerial, and satellite imagery, then track each vessel across frames. This human-annotated data trains models to classify vessel types and follow movement through busy waterways.
We provide 3D data annotation and annotation for machine learning across LiDAR point cloud, radar, sonar, thermal, and camera data, with labels aligned across sensors. These sensor fusion labels train perception models for obstacle avoidance on autonomous and unmanned ships.
Our image annotation services label containers, container ID codes, and terminal scenes, with OCR transcription for recognition models. Accurate object detection annotation supports gate, yard and quay automation.
We use bounding box annotation and polygons to label cranes, trucks, cargo zones, lanes, buoys, docks, and coastal structures. These labels give mapping and monitoring models a clear record of port and shoreline assets.
Our video annotation services label fish and other marine species in underwater footage, including blurred and low-visibility frames, so models can detect and count them. This human-annotated data supports fisheries monitoring and marine research.
Annotators outline coral, algae, and seabed habitats with polygons and apply the labels your researchers specify. These semantic segmentation services help models track habitat extent and change across lakes, reefs, and ocean floors.
Our 3D data annotation separates shore, piers, vessels, and open water in LiDAR point clouds, classifying each point by type. These labels train navigation and harbor mapping models to read complex coastal scenes.
We annotate sonar and acoustic imagery by marking targets, classifying objects, and labeling range and angle where needed. This AI annotation supports underwater object detection where cameras cannot see.
Annotators label vessels, buoys, land, and clutter in radar data, and track targets across scans. These labels train models that detect risks and support collision avoidance at sea.
We annotate SAR and optical satellite imagery, and align labels with AIS records, to mark vessels that are not broadcasting or behaving as expected. This data annotation for AI supports models that flag suspicious activity and illegal fishing.
Our image annotation services use polygons to outline hull surfaces and mark corrosion, dents, cracks, and coating loss. These labels help inspection models assess vessel condition and prioritize repairs.
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.
Maritime 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 marine & maritime, 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, marine & maritime projects may involve vessels, port scenes, marine equipment, visible objects, video events, and other imagery-based categories specified by the project. 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 Weather and sea-state variation, glare, distant or partly occluded vessels, night imagery, and visually similar vessel classes. 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.
