Data Annotation for Marine & Oceanography AI
Underwater Imagery & Ocean Data Labeling
Australia's Marine AI Data Partner
Australia is custodian of some of the most biodiverse marine environments on Earth — the Great Barrier Reef, the Coral Sea, the Southern Ocean, and over 60,000 kilometres of coastline. The AI systems protecting and studying these waters depend on precisely annotated training data across fish species identification, coral reef health assessment, underwater habitat mapping, marine mammal detection, fisheries compliance monitoring, aquaculture operations, ocean climate modelling, and underwater robotics navigation.
AI Taggers delivers specialist marine data annotation across underwater image annotation, underwater video annotation, passive acoustic annotation, and sonar point cloud annotation for marine research institutions, government agencies, conservation organisations, fisheries managers, and ocean technology companies.
Trusted by marine scientists, reef monitoring programs, fisheries agencies, and ocean tech companies to annotate underwater imagery and ocean data with scientific precision.
Fish Species Detection & Classification
Identify, classify, and count fish species from underwater imagery and video for fisheries science and biodiversity monitoring.
Species-Level Bounding Box Annotation
Detect and classify fish species with tight bounding boxes across underwater imagery, supporting both single-species and multi-species detection models for fisheries science and marine biodiversity research.
Australian Species Annotation
Specialist annotation covering Great Barrier Reef species lists, tropical reef fish, temperate species, and deep-sea taxa unique to Australian waters and the broader Indo-Pacific region.
Instance Segmentation for Fish
Pixel-level instance segmentation separating individual fish from backgrounds, other fish, and reef structures for accurate counting and biomass estimation in cluttered underwater scenes.
Behaviour Annotation
Label fish behaviours including feeding, schooling, predator avoidance, courtship, territoriality, and spawning events from underwater video for behavioural ecology AI models.
Abundance & Density Estimation (MaxN)
Annotate maximum count per species per frame (MaxN) and relative abundance metrics from BRUV and ROV footage for standardised fisheries stock assessment.
Stereo BRUV Annotation
Paired-camera stereo BRUV annotation with length measurement calibration, species identification, and 3D spatial positioning for fisheries biomass estimation models.
Trawl & Catch Image Annotation
Classify and count species from trawl catch photographs, bycatch sorting images, and on-deck camera systems for automated fisheries monitoring and compliance reporting.
Coral Reef & Benthic Habitat
Monitor reef health, classify coral species, and map benthic substrates with scientific precision.
Coral Cover Annotation
Quantify live coral cover, dead coral, algae, sand, rubble, and other substrate categories from reef transect imagery for long-term reef health monitoring programs.
Coral Species Annotation
Identify coral to genus and species level including Acropora, Porites, Montipora, Pocillopora, and soft corals from high-resolution underwater imagery for biodiversity surveys.
Coral Bleaching Annotation
Label bleaching severity from healthy through pale, partially bleached, fully bleached, and recently dead categories using standardised coral bleaching indices.
Coral Disease Annotation
Annotate disease indicators including black band disease, white syndrome, brown band disease, skeletal eroding band, and other pathologies for reef health AI systems.
Benthic Substrate Classification (CATAMI)
Classify benthic substrates following the Collaborative and Annotation Tools for Analysis of Marine Imagery (CATAMI) classification scheme used across Australian marine science.
Reef Structural Complexity
Annotate reef rugosity, structural complexity, habitat zones, and three-dimensional reef architecture from photogrammetry and stereo imagery for habitat quality assessment.
Marine Mammal Detection
Detect and classify marine mammals from aerial, underwater, and acoustic data for conservation and environmental monitoring.
Cetacean Detection
Detect and classify cetaceans from aerial survey imagery, drone footage, and vessel-based photography for population monitoring, migration tracking, and conservation programs.
Australian Cetacean Annotation
Specialist annotation for humpback whales, southern right whales, blue whales, sperm whales, bottlenose dolphins, common dolphins, and other cetacean species found in Australian waters.
Dugong & Sea Turtle Annotation
Detect and count dugongs and sea turtle species (green, loggerhead, hawksbill, flatback, leatherback, olive ridley) from aerial surveys and underwater imagery for population assessment.
Seal & Sea Lion Annotation
Annotate Australian fur seals, Australian sea lions, leopard seals, and elephant seals from colony surveys, haul-out counts, and underwater encounter footage.
Marine Mammal Acoustic Annotation
Label cetacean vocalisations, echolocation clicks, and other marine mammal acoustic signatures from hydrophone and passive acoustic monitoring recordings for species identification and behaviour classification.
Underwater Robotics & AUV Navigation
Train perception systems for autonomous underwater vehicles, ROVs, and subsea inspection platforms.
Seafloor Obstacle Annotation
Label seafloor obstacles, rock formations, drop-offs, debris, and navigation hazards for AUV and ROV path planning in underwater robotics operations.
Pipe & Infrastructure Annotation
Detect and classify subsea pipelines, cables, risers, wellheads, and offshore infrastructure components for automated inspection and anomaly detection systems.
Visibility & Water Condition Annotation
Label water clarity, turbidity levels, particulate density, lighting conditions, and colour cast for adaptive perception systems that operate across variable underwater conditions.
Point Cloud Seafloor Annotation
Annotate multibeam sonar and photogrammetric 3D point clouds of the seafloor with terrain classes, habitat types, and structural features for sonar point cloud annotation workflows.
Oceanographic & Environmental Monitoring
Annotate satellite, aerial, and in-situ ocean data for climate science, habitat mapping, and pollution monitoring.
Satellite & Aerial Ocean Imagery
Annotate ocean colour, sea surface temperature patterns, chlorophyll concentration, upwelling zones, and oceanographic features from satellite and aerial remote sensing platforms.
Kelp & Seagrass Annotation
Map kelp forests, seagrass meadows, and macroalgae extent from aerial and underwater imagery for habitat monitoring and blue carbon assessment programs.
Water Quality Annotation
Label water quality indicators including algal blooms, sediment plumes, pollution events, and turbidity patterns from satellite imagery and in-situ camera systems.
Shoreline & Coastal Annotation
Annotate coastline position, erosion features, beach profiles, coastal vegetation, and intertidal zones from aerial and satellite imagery for coastal change monitoring.
Plastic & Marine Debris Annotation
Detect and classify floating plastic, ghost nets, marine litter, and debris from surface imagery, underwater cameras, and aerial surveys for ocean cleanup and pollution monitoring AI.
Aquaculture Monitoring
Support precision aquaculture with AI-ready annotation for fish farming, cage inspection, and feeding optimisation.
Pen & Cage Fish Annotation
Detect and classify fish within aquaculture pens and sea cages, annotate size distribution, health indicators, and stocking density for farm management AI linked to aquaculture annotation workflows.
Net & Infrastructure Inspection
Annotate net damage, biofouling levels, structural deformation, and mooring condition from underwater inspection footage for automated aquaculture maintenance systems.
Feeding Efficiency Annotation
Label feed pellet distribution, fish feeding response, uneaten feed detection, and feeding behaviour patterns for AI-driven precision feeding optimisation.
Australian Marine Science Alignment
Annotation protocols aligned with Australia's leading marine science frameworks, classification systems, and regulatory requirements.
CATAMI Classification Alignment
All benthic and substrate annotations follow the CATAMI classification scheme, ensuring compatibility with Australian marine science databases and long-term monitoring programs.
AIMS Long-Term Monitoring Support
Annotation protocols aligned with Australian Institute of Marine Science (AIMS) long-term monitoring program methodologies for reef health, crown-of-thorns starfish, and coral bleaching surveys.
BioHub & IMOS Integration
Data outputs formatted for integration with the Integrated Marine Observing System (IMOS) and national biodiversity data platforms for seamless scientific data sharing.
AFMA Compliance Annotation
Fisheries annotation aligned with Australian Fisheries Management Authority (AFMA) requirements for electronic monitoring, catch documentation, and bycatch reporting.
Marine Parks Compliance
Annotation supporting marine park zoning compliance monitoring, sanctuary zone surveillance, and protected species detection for marine park monitoring programs.
Frequently Asked Questions
What is marine data annotation for AI?
Marine data annotation is the process of labeling underwater imagery, video, acoustic recordings, sonar data, and satellite ocean imagery so that AI models can learn to identify fish species, classify coral health, detect marine mammals, map habitats, and monitor ocean conditions. Annotations include bounding boxes around fish, pixel-level coral segmentation, acoustic event labeling for marine mammals, and substrate classification following scientific standards like CATAMI.
What is coral reef annotation for AI?
Coral reef annotation involves labeling underwater reef imagery with coral species identification, coral cover quantification, bleaching severity classification, disease detection, and benthic substrate categorisation. These annotations train AI models used in reef health monitoring, climate impact assessment, and conservation planning. AI Taggers follows established marine science protocols including CATAMI classification for consistency with national monitoring programs.
Can AI Taggers annotate Great Barrier Reef imagery?
Yes. AI Taggers has specialist expertise in annotating Great Barrier Reef imagery including coral species identification across hard and soft coral genera, fish species classification for GBR species lists, crown-of-thorns starfish detection, bleaching severity assessment, and benthic substrate mapping aligned with AIMS monitoring methodologies. We work with research institutions, government agencies, and conservation organisations focused on GBR science.
What is BRUV annotation for fisheries AI?
Baited Remote Underwater Video (BRUV) annotation involves identifying and counting fish species from standardised video deployments, recording MaxN (maximum number of individuals per species in a single frame), annotating species-level classifications, and measuring fish lengths from stereo BRUV pairs. These annotations train AI models for automated fisheries stock assessment, biodiversity surveys, and marine protected area monitoring.
Does AI Taggers annotate passive acoustic monitoring data for marine mammals?
Yes. AI Taggers annotates passive acoustic monitoring (PAM) data including cetacean vocalisations (whale song, dolphin clicks, echolocation), temporal event boundaries, species-level acoustic classification, call type categorisation, and signal-to-noise quality labeling. These annotations support AI models for automated marine mammal detection, population monitoring, and environmental impact assessment for offshore developments.
What annotation does AI Taggers provide for underwater robotics navigation?
We annotate data for AUV and ROV perception systems including seafloor obstacle detection, terrain classification, pipe and infrastructure identification, water condition labeling, and 3D point cloud annotation from multibeam sonar. These annotations train AI navigation, inspection, and mapping systems used in offshore energy, marine science, and defence applications.
Can AI Taggers support annotation for Indigenous sea country monitoring programs?
Yes. AI Taggers is committed to supporting Indigenous-led sea country monitoring and management programs. We work collaboratively with Traditional Owner groups and Indigenous ranger programs to develop culturally appropriate annotation guidelines, respect Indigenous knowledge systems, and deliver data that supports Indigenous sea country management objectives alongside western scientific frameworks.
Get Started With Marine Annotation
Whether you are monitoring coral reef health, building fisheries AI, training underwater robotics, or tracking marine mammals, AI Taggers delivers the scientific-grade annotation your marine AI needs.