Published on May 30, 2025

Building the Foundation for Maritime AI: SAR-Based Vessel Detection

Building the Foundation for Maritime AI: SAR-Based Vessel Detection

About the customer

A global leader in the Earth and space observation industry, utilizing AI and satellite data to deliver strategic insights for businesses and government sectors worldwide. Their AI-powered platform transforms complex data from Synthetic Aperture Radar (SAR) satellites, Automatic Identification System (AIS) signals, and proprietary maritime sources into real-time situational awareness. Their solutions enable critical outcomes in vessel tracking, offshore asset monitoring, illegal fishing detection, port surveillance, and maritime domain awareness.

Goals

The objective is to create a high-quality, large-scale ground truth dataset using thousands of Synthetic Aperture Radar (SAR) images. This dataset will aid in developing a high-performance, multiclass vessel object detection model. Our goal is to facilitate the creation and implementation of geospatial AI models by ensuring rapid turnaround, strict quality control, and cost-effective labeling. This approach aims to minimize the time needed for production while enhancing model accuracy at scale.

Challenge

SAR-based vessel detection poses unique challenges due to the characteristics of radar-based imaging. This includes issues like speckle noise, variations in reflectivity signatures, and lower visual clarity than electro-optical (EO) imagery. To accurately identify and differentiate between vessel types, such as tankers, cargo ships, fishing vessels, and patrol boats, it is essential to have a deep understanding of radar signatures and contextual geospatial interpretation. The complexity increases with densely clustered vessels, varying sea conditions, wake patterns, and ambiguities at the land-sea boundary. Additionally, traditional annotation teams often lack the specialized expertise to differentiate vessels from radar clutter or interpret unclear reflections.

Labelbees demonstrated strong technical adaptability and delivered quality results. 
GIS Specialist, Leading Space-Tech Company,

Solution

Labelbees has developed a scalable, high-precision data collection pipeline designed explicitly for Synthetic Aperture Radar (SAR) data. By utilizing in-house SAR experts, proprietary workflows, and ontology research tailored for radar imagery, Labelbees has successfully delivered thousands of annotated vessel instances with pixel-level accuracy. Our subject matter experts worked closely with data scientists to ensure the precise interpretation of radar-specific patterns and vessel characteristics.

Our extensive global vessel ontology, which is based on over 100,000 real-world vessel profiles, supports the nuances of SAR-specific labeling. This includes intensity-based bounding, wake interpretation, and the orientation of objects under radar tilt. To manage project complexities, our in-house labeling team utilized subject matter experts, machine learning data specialists, and data scientists to ensure high-quality outcomes throughout the pipeline. By following a rigorous, custom-built data labeling workflow, Labelbees provided a tailored solution that included:

  • SAR-Specific Ontology Adaptation
  • Identification and Curation of Edge Cases (e.g., backscatter)
  • A Data Solutions Team Led by Domain Experts
  • Custom Metadata and Environmental Context Tagging
  • Detailed Labeling Guidelines and Documentation
  • A Robust Quality Assurance Process
  • Feedback-Driven Iteration Cycles
  • Cross-Modal Validation
  • Dataset Insights and Reporting

Building the Foundation for Maritime AI: SAR-Based Vessel Detection

Sample image for SAR - Based vessel detection

Result

Labelbees facilitated the rapid development of an advanced SAR-based multiclass vessel detection model. Our high-quality ground truth data enabled the client to create robust models with enhanced detection accuracy across various maritime scenarios, ranging from open ocean to congested near-shore areas.

We delivered large batches of SAR annotations with near real-time turnaround, improved annotation precision by 18% compared to previous benchmarks, and reduced the time required for training data preparation. Additionally, we achieved first-pass annotation acceptance with zero rework cycles.

  • Accelerated deployment of AI-driven maritime intelligence features.
  • Facilitated cross-domain fusion (SAR + AIS + EO) for enhanced analytics.
  • Avoided expensive rework and delays, resulting in the achievement of first-pass approval.
  • Lowered model retraining costs and operational latency.
  • Boosted competitiveness in the defense, logistics, and maritime security sectors.
They showed a commendable understanding of the domain-specific nuances required for this task 
GIS Specialist, Leading Space-Tech Company,

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