The Need for Change in Subsea Surveys
The demand for large-scale seabed surveys is growing rapidly, but traditional survey methods face critical challenges:
- Data overload: Advancements in sensor technology mean we now collect vast amounts of data from sonar (multibeam, side-scan, synthetic aperture sonar) and optical systems (laser, images, video), making efficient processing essential.
- Slow and costly processing: Data processing remains largely manual, requiring significant time and expertise.
- Shortage of skilled personnel: The industry lacks enough qualified professionals to process the increasing volumes of data.
- Inefficiency in workflows: Existing workflows were designed for linear asset inspections (e.g., pipelines) but must now scale for wide-area surveys.
The challenge is not just acquiring more data but extracting meaningful insights faster and more efficiently. Addressing these inefficiencies is key to transforming subsea surveying.
The Shift to Autonomous Underwater Vehicles (AUVs)
AUVs have revolutionized underwater data collection by enabling high-speed large-area surveys. However, the shift to AUVs has primarily increased the rate of data acquisition rather than solving the core problem of data processing.
Despite their advantages, AUVs must still return to the surface before data processing can begin. The reliance on post-mission analysis means that while we are collecting more data than ever, we are not necessarily gaining insights any faster. The bottleneck remains: a lack of automated, efficient processing to convert raw data into usable information in real time.
The Cathx Approach: Accelerating Insight Generation
Cathx has developed a structured approach to solving this challenge by focusing on three key stages.
Stage 1: Reducing Data Volume to What Matters
The first step is prioritization—reducing the workload for skilled operators by minimizing the volume of data they need to review. To achieve this, we are developing Clarity microservices that use advanced machine vision techniques—such as colour and shape detection—to automatically filter data and highlight events of interest. For example, an environmental scientist monitoring coral health can receive only the images where coral structures are detected and flagged for signs of discoloration or bleaching, allowing them to focus on meaningful changes rather than combing through irrelevant footage.
These filtered datasets not only streamline analysis but also create high-quality inputs for training AI models. As more labelled data is collected, model accuracy continues to improve, enabling increasingly precise and automated detection of critical subsea events.
What Is CATHX Clarity?
Clarity is the world’s first autonomous perception platform for subsea data, designed to automate data processing workflows directly on underwater vehicles—at the moment data is collected. At its core is a modular microservices architecture: each Clarity microservice performs a specific processing task, such as sharpening images or detecting objects, and these can be combined into custom workflows tailored to any mission. With Clarity, organizations can build scalable, real-time solutions that transform raw data into actionable insight—fast, efficient, and ready for AI.
Stage 2: Bringing Processing to the AUV
To further enhance efficiency, event detection should move onboard the vehicle itself. By embedding real-time processing capabilities within the AUV, we can identify critical data as it is collected. Once the AUV is retrieved, decision-makers can immediately review key insights rather than waiting for extensive post-mission processing.
This shift significantly reduces the turnaround time for large-area surveys. Important discoveries—such as structural anomalies, environmental changes, or asset integrity issues—can be flagged as soon as the AUV surfaces, allowing for immediate decision-making.
Stage 3: AI-Driven Automation
The final step is leveraging machine learning to enhance event detection and classification. Initially, AI models can be trained offline using high-quality labelled datasets. Over time, these models can be deployed directly on AUVs, enabling onboard classification of features and anomalies without human intervention. As more data is collected, it becomes possible to further improve the performance of AI models.
Cathx’s roadmap is designed to facilitate AI model development and integration, allowing users to build their own detection capabilities and integrate them seamlessly as Clarity workflows. Beyond data classification, the ultimate goal is to enhance autonomous operations by integrating perception-driven
mission planning. By enabling AUVs to adapt dynamically based on real-time data analysis, we move closer to true underwater autonomy.
Future Perspectives: Where Is Large-Area Surveying Headed?
The future of subsea surveying lies in automation, real-time processing, and AI-enhanced decision making. As sensor technology continues to evolve, the industry must shift away from traditional, linear workflows designed for localized asset inspections and embrace scalable solutions that can handle vast amounts of data and increasingly complex environments.
Key advancements we anticipate include:
- Real-time adaptive mission planning: AUVs that can adjust their survey paths dynamically based on detected events
- Enhanced onboard processing: More powerful embedded computing capability to reduce reliance on post-mission data handling
- Greater interoperability: Seamless integration of optical and sonar data for comprehensive multi-modal analysis
By embracing these innovations, we can transform large-area optical surveys from a time-consuming process into streamlined, intelligent workflows that deliver actionable insights faster and more efficiently than ever before.
Making subsea data smarter, faster, and more effective
The industry is at a pivotal moment—advancements in data acquisition must be matched by equally sophisticated processing and automation solutions. At Cathx, we are committed to redefining subsea survey workflows by reducing data overload, enabling real-time event detection, and driving AI powered automation. The future of large-area optical surveys is not just about collecting more data, but about making that data work smarter, faster, and more effectively for industry applications.
