Only Data Fusion Cuts Through Maritime Noise

Automatic identification system (AIS) transmissions generate a tsunami of data, but most of that raw data is not useful or accurate. Data noise – such as irrelevant signals, erroneous readings, or misidentified vessels – can obscure the true picture of maritime activity. 

Data corruption, particularly in relation to timestamps and coordinates, can also lead to significant errors in vessel tracking. For example, a corrupted timestamp might suggest that a vessel is in two places simultaneously, or a faulty coordinate could place a vessel on land, rather than at sea. 

With the rise of complex technological threats and deliberate AIS manipulation, ensuring the authenticity of vessel data is more important than ever, particularly for detecting and mitigating deceptive shipping practices (DSPs). 

Why Data Fusion is a Must 

Only data fusion can alleviate the maritime data influx, powered by big data technologies, distributed computing, and artificial intelligence (AI) to process, store, and analyze millions of data points in real-time. This process is essential for maintaining the accuracy and timeliness of vessel tracking information. Here are three ways fusion directly addresses the obstacles we mentioned previously: 

• Data noise: fusion employs sophisticated filtering mechanisms to eliminate this noise, ensuring that only valid and relevant data is processed
• Data corruption: rigorous validation techniques detect and correct anomalies, preserving the integrity of the dataset
• AIS manipulation: fusion is designed to identify and mitigate manipulation attempts, such as GPS jamming or GNSS spoofing, by cross-referencing data sources and employing advanced algorithms to detect inconsistencies

Data is data, right? Is there really a difference between selecting Windward’s more than a decade of fused and indexed maritime data, or just buying raw AIS data? It turns out there is a big difference. 

First, you would need to spend millions of dollars to obtain ten years of historical, fused AIS data – and working with less than that puts you at risk of not having sufficient or clean enough data to optimally train and refine AI data models in the complex maritime ecosystem. Without Windward’s fusion, you’ll be forced to rely on blips on the map, and it won’t be possible to meaningfully reduce false positives or obtain an accurate operational picture

Additionally, we should consider the saying, “Garbage in, garbage out.” Raw AIS data comes with a great deal of “noise” and effective AI is not just about data, it involves taking the data and producing actionable insights.

Windward’s Data Differentiators

• Multiple sources: by utilizing multiple sources for data (satellite, terrestrial, unstructured open source, and more), Windward ensures our technology is not over-reliant on any one source. 
• Clean and fused data: some maritime technology vendors show data as-is, while others do basic clean-up. Windward heavily invests in cleaning and then fusing and indexing the data, because without these processes, everything built on top of that flawed foundation will be wobbly. Maritime domain expertise is key for understanding and constantly evaluating data points.
• Iterative improvement of current and historical data: part of what sets Windward apart is our platform’s ability to take new insights and apply them to our historical data. This transforms the existing data into a treasure trove that continues to yield new analytical gems.

For more information on the foundational pieces and principles of Windward’s Maritime AI™ platform, check out our executive brief: A Look into the “Engine Room” Of Windward’s AI. And this interview with Windward maritime experts explains how the fusion process has been critical for Windward’s location (GNSS) manipulation risk model. 

Here’s how fusion happens: 

1. Data acquisition and validation: data is continuously collected from various sources and standardized for analysis. Before moving forward, it is validated to remove inaccuracies and redundant or erroneous information.
2. Feasibility and assignment: the validated data is evaluated to ensure it reflects realistic behavior patterns, after which it is assigned to the most appropriate entities using advanced matching techniques.
3. Conflict resolution and refinement: in cases of conflicting data assignments, a scoring mechanism resolves any ambiguities. The data is then further refined to maintain the accuracy and integrity of entity paths.
4. Data optimization: the final step involves streamlining the dataset, retaining only the most relevant information to ensure efficient processing in downstream applications.

But what are the desired goals that enacting these steps will achieve?

As with any endeavor, you cannot achieve goals that you have not clearly defined. Here are Windward’s three aims during the fusion process and the resultant benefits for our customers. 

1. Accurately define noteworthy vessel entities
   The core objective of the Windward fusion process is to create the most accurate representation of vessel activity by integrating both dynamic and static data. It’s essentially Windward’s “from-farm-to-table” approach, or more appropriately, our “from-sea-to-insights” approach. Dynamic data includes real-time movement information, while static data encompasses vessel characteristics such as name, IMO numbers, and physical attributes. By merging these data types, Windward ensures that each vessel’s identity is correctly maintained, providing a reliable basis for maritime analysis.
2. Historical accuracy
   Windward’s proven fusion process has been built on a foundation of historical data going back to 2013. This historical perspective empowers us to not only track current vessel movements, but to also analyze long-term trends and patterns. The ability to reference and compare historical data ensures that our Maritime AI™ platform remains robust, accurate, and capable of detecting anomalies over time.
3. Comprehensive coverage
   Achieving comprehensive coverage of vessels transmitting AIS signals is another critical goal of the fusion process. With an ever-expanding fleet of vessels and the increasing volume of AIS data, Windward’s fusion process strives to cover the overwhelming majority of global maritime traffic. This extensive coverage is vital for maintaining a complete and uninterrupted view of global maritime activity.

Here are three ways your organization will benefit from Windward’s fusion process. 

1. Enhanced data integrity
   By systematically cleansing and validating AIS data, Windward’s fusion process ensures that the information used for vessel tracking is highly reliable. This reduces the risk of errors that could lead to incorrect vessel identification or missed detections, providing a strong foundation for maritime decision-making.
2. Advanced logic capabilities
   The fusion process is not static – it is designed to learn and adapt over time. One of its key features is the ability to revisit and correct past decisions when new information becomes available. For instance, if new data suggests that what appeared to be a single vessel should be split into two entities, or merged with another, the system can adjust its records accordingly. This dynamic, AI-powered approach ensures that the data remains as accurate as possible, even as new challenges and anomalies arise.
3. Proactive detection with the trigger algorithm
   A standout feature of the fusion process is a unique, AI-driven algorithm that leverages vessel behavior to predict ownership changes. The algorithm’s ability to foresee changes based on subtle indicators, such as shifts in vessel patterns or changes in operational areas, provides users with early warnings and insights that are critical for risk management.

Data → Insights → Action is the goal, right? 

Accurate, fused, and clean data results in strategic decision-making that minimizes risk, increases operational efficiency, and makes it significantly easier to facilitate global trade and ensure compliance with dynamic regulations. A quick and easy example: 

Some vessels frequently change their identities in an attempt to escape scrutiny and/or engage in deceptive shipping practices. Windward’s patented fusion process, together with satellite, terrestrial, unstructured open source, and more data sources, is able to trace vessels back to their original identity, effectively exposing identity spoofing. 

Without fusion, full visibility is impossible. But not all fusion processes are created equally, and some of what is called “fusion” is actually just data cleaning. Let’s continue this conversation…