Post 17: AI for Detecting Illegal Fishing Activities

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Introduction

Illegal, unreported, and unregulated (IUU) fishing poses a significant threat to marine ecosystems, global fish stocks, and the economies that depend on them. These activities undermine conservation efforts and disrupt sustainable management of marine resources. Traditional methods of surveillance and enforcement have often proven insufficient in addressing the scale and sophistication of illegal fishing operations. Not only this, but the governance of the oceans is extremely complex as explore in previous posts. Artificial intelligence (AI) offers a promising solution to enhance the detection, monitoring, and response to IUU fishing. Additionally, AI's potential extends to other critical areas such as detecting illegal deep-sea mining, which similarly impacts environmental and economic sustainability.

S1: Why detecting IUU fishing is vital

Environmental Impact

Illegal fishing has severe consequences for marine ecosystems. It leads to the overexploitation of fish populations, many of which are already under stress from legal fishing activities. Overfishing disrupts the balance of marine ecosystems, threatening the survival of various species and the health of the ocean. For example, illegal fishing can deplete key species that play crucial roles in the food web, leading to cascading effects throughout the ecosystem. Additionally, illegal fishers often use destructive fishing methods, such as dynamite fishing and bottom trawling, which cause significant habitat destruction, further endangering marine biodiversity.

Economic Consequences

The economic impact of illegal fishing is profound. The World Bank estimates that IUU fishing costs the global economy up to $23.5 billion annually. Legal fisheries suffer from unfair competition, as illegal fishers do not adhere to the regulations and quotas designed to ensure sustainable practices. This not only reduces the income of legitimate fishers but also undermines the market for legally caught fish, potentially leading to lower prices and diminished returns. Coastal communities, especially those in developing countries, are disproportionately affected, as they rely heavily on fishing for their livelihoods. The economic strain can lead to increased poverty and food insecurity, exacerbating social inequalities.

Social and Regulatory Challenges

Enforcing fishing regulations and detecting illegal activities present significant social and regulatory challenges. Many illegal fishing operations take place in remote or poorly monitored areas, making surveillance difficult. The high seas, beyond national jurisdictions, provide a safe haven for illegal fishers due to the lack of effective governance and enforcement mechanisms. Additionally, the sophisticated methods used by illegal fishers, such as falsifying vessel identities and disabling tracking systems, further complicate detection efforts.

To address these challenges, there is a need for innovative solutions that can enhance surveillance and enforcement capabilities. AI technologies, with their ability to analyse vast amounts of data and identify patterns indicative of illegal activities, offer a powerful tool in this fight. Beyond fishing, similar AI-driven approaches could be used to tackle illegal deep-sea mining, which also has profound environmental and economic impacts.

S2: How AI is Used in Detecting IUU Fishing

Satellite Imagery and Remote Sensing

AI plays a crucial role in analysing satellite imagery and remote sensing data to detect illegal fishing activities. High-resolution satellite images can capture vast ocean areas, identifying suspicious vessel movements and fishing operations in protected or restricted zones. Machine learning algorithms process these images to detect anomalies, such as unregistered vessels or unusual fishing patterns, which may indicate illegal activities. By continuously monitoring these images, AI can provide real-time alerts to authorities, enabling swift action against illegal fishers.

Automatic Identification System (AIS) Data

AIS is used to track the movements of vessels at sea. AI enhances AIS data analysis by identifying patterns that suggest illegal fishing. For instance, machine learning models can detect when vessels turn off their AIS transponders to avoid detection, a common tactic used by illegal fishers. By correlating AIS data with satellite imagery, AI systems can identify vessels engaged in suspicious behaviour, such as entering restricted areas or operating without proper registration.

Acoustic Monitoring

AI is also used to analyse underwater acoustic data to detect illegal fishing activities. Acoustic sensors deployed in the ocean can capture the sounds of boat engines, fishing gear, and other maritime activities. AI algorithms process this acoustic data to identify the unique sound signatures of illegal fishing operations. This method is particularly useful in detecting illegal, unreported, and unregulated (IUU) fishing in remote areas where visual monitoring is challenging.

Machine Learning Algorithms

Various machine learning techniques are employed to enhance the detection of illegal fishing. Supervised learning algorithms, such as Support Vector Machines (SVM) and Random Forests, are trained on labelled datasets containing known instances of illegal fishing. These models learn to recognise patterns and features associated with illegal activities. Unsupervised learning algorithms, such as clustering and anomaly detection, identify unusual patterns in data that may indicate illegal fishing without requiring labelled examples. These algorithms continuously improve as they process more data, increasing their accuracy and reliability over time.

Real-Time Monitoring Systems

AI enables the development of real-time monitoring systems that provide continuous surveillance of fishing activities. These systems integrate data from various sources, including satellite imagery, AIS, and acoustic sensors, to offer a comprehensive view of maritime operations. AI algorithms analyse this data in real time, detecting illegal activities and generating alerts for enforcement agencies. This real-time capability allows for prompt responses to illegal fishing, reducing the time fishers have to operate undetected.

S3: Process of Detecting IUU Fishing Using AI and Implementation

Data Collection

The detection of illegal fishing activities using AI relies on collecting various types of data, each providing unique insights into marine operations. Key data sources include satellite imagery, AIS data, and acoustic recordings. High-resolution satellite images provide visual evidence of vessel movements and fishing activities over vast ocean areas. AIS transponders on vessels broadcast information such as location, speed, and heading, helping track legitimate and suspicious vessel movements. Acoustic sensors capture underwater sounds of boat engines, fishing gear, and other maritime activities, which AI analyses to identify illegal fishing activities.

Data Pre-processing

Before the collected data can be used, it must be pre-processed to ensure accuracy and usability. Data pre-processing involves noise reduction, where raw data, especially from acoustic sensors, is filtered to remove irrelevant sounds. Normalisation ensures consistency across different data formats and scales, making integration and analysis more straightforward. Anomaly detection is crucial for identifying and addressing corrupted satellite images, gaps in AIS data, or unusual acoustic patterns that do not correspond to known marine activities.

Model Training and Validation

AI models are trained using historical data that includes both legitimate and illegal fishing activities. During training, models undergo supervised learning with labelled datasets marking instances of illegal fishing. This helps the models recognize patterns associated with illegal activities. Key features such as vessel speed, movement patterns, and specific acoustic signatures are extracted from the data to help distinguish between legal and illegal activities.

Validation and testing ensure the models' accuracy and reliability. Cross-validation involves dividing the dataset into multiple subsets, training the model on some subsets, and testing it on others to evaluate performance. Metrics such as accuracy, precision, recall, and F1-score assess the model's effectiveness in identifying illegal fishing activities. High scores in these metrics indicate the model's robustness.

Real-Time Monitoring

Once trained and validated, AI models are deployed for real-time monitoring of fishing activities. This involves integrating data from satellite imagery, AIS, and acoustic sensors to provide a comprehensive view of maritime operations. Real-time processing analyses data as it is received, allowing for immediate detection of suspicious activities. Edge computing and cloud-based platforms are often used to handle the high computational demands of real-time processing.

Alert and Response Mechanisms

When the AI system detects potential illegal fishing activities, it generates automated alerts. These alerts are based on predefined criteria such as vessel behavior anomalies, unauthorised entry into protected areas, or detection of illegal fishing gear sounds. Notification systems send alerts to relevant authorities and enforcement agencies through various channels, including emails, SMS, and dedicated monitoring dashboards.

Upon receiving alerts, authorities can take swift action to address illegal fishing. Enforcement agencies may dispatch patrol vessels or aerial drones to the suspected location for verification and intervention. Detailed data logs and analysis reports provide the evidence needed for further investigation and legal action against perpetrators.

Implementation, Stakeholders, and Impact

Who Uses AI for Illegal Fishing Detection

AI-driven systems for detecting illegal fishing are used by various stakeholders, including governmental agencies, non-governmental organisations (NGOs), and international bodies. National maritime and fisheries authorities deploy these technologies to monitor and enforce fishing regulations within their territorial waters. NGOs, such as the Environmental Justice Foundation (EJF) and Oceana, utilise AI to support their advocacy and conservation efforts. International organisations, including the Food and Agriculture Organisation (FAO) and the International Maritime Organisation (IMO), collaborate with governments and tech companies to implement AI solutions for global monitoring of illegal fishing activities.

Impact of AI in Illegal Fishing Detection

The deployment of AI technologies has significantly enhanced the capacity to detect and combat illegal fishing. Real-time monitoring and automated alerts enable quicker response times, reducing the window of opportunity for illegal activities. This proactive approach helps preserve fish stocks and protect marine ecosystems from overexploitation and habitat destruction.

Economically, the use of AI reduces financial losses incurred by legal fisheries due to unfair competition from illegal operators. By ensuring compliance with regulations, AI supports sustainable fishing practices, which in turn contributes to the stability and prosperity of coastal communities dependent on fishing for their livelihoods.

Socially, AI aids in promoting transparency and accountability in marine governance. The data collected and analysed by AI systems provides robust evidence that can be used to hold offenders accountable, fostering a culture of compliance and stewardship among fishers.

S4: Challenges in Using AI for Detecting Illegal Fishing

Data Quality and Availability

Data Quality

One of the primary challenges in using AI for detecting illegal fishing is ensuring high-quality data. Satellite imagery, AIS data, and acoustic recordings must be accurate and reliable. Poor-quality images, gaps in AIS data, and noisy acoustic recordings can lead to incorrect analyses and false detections. Ensuring that the data is consistent and of high resolution is crucial for the effectiveness of AI algorithms. Additionally, environmental factors such as cloud cover, sea state, and interference from other vessels can impact the quality of the data collected.

Data Availability

Data availability is another significant challenge. While satellite coverage has improved, there are still areas, particularly in remote regions and the high seas, where data may be sparse or unavailable. The availability of AIS data can be compromised by vessels deliberately turning off their transponders to avoid detection. Acoustic data collection requires the deployment of sensors in strategic locations, which may not always be feasible due to logistical and financial constraints. Comprehensive and continuous data collection is essential for effective AI-based monitoring systems.

False Positives and Negatives

Minimising False Positives

False positives, where legal fishing activities are incorrectly identified as illegal, can lead to unnecessary alerts and wasted resources. This can undermine the credibility of the monitoring system and lead to distrust among fishers and regulatory authorities. AI models must be trained with diverse and extensive datasets to accurately distinguish between legal and illegal activities. Implementing advanced machine learning techniques and continuous model refinement can help minimise false positives.

Minimising False Negatives

False negatives, where illegal fishing activities go undetected, pose a significant risk to marine conservation efforts. Missing illegal activities allows them to continue unchecked, causing further harm to marine ecosystems and economies. Ensuring that AI models are highly sensitive and capable of detecting subtle signs of illegal activities is crucial. This involves using sophisticated algorithms and incorporating multiple data sources to improve detection accuracy.

Technological Limitations

Computational Demands

AI models, especially those involving deep learning, require substantial computational resources. Processing high-resolution satellite imagery, large volumes of AIS data, and continuous acoustic recordings in real-time demands powerful hardware and efficient algorithms. The high computational requirements can be a barrier, particularly for smaller organisations and developing countries with limited access to advanced technology.

Infrastructure and Maintenance

Establishing and maintaining the infrastructure needed for AI-based monitoring systems can be challenging. This includes deploying and maintaining sensors, ensuring reliable internet connectivity for data transmission, and managing data storage and processing facilities. The costs associated with these infrastructure needs can be prohibitive, requiring significant investment and ongoing maintenance.

Legal and Ethical Considerations

Privacy Concerns

The use of AI for surveillance raises privacy concerns, particularly when monitoring activities near coastal areas where human activities may be inadvertently captured. Ensuring that the data collection and analysis processes comply with privacy regulations and ethical guidelines is essential. Clear policies and transparency about how data is used and protected can help address these concerns.

Regulatory Compliance

Different countries have varying regulations regarding the monitoring and enforcement of fishing activities. Ensuring that AI-based systems comply with these regulations is crucial to avoid legal challenges. Collaboration with international regulatory bodies can help standardize practices and ensure that monitoring efforts are legally sound and globally accepted.

Environmental Impact

Non-Intrusive Monitoring

While AI offers powerful tools for monitoring illegal fishing, it is essential to ensure that the technologies used do not negatively impact marine environments. Deploying sensors and other monitoring equipment should be done in a way that minimizes disturbance to marine life and habitats. Sustainable practices in the deployment and maintenance of these technologies are vital to ensure that conservation efforts do not inadvertently cause harm.

S5: Future Directions and Innovations

Advancements in AI and Machine Learning

Enhanced Machine Learning Models

Future advancements in AI and machine learning will significantly enhance the detection of illegal fishing activities. The development of more sophisticated models, such as deep learning neural networks and reinforcement learning, will improve the accuracy and efficiency of identifying suspicious activities. These models can process larger datasets and identify complex patterns that simpler algorithms might miss. For example, convolutional neural networks (CNNs) could analyse satellite images more effectively, while recurrent neural networks (RNNs) could better handle sequential AIS data for detecting unusual vessel behaviours.

Real-Time Adaptation and Learning

AI systems capable of real-time adaptation and continuous learning will become increasingly important. These systems will be able to update their models based on new data, improving their detection capabilities over time. This dynamic learning approach ensures that AI systems remain effective even as illegal fishing tactics evolve. Techniques such as online learning and transfer learning can facilitate this continuous improvement, allowing AI systems to quickly adapt to new scenarios and datasets.

Integration with Other Technologies

IoT and Sensor Networks

Integrating AI with the Internet of Things (IoT) and sensor networks will provide a more comprehensive and robust monitoring system. IoT devices, such as smart buoys and underwater drones equipped with various sensors, can collect a wide range of environmental and activity data. AI algorithms can then analyse this multi-modal data to identify illegal fishing activities more accurately. The combination of different data sources, including visual, acoustic, and environmental data, will enhance the overall detection and monitoring capabilities.

Satellite and Aerial Surveillance

The integration of satellite and aerial surveillance with AI will also advance the detection of illegal fishing. High-resolution satellite imagery, combined with data from aerial drones, can provide detailed and extensive coverage of marine areas. AI can process these images in real time, detecting illegal fishing operations even in remote and vast ocean regions. Advances in satellite technology, such as higher resolution images and more frequent imaging, will further enhance this capability.

Scalability and Accessibility

Cloud Computing and Edge AI

Cloud computing and edge AI will play critical roles in making AI-based monitoring systems more scalable and accessible. Cloud-based platforms can handle large-scale data processing and storage, allowing for the integration and analysis of data from multiple sources. This scalability is essential for monitoring extensive marine areas and for enabling collaboration between different organise data source, will enable real-time analysis even in remote locations with limited internet connectivity. This combination of cloud and edge computing will ensure that AI monitoring systems are both powerful and flexible.

Citizen Science and Public Engagement

Engaging the public in monitoring efforts through citizen science initiatives will democratize data collection and expand the reach of monitoring systems. Mobile apps and online platforms that utilise AI to assist in species identification and activity reporting can empower non-experts to contribute valuable data. This collective effort can enhance biodiversity monitoring, illegal fishing detection, and raise public awareness about marine conservation issues. AI tools can help validate and integrate citizen-generated data, ensuring its quality and usefulness for scientific and regulatory purposes.

Global Collaboration and Data Sharing

International Cooperation

Global collaboration and data sharing are essential for effective illegal fishing detection and enforcement. AI technologies can facilitate this by providing standardised data formats and platforms for sharing information across borders. International organisations, such as the United Nations and regional fisheries management organisations, can play a pivotal role in promoting cooperation and data sharing. Collaborative initiatives can lead to the development of global databases and monitoring systems, providing a comprehensive view of illegal fishing activities worldwide.

Standardisation and Interoperability

Developing standardised protocols and ensuring interoperability between different monitoring systems will be crucial for global collaboration. Standardisation will enable the integration of data from various sources and systems, enhancing the overall effectiveness of monitoring efforts. AI can assist in harmonising data formats and ensuring that information from different regions and organisations can be seamlessly combined and analysed.

Sustainable Practices

Minimising Environmental Impact

As AI technologies are deployed for monitoring illegal fishing, it is essential to minimise their environmental impact. Sustainable practices in the design, deployment, and maintenance of monitoring equipment are crucial. For instance, using energy-efficient sensors, renewable energy sources for power, and biodegradable materials can reduce the ecological footprint of monitoring systems. Regular assessments of the environmental impact of these technologies will ensure that they contribute positively to marine conservation efforts.

Conclusion

We've explored the transformative role of AI in detecting illegal fishing activities. By leveraging advanced technologies such as satellite imagery, AIS data, and acoustic monitoring, AI enhances our ability to monitor and respond to illegal fishing in real time. We discussed the processes involved in AI-driven detection, the challenges faced, and the promising future directions and innovations that will further enhance these capabilities.

Detecting illegal fishing is crucial for protecting marine biodiversity, supporting sustainable fishing practices, and safeguarding the livelihoods of communities dependent on legal fishing. AI offers a powerful tool to address these challenges effectively and efficiently.

Thank you for reading and i hope you enjoyed this topic as much as I did! Stay tuned for our next blog post on AI-Driven Marine Habitat Mapping. Follow us on Instagram at @oceantechinsider for updates and more insights into the intersection of technology and ocean conservation.

"To me, the sea is a continual miracle; The fishes that swim—the rocks—the motion of the waves—the ships, with men in them. What stranger miracles are there?"

— Walt Whitman

Sources

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Samy-Kamal, M., 2022. Insights on illegal, unreported and unregulated (IUU) fishing activities by Egyptian vessels in neighbouring countries. Fishes, 7(5), p.288.

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Desai, R.M. and Shambaugh, G.E., 2021. Measuring the global impact of destructive and illegal fishing on maritime piracy: A spatial analysis. Plos one, 16(2), p.e0246835.

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Weekers, D., Petrossian, G. and Thiault, L., 2021. Illegal fishing and compliance management in marine protected areas: A situational approach. Crime Science, 10(1), p.9.

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Samy-Kamal, M., 2022. Insights on illegal, unreported and unregulated (IUU) fishing activities by Egyptian vessels in neighbouring countries. Fishes, 7(5), p.288.

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Belhabib, D., Sumaila, U.R. and Le Billon, P., 2019. The fisheries of Africa: Exploitation, policy, and maritime security trends. Marine Policy, 101, pp.80-92.