Artificial Intelligence (AI)-Driven Remote Sensing and Predictive Analytics for Illegal Underwater Activity Detection

Abstract

Although underwater ecosystems have many species and provide economic benefits, they are now being threatened by actions such as unregulated fishing, smuggling by sea, underwater attacks, and violations of boundaries. Older ways of surveillance find it tough to follow what happens in these big and complex waters due to equipment obstacles, changes in the environment, and the secrecy of the activities. And thanks to artificial Intelligence (AI), remote sensing, and predictive analytics joining forces, there is now a better way to deal with threats at sea and in the ocean. The chapter highlights the importance of systems that can respond smartly to new problems and situations, to get early notice and identify threats. This section highlights convolutional neural networks (CNNs), recurrent neural networks (RNNs), DRL (deep reinforcement learning), and anomaly detection algorithms to show how they power the extraction of useful insights from sonar information, underwater sounds, and sensor data. Besides, predictive analytics is examined for its part in forecasting risks using data learned from the past and patterns. By using a lot of data gathered from underwater drones, autonomous underwater vehicles (AUVs), buoys, and remote sensing attached to satellites, machine learning models can spot delicate changes and single out potential high-risk zones. Examples are provided to prove how these technologies are used in practice such as stopping illegal fishing, detecting smugglers, and noticing strange movements from submarines. Utilizing remote sensing platforms together with AI allows for regular, automatic monitoring, requiring very little human help. With the use of deep learning systems, systems can still identify complex things they hear and see underwater, even when visibility is poor. Using Internet of Things (IoT) frameworks guarantees a smooth transfer of information which improves awareness for US naval defense, coastal guards, and those handling environmental monitoring. Although the chapter also discusses the problems and moral aspects of using AI for underwater surveillance. They deal with concerns about the privacy of data collected in waters, worries about environmental damage, issues related to the training data of algorithms, and why there needs to be explainable AI available to maintain accountability. Using a human-in-the-loop allows essential supervision of processes and activities that use automated decision-making, mainly when the situation is risky. In short, the chapter sets out how the use of AI-powered solutions in remote sensing and advanced analytics can greatly change underwater surveillance. It stresses the importance of getting technologists, marine scientists, policymakers, and defense experts to work together to design reliable, ethical, and sustainable systems for facing threats underwater and securing the seas.