# Early Perspectives on the Planned Brazilian Program to Address Ship-Sourced Pollution
## 2 Project Description
The project design is subdivided into nine complementary goals. _Goal 1--automatic detection of ships at sea_  
This goal focuses on training artificial intelligence (AI) algorithms to analyze satellite images and auxiliary data, such as Radio Frequency (RF) signals and AIS signatures, to identify the presence, position, and anomalous movements of vessels. The integration of AI in detecting unusual ship trajectories is crucial for combating illegal oil spills, especially during nighttime operations when satellite-based oil detection is more challenging [21]. Vessels involved in illicit activities often use tactics such as applying dispersants, disabling AIS, altering routes, and operating under the cover of rain or darkness to evade detection [22; 23]. Among the available technologies for maritime surveillance, the multi-channel Synthetic Aperture Radar (SAR) has emerged as one of the most effective and sophisticated techniques for monitoring vessel activities at sea. The usual approaches consist of two steps: (1) selection of the target in SAR images of busy traffic, which corresponds to Automatic Identification System (AIS) signals by the \"dead-reckoning (DR) position\", and (2) estimation of the position, size, and speed of the ship from SAR images and comparison these results with the AIS \"true\" data [24; 25]. Despite the effectiveness of this approach, one of its main limitations lies in the massive volume of data generated by multi-channel SAR instruments, especially when operating, or over regions with dense maritime traffic. Processing and interpreting this high-dimensional data in real time is a significant challenge and requires the support of advanced artificial intelligence (AI) techniques, such as Convolutional Neural Networks (CNNs) [26; 27]. Although there are other options, CNNs are the most well-established and representative deep learning model [28], they are particularly effective in extracting spatial features from SAR images [29], allowing the automated identification and classification of vessels and oil slicks [30; 31]. While the detection of vessels using SAR and CNNs is a relatively well-established and technically mature process, likely, the scientific challenge of this goal lies in identifying and defining patterns of anomalous vessel behavior. Unlike other forms of maritime infractions, illegal oil dumping is often carried out while vessels remain on route, exhibiting no abrupt deviations in course or speed. This subtlety makes anomaly detection particularly complex, as traditional indicators such as sudden changes in trajectory or AIS deactivation may not always be present. As such, advancing this goal requires the development of novel behavioral models capable of discerning less obvious anomalies--potentially through the integration of contextual data, long-term movement patterns, and environmental variables. The successful identification of these nuanced behaviors will be critical to the effectiveness of this objective, as it constitutes the system's first alert signal. From this initial detection, other components are activated, such as the acquisition of aerial imagery (from satellites, drones, etc.) for visual confirmation of the spill, followed by the deployment of forecasting models and assessment of environmental conditions. Goal 2--automatic detection of oil at sea_  
Oil spill detection using SAR and deep learning techniques has become a significantly more established and widely adopted practice compared to vessel detection [32; 33]. The physical characteristics of oil slicks--such as their tendency to dampen capillary waves on the ocean surface--result in well-defined dark patterns in SAR images, which can be effectively distinguished from the background sea clutter using machine learning and image classification algorithms [34; 35; 36]. Over the past decade, several studies have refined this process, improving both accuracy and computational efficiency [37; 38; 39; 40].