# Early Perspectives on the Planned Brazilian Program to Address Ship-Sourced Pollution
## 3 Perspectives  
Increasing Brazil's satellite capabilities is central to SisMOM's strategy for protecting the BJW from oil spills and other environmental threats. The microsatellite constellation instruments will be the most critical component of the entire project, as they will collect the essential data and provide continuous, near real-time coverage of the BJW. A failure to achieve Goal 8 could undermine SisMOM's overall ability to fulfill its objectives, potentially leading to the collapse of the entire project and rendering the integrated response and monitoring system ineffective [85; 86; 87; 88]. The new constellation will generate terabytes of unlabeled data and daily broadcasts that AI algorithms must process to optimize the information for decision-makers [21]. AI algorithms can significantly enhance sea surveillance by detecting potential anomalies in the trajectory, speed, or position of ships, using AIS data. This capability improves SisMOM's ability to identify behaviors associated with oil bunkering or dumping, piracy, and other illegal activities [94]. Additionally, Radio Frequency data and images from the new satellite constellation (Goal 8) must be matched with AIS data to detect ships that fail to activate their AIS equipment as required [95]. The development and expansion of a microsatellite or CubeSat constellation is, in principle, a relatively straightforward task, as the necessary methodologies and instrumentation are well-established within the current state of aerospace engineering. However, the effective operation of such a system poses significantly greater challenges, particularly when the number of deployed satellites reaches a high magnitude. An increase in the number of satellites inevitably leads to a substantial escalation in the volume of data generated, which, in turn, demands a proportionally greater investment in data processing infrastructure and computational capabilities. Moreover, the prevalent use of Synthetic Aperture Radar (SAR) imagery in such constellations introduces additional operational complexities. While SAR is highly effective for all-weather, day-and-night imaging, it is also prone to generating a considerable number of false positives in oil spill detection scenarios. This limitation can result in frequent and unnecessary deployments of Remotely Operated Vehicles (ROVs) unless the satellite data is meticulously analyzed and filtered. To ensure the efficient functioning of a satellite constellation designed for environmental monitoring--particularly for detecting oil spills--robust data processing frameworks must be implemented. These should include advanced algorithms capable of minimizing false positives and maximizing detection accuracy. Furthermore, the integration of artificial intelligence and machine learning techniques is essential for automating image interpretation, prioritizing alerts, and supporting real-time decision-making. Thus, while the physical deployment of the constellation may be technically manageable, its successful application relies heavily on the sophistication and scalability of its data processing and analysis systems. Accurate discrimination between true oil slicks and natural look-alike phenomena--such as low-wind zones, biogenic films, or rain cells--presents technical challenges for SisMOM. Convolutional Neural Networks (CNNs) and Density-based Spatial Clustering of Applications with Noise (DBSCAN) could be combined to improve oil slick recognition and reduce false positive events. CNNs are particularly well-suited for extracting high-dimensional spatial features from remote sensing imagery. When applied to SAR data, CNN architectures can autonomously learn to identify textural and structural characteristics associated with oil slicks, thereby reducing the reliance on hand-crafted features or heuristic rule sets. Advanced variants such as U-Net and Fully Convolutional Networks (FCNs) have been adapted to the semantic segmentation of SAR images [95; 96], achieving pixel-level classification with high spatial fidelity.