# Early Perspectives on the Planned Brazilian Program to Address Ship-Sourced Pollution
## 2 Project Description
The \"small satellites, big data\" approach offers a cost-effective solution for improving oil slick detection. Microsatellites and CubeSats enable continuous global data collection [41], which can be analyzed using machine learning techniques [21]. This approach enhances the detection of oil slicks in BJW by training AI algorithms to analyze satellite imagery, ocean color data, numerical forecasts, and sea state measurements. This extensive data improves the accuracy of oil slick detection and monitoring, providing timely and precise information for response teams. Moreover, both goals--ships and oil spill detection--could benefit significantly from a unified methodological framework, as the most widely accepted scientific approaches share considerable overlap, particularly the use of SAR imagery and CNNs, which demonstrated strong performance in extracting spatial data and enabling classification tasks in both contexts [31; 32; 33; 34]. However, it is worth noting that oil spill detection has seen broader experimentation with a variety of machine learning and deep learning techniques beyond CNNs, including transformer-based architectures and U-Net for semantic segmentation, FFDNet for noise reduction, Random Forest classifiers for pattern recognition, and YOLO for real-time object detection [39; 40]. Goal 3--oil spill modeling  
The development is based on open-source oil spill models such as the Spill, Transport and Fate Model (STFM) [42; 43; 44]; the Coupled Model for Oil Spill Prediction (CMOP) [45; 46]; General NOAA Operational Modeling Environment (PyGNOME) [47; 48]; and OpenDrift/OpenOIL [49; 50], which can be executed independently or as part of an ensemble approach. This ensemble methodology aims to enhance the reliability of simulations by capturing uncertainties inherent in model structures, initial conditions, and environmental forcing. To support operational usability, a new user interface for the SISMOM oil modeling platform was developed based on the design and functionalities of the STFM system (FAPESP 2023/01542-3 and 2024/06927-3) [51; 52; 53]. This interface will (i) improve the setup features for both technical and decision-makers; (ii) streamline the configuration, execution, and visualization of oil spill scenarios; and (iii) enable reproducibility and consistency across simulation outputs (Figure 2). A critical component of the interface development involves the standardization of input data formats across different modeling frameworks. This ensures interoperability between models and allows the seamless integration of outputs for comparative or ensemble analysis. Moreover, the interface is designed to be fully compatible with the metocean datasets generated under Goal 5, including high-resolution wind, current, and wave fields. This compatibility allows for automatic ingestion and conversion of forecast and hindcast environmental data into a unified format, thereby reducing user burden and minimizing errors associated with manual preprocessing. New optimized algorithms have been developed to enhance the performance of oil spill modeling using the new high-resolution hydrodynamic grids defined in Goal 5. These algorithms are specifically designed to increase the speed of numerical processing, automatically switch between the input data grids as the particles approach the coast, and assimilate satellite images during the simulation period. This will improve the precision of oil spill modeling and enhance the overall predictive capability of the SisMOM. Moreover, the performance of the models is also being evaluated through well-documented case studies based on oil spill data provided by IBAMA. In these analyses, the spatiotemporal variability of oil trajectories generated by the models is compared with aerial imagery of the oil slicks.