Maritime Environment Safety: Advanced Oil Spill Detection through AIS and Remote Sensing

Abstract

Oil spills constitute one of the most devastating environmental disasters that threaten marine ecosystems, coastal communities, and wildlife. From identification to intervention, prompt action is necessary to reduce the impact of oil spills. Traditionally used techniques for detection of oil spills involve direct visual surveillance or simple sampling which continues to be extremely costly, slow, and unsustainable for operating on a larger scale. In this scenario, AIS data, together with technologies for satellite remote sensing, are gradually becoming promising tools for detection and monitoring of oil spills in real time over vast stretches of ocean areas. This paper looks into the feasibility of combining the two types of data, namely vessel movements tracked by AIS, and satellite datasets consisting of SAR and optical imagery for detecting and assessing oil spills in marine waters. The strengths and challenges of integrating them as a way of enhancing the efficiency of oil spill detection and timeliness in the response is highlighted.

Introduction

1. Overview of Oil Spills and Impact

Oil spills are severe environmental disasters causing long-term damage to marine ecosystems, wildlife, and coastal economies. They result from maritime accidents, illegal dumping, or offshore drilling leaks. The consequences are both ecological (habitat destruction, toxic exposure to marine life) and economic (damage to fisheries and tourism). Rapid detection and response are essential, which require advanced monitoring systems beyond traditional, costly, and weather-dependent methods.

2. Advancements in Oil Spill Detection

Recent developments in Automatic Identification System (AIS) and satellite remote sensing, especially Synthetic Aperture Radar (SAR), have revolutionized oil spill detection:

• SAR Capabilities:

  • Works in all weather and at night.

  • Detects large and thin oil slicks across vast ocean areas.

  • Distinguishes oil from seawater via surface texture analysis.

  • Integrates with optical, thermal, and AI tools for better accuracy.

• SAR Limitations:

  • Can mistake natural films (e.g., algae) for oil.

  • Requires validation from optical or field data.

3. Role of AIS in Spill Detection

AIS tracks vessel movement and broadcasts real-time data (location, speed, route). Originally for collision avoidance, it's now critical for monitoring and investigating spills. Key methods include:

• Real-time Surveillance: Tracking vessels near sensitive areas.

• Anomaly Detection: Spotting suspicious behaviors (e.g., course deviation, sudden stops, AIS signal loss).

• Cross-Referencing with Remote Sensing: Matching vessel presence with detected oil slicks.

• Historical Analysis: Backtracking to identify spill sources.

• Spill Movement Prediction: Using oceanographic data (wind, current) with AIS for modeling spill trajectories.

Legal Enforcement: AIS records help identify and prosecute responsible vessels, aiding marine environmental protection.

4. Integrated Oil Spill Detection Methodology

A multi-step system combines satellite data, AIS, oceanographic data, and AI-based models:

1. Data Collection:

   • SAR, optical, thermal imagery.

   • AIS vessel data.

   • Wind, current, and wave data.

2. Preprocessing & Fusion:

   • Align and clean datasets.

   • Merge for comprehensive spatial and temporal analysis.

3. Detection & Prediction:

   • AI models (e.g., machine learning, CNNs) identify oil patterns.

   • Simulation models predict spill drift and impact.

4. Response Planning:

   • Real-time alerts.

   • Deployment of containment tools (booms, skimmers).

   • Legal action based on AIS data.

5. AI Models for Oil Spill Segmentation

A. Swin Transformer

• Uses shifted window attention for efficient global/local feature capture.

• Ideal for multi-spectral and SAR images.

• Better performance and lower computation than standard transformers.

B. U-Net

• Convolutional encoder-decoder structure with skip connections.

• Excels in high-resolution image segmentation with smaller datasets.

• Faster and more efficient than transformer models.

Conclusion

A powerful integration of AIS, SAR, remote sensing, oceanographic modeling, and AI ensures a robust system for detecting, tracking, and responding to oil spills. These technologies enable faster response, more accurate source identification, and better protection of marine environments, while also supporting legal enforcement against polluters.

References

[1] Garcia, F., & Thomas, D. (2020). \"Use of AIS and Remote Sensing Data for Oil Spill Detection in the Mediterranean Sea.\" Marine Pollution Bulletin, 150(1), 26-34. [2] Lu, W., & Zhao, S. (2019). \"The Application of Satellite SAR for Monitoring Oil Spills in the Ocean.\" Remote Sensing, 11(12), 1432. [3] Manfreda, S., & Tarantino, A. (2021). \"Integrating AIS Data and Satellite Imagery for Oil Spill Monitoring.\" International Journal of Environmental Monitoring, 8(2), 112-125. [4] Solberg, A.H.; Brekke, C.; Husoy, P.O. Oil spill detection in Radarsat and Envisat SAR images. IEEE Trans. Geosci. Remote Sens. 2007, 45, 746–755. [CrossRef]. [5] Fingas, M.; Brown, C. Review of oil spill remote sensing. Mar. Pollut. Bull. 2014, 83, 9–23. [CrossRef] [PubMed]. [6] Fingas, M.F.; Brown, C.E. Review of oil spill remote sensing. Spill Sci. Technol. Bull. 1997, 4, 199–208. [CrossRef]. [7] Espedal, H.; Johannessen, O. Cover: Detection of oil spills near offshore installations using synthetic aperture radar (SAR). Int. J. Remote Sens. 2000, 21, 2141–2144. [CrossRef]. [8] Kapustin, I.A.; Shomina, O.V.; Ermoshkin, A.V.; Bogatov, N.A.; Kupaev, A.V.; Molkov, A.A.; Ermakov, S.A. On Capabilities of Tracking Marine Surface Currents Using Artificial Film Slicks. Remote Sens. 2019, 11, 840. [CrossRef]. [9] Solberg, A.S.; Storvik, G.; Solberg, R.; Volden, E. Automatic detection of oil spills in ERS SAR images. IEEE Trans. Geosci. Remote Sens. 1999, 37, 1916–1924. [CrossRef]. [10] Fiscella, B.; Giancaspro, A.; Nirchio, F.; Pavese, P.; Trivero, P. Oil spill detection using marine SAR images. Int. J. Remote Sens. 2000, 21, 3561–3566. [CrossRef]. [11] Espedal, H. Satellite SAR oil spill detection using wind history information. Int. J. Remote Sens. 1999, 20, 49–65. [CrossRef]. [12] Karantzalos, K.; Argialas, D. Automatic detection and tracking of oil spills in SAR imagery with level set segmentation. Int. J. Remote Sens. 2008, 29, 6281–6296. [CrossRef]

Copyright

Copyright © 2025 Prof. Senthilnathan S, Prof. Rajakumar R, Sanjai A, Yuvasri S, Suganya E, Vasanth D. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.