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Detecting Marine Debris Using Sentinel-2 Satellite Images
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Machine learning algorithms — particularly LightGBM with a 95.16% F1-score — successfully detected marine debris in Sentinel-2 satellite imagery of Kuta Beach, Bali, identifying up to 500 m² of plastic waste along the coastline. This remote sensing approach offers a scalable, cost-effective alternative to field monitoring for tracking marine plastic pollution hotspots over time.
Plastic waste pollution in the oceans remains a global problem. Kuta Beach is one of Bali's tourist destinations that has been affected by plastic waste pollution. This is not in line with the 14th SDGs, which is to prevent and reduce marine debris pollution. However, the marine debris monitoring process carried out by the Ministry of Environment and Forestry requires officers to conduct direct monitoring in the field, which incurs higher costs. Therefore, satellite imagery can be an alternative option for more effective and efficient marine debris detection. This study aims to detect marine debris on Kuta Beach using machine learning algorithms, namely Random Forest (RF), XGBoost, and LightGBM. This study uses the Marine Debris Archive (MARIDA) dataset, which has marine debris labels, and Sentinel-2 images of Kuta Beach from 2019–2023. The LightGBM algorithm provided the best performance in detecting marine debris with an F1-score of 95.16%. The area detected as marine debris on Kuta Beach in 2019–2023 was 500 m2, 0 m2, 100 m2, 300 m2, and 400 m2, respectively. Based on these results, marine debris is generally detected around the coastline, particularly in the southern area of Kuta Beach, which is located near a shopping center.
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