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Assessment of Spectral based Solutions for the Detection of Floating Marine Debris
Summary
This study assessed spectral-based remote sensing solutions for detecting floating marine plastic debris using the standardized MARIDA dataset, evaluating machine learning algorithms for performance benchmarking. Results highlighted the need for precise reference datasets for fair comparison of detection methods and identified gaps requiring further development to support accurate and scalable marine debris monitoring.
Typically, the detection of marine debris relies on in-situ campaigns that are characterized by huge human effort and limited spatial coverage. Following the need of a rapid solution for the detection of floating plastic, methods based on remote sensing data have been proposed recently. Their main limitation is represented by the lack of a general reference for evaluating performance. Recently, the Marine Debris Archive (MARIDA) has been released as a standard dataset to develop and evaluate Machine Learning (ML) algorithms for detection of Marine Plastic Debris. The MARIDA dataset has been created for simplifying the comparison between detection solutions with the aim of stimulating the research in the field of marine environment preservation. In this work, an assessment of spectral based solutions is proposed by evaluating performance on MARIDA dataset. The outcome highlights the need of precise reference for fair evaluation.