# Assessment of Spectral based Solutions for the Detection of Floating Marine Debris

arXiv: https://arxiv.org/abs/2408.10187 · HTML: https://mlsanity.com/paper/2408.10187/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [High-precision Density Mapping of Marine Debris and Floating Plastics via Satellite Imagery](https://mlsanity.com/paper/2210.05468/) (2022)
- [AI techniques for near real-time monitoring of contaminants in coastal waters on board future Phisat-2 mission](https://mlsanity.com/paper/2404.19586/) (2024)
- [Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data](https://mlsanity.com/paper/2401.03792/) (2024)
- [On Advances, Challenges and Potentials of Remote Sensing Image Analysis in Marine Debris and Suspected Plastics Monitoring](https://mlsanity.com/paper/2210.06090/) (2022)
- [ResAttUNet: Detecting Marine Debris using an Attention activated Residual UNet](https://mlsanity.com/paper/2210.08506/) (2022)

## Abstract (arXiv)

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.