# High-precision Density Mapping of Marine Debris and Floating Plastics via Satellite Imagery

arXiv: https://arxiv.org/abs/2210.05468 · HTML: https://mlsanity.com/paper/2210.05468/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 58 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)

## Built on by (most cited first, 5 of 5)

- [Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2](https://mlsanity.com/paper/2307.02465/) (2023)
- [ResAttUNet: Detecting Marine Debris using an Attention activated Residual UNet](https://mlsanity.com/paper/2210.08506/) (2022)
- [DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery](https://mlsanity.com/paper/2311.13716/) (2023)
- [Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction](https://mlsanity.com/paper/2502.06062/) (2025)
- [Assessment of Spectral based Solutions for the Detection of Floating Marine Debris](https://mlsanity.com/paper/2408.10187/) (2024)

## Abstract (arXiv)

Combining multi-spectral satellite data and machine learning has been suggested as a method for monitoring plastic pollutants in the ocean environment. Recent studies have made theoretical progress regarding the identification of marine plastic via machine learning. However, no study has assessed the application of these methods for mapping and monitoring marine-plastic density. As such, this paper comprised of three main components: (1) the development of a machine learning model, (2) the construction of the MAP-Mapper, an automated tool for mapping marine-plastic density, and finally (3) an evaluation of the whole system for out-of-distribution test locations. The findings from this paper leverage the fact that machine learning models need to be high-precision to reduce the impact of false positives on results. The developed MAP-Mapper architectures provide users choices to reach high-precision (textitabbv. -HP) or optimum precision-recall (textitabbv. -Opt) values in terms of the training/test data set. Our MAP-Mapper-HP model greatly increased the precision of plastic detection to 95%, whilst MAP-Mapper-Opt reaches precision-recall pair of 87%-88%. The MAP-Mapper contributes to the literature with the first tool to exploit advanced deep/machine learning and multi-spectral imagery to map marine-plastic density in automated software. The proposed data pipeline has taken a novel approach to map plastic density in ocean regions. As such, this enables an initial assessment of the challenges and opportunities of this method to help guide future work and scientific study.