  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 ($\textit{abbv.}$ -HP) or optimum precision-recall
($\textit{abbv.}$ -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.
