  Detecting and quantifying marine pollution and macro-plastics is an
increasingly pressing ecological issue that directly impacts ecology and human
health. Efforts to quantify marine pollution are often conducted with sparse
and expensive beach surveys, which are difficult to conduct on a large scale.
Here, remote sensing can provide reliable estimates of plastic pollution by
regularly monitoring and detecting marine debris in coastal areas.
Medium-resolution satellite data of coastal areas is readily available and can
be leveraged to detect aggregations of marine debris containing plastic litter.
In this work, we present a detector for marine debris built on a deep
segmentation model that outputs a probability for marine debris at the pixel
level. We train this detector with a combination of annotated datasets of
marine debris and evaluate it on specifically selected test sites where it is
highly probable that plastic pollution is present in the detected marine
debris. We demonstrate quantitatively and qualitatively that a deep learning
model trained on this dataset issued from multiple sources outperforms existing
detection models trained on previous datasets by a large margin. Our
experiments show, consistent with the principles of data-centric AI, that this
performance is due to our particular dataset design with extensive sampling of
negative examples and label refinements rather than depending on the particular
deep learning model. We hope to accelerate advances in the large-scale
automated detection of marine debris, which is a step towards quantifying and
monitoring marine litter with remote sensing at global scales, and release the
model weights and training source code under
https://github.com/marccoru/marinedebrisdetector
