Large quantities of mismanaged plastic waste are polluting and threatening the health of the blue planet. As such, vast amounts of this plastic waste found in the oceans originates from land. It finds its way to the open ocean through rivers, waterways and estuarine systems. Here we present a novel machine learning algorithm based on convolutional neural networks (CNNs) that is capable of detecting and quantifying floating and washed ashore plastic litter. The aquatic plastic litter detection, classification and quantification system (APLASTIC-Q) was developed and trained using very high geo-spatial resolution imagery (∼5 pixels cm⁻¹ = 0.002 m pixel⁻¹) captured from aerial surveys in Cambodia. APLASTIC-Q was made up of two machine learning components (i) plastic litter detector (PLD-CNN) and (ii) plastic litter quantifier (PLQ-CNN). PLD-CNN managed to categorize targets as water, sand, vegetation and plastic litter with an 83% accuracy. It also provided a qualitative count of litter as low or high based on a thresholding approach. PLQ-CNN further distinguished and enumerated the litter items in each of the classes defined as water bottles, Styrofoam, canisters, cartons, bowls, shoes, polystyrene packaging, cups, textile, carry bags small or large. The types and amounts of plastic litter provide benchmark information that is urgently needed for decision-making by policymakers, citizens and other public and private stakeholders. Quasi-quantification was based on automated counts of items present in the imagery with caveats of underlying object in case of aggregated litter. Our scientific evidence-based machine learning algorithm has the prospects of complementing net trawl surveys, field campaigns and clean-up activities for improved quantification of plastic litter. APLASTIC-Q is a smart algorithm that is easy to adapt for fast and automated detection as well as quantification of floating or washed ashore plastic litter from aerial, high-altitude pseudo satellites and space missions.

Concept drift detection techniques can be used to discover substantial changes of the patterns encoded in data streams in real-time. If left unaddressed, these changes can render deployed machine learning models unreliable because their training data no longer matches the patterns present in the data stream. Most algorithms proposed in the literature depend on the immediate availability of ground truth class labels. This is unrealistic for many applications due to the associated cost of labeling. Therefore, this study reviews the availability of fully unsupervised concept drift detectors, which can operate entirely without labeled data. Ten algorithms are analyzed in terms of architectural choices, core ideas and assumptions about data because they fulfilled several inclusion criteria designed to ensure faithful and reliable implementations. Seven of these algorithms are evaluated with common concept drift detection metrics on eleven real-world data streams; the remaining three performed too slow or depended on chance. Based on the results of these experiments, three concept drift detectors—Discriminative Drift Detector, Image-Based Drift Detector and Semi-Parametric Log-Likelihood—can be recommended depending on the desired target metric. This study further reveals issues with the evaluation metrics Mean Time Ratio and lift-per-drift. Finally, it highlights open research challenges.