Targeting Plastics: Machine Learning Applied to Litter Detection in Aerial Multispectral Images

Abstract

The occurrence of litter in natural areas is nowadays one of the major environmental challenges. The uncontrolled dumping of solid waste in nature not only threatens wildlife on land and in water, but also constitutes a serious threat to human health. The detection and monitoring of areas affected by litter pollution is thus of utmost importance, as it allows for the cleaning of these areas and guides public authorities in defining mitigation measures. Among the methods used to spot littered areas, aerial surveillance stands out as a valuable alternative as it allows for the detection of relatively small such regions while covering a relatively large area in a short timeframe. In this study, remotely piloted aircraft systems equipped with multispectral cameras are deployed over littered areas with the ultimate goal of obtaining classification maps based on spectral characteristics. Our approach employs classification algorithms based on random forest approaches in order to distinguish between four classes of natural land cover types and five litter classes. The obtained results show that the detection of various litter types is feasible in the proposed scenario and the employed machine learning algorithms achieve accuracies superior to 85% for all classes in test data. The study further explores sources of errors, the effect of spatial resolution on the retrieved maps and the applicability of the designed algorithm to floating litter detection.

On the Co-Registration of Asynchronous Multi-Spectral and Thermal Images

“…In particular, different tools and methods have been used over the years to detect and monitor floating plastic objects in fresh and salt water. Some researchers used in-situ visual census for their work (Galgani et al, 2013, Geraeds et al, 2019, but most used images or orthophotos obtained from satellite (Tasseron et al, 2021, Themistocleous et al, 2020, Topouzelis et al, 2020 or Unmanned Aerial Vehicle (UAV) imagery (Cortesi et al, 2022, Jakovljevic et al, 2020, Iordache et al, 2022, Cortesi et al, 2023.…”