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AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 2
AGILE GIScience Ser., 2, 32, 2021
https://doi.org/10.5194/agile-giss-2-32-2021
AGILE GIScience Ser., 2, 32, 2021
https://doi.org/10.5194/agile-giss-2-32-2021

  04 Jun 2021

04 Jun 2021

Identification of Microplastics in Soils Using 2D Geometric Shape Descriptors

Irada Ismayilova1, Tabea Zeyer2, and Sabine Timpf1 Irada Ismayilova et al.
  • 1Geoinformatics Group, Institute of Geography, University of Augsburg, Augsburg, Germany
  • 2Water and Soil Resources Research, Institute of Geography, University of Augsburg, Augsburg, Germany

Keywords: microplastic, shape descriptors, machine learning

Abstract. Microplastics (MP), until now mostly studied in aquatic ecosystems, are also largely polluting terrestrial ecosystems, especially soil systems. Overall, there is a lack of robust and fast methods to identify, separate and eliminate MPs from soils. This paper is a first attempt to use 2D shape descriptors and Random Forest Machine Learning method in order to discriminate soil and MP particles. The results of this study demonstrate promising potential of the Machine Learning approach and shape descriptors in this relatively new scientific field of determining MPs in soils.

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