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Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach

Author

Listed:
  • Robert Rettig

    (German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany)

  • Felix Becker

    (German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany)

  • Alexander Berghoff

    (Optimare Systems GmbH, 27572 Bremerhaven, Germany)

  • Tobias Binkele

    (Optimare Systems GmbH, 27572 Bremerhaven, Germany)

  • Wolfram Michael Butter

    (German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany)

  • Tilman Floehr

    (everwave GmbH, 52062 Aachen, Germany)

  • Martin Kumm

    (Department of Engineering Sciences, Jade University of Applied Sciences, 26389 Wilhelmshaven, Germany)

  • Carolin Leluschko

    (German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany)

  • Florian Littau

    (Optimare Systems GmbH, 27572 Bremerhaven, Germany)

  • Elmar Reinders

    (Optimare Systems GmbH, 27572 Bremerhaven, Germany)

  • Eike Rodenbäck

    (German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany)

  • Tobias Schmid

    (Department of Engineering Sciences, Jade University of Applied Sciences, 26389 Wilhelmshaven, Germany)

  • Sabine Schründer

    (everwave GmbH, 52062 Aachen, Germany)

  • Sören Schweigert

    (Optimare Systems GmbH, 27572 Bremerhaven, Germany)

  • Michael Sinhuber

    (Optimare Systems GmbH, 27572 Bremerhaven, Germany)

  • Jens Wellhausen

    (Department of Engineering Sciences, Jade University of Applied Sciences, 26389 Wilhelmshaven, Germany)

  • Frederic Stahl

    (German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany)

  • Christoph Tholen

    (German Research Center for Artificial Intelligence, 26129 Oldenburg, Germany)

Abstract

The dataset developed within the PlasticObs+ project aims to facilitate a multi-resolution approach for detecting and quantifying anthropogenic litter through areal images. Traditional detection methods often suffer from narrow, use-case-specific limitations, reducing their transferability. To address this, an image dataset was created featuring various spatial and spectral resolutions. The highest spatial resolution images (ground sampling distance = 0.2 cm) were used to generate a labeled dataset, which was georeferenced for mapping onto coarser-resolution images.

Suggested Citation

  • Robert Rettig & Felix Becker & Alexander Berghoff & Tobias Binkele & Wolfram Michael Butter & Tilman Floehr & Martin Kumm & Carolin Leluschko & Florian Littau & Elmar Reinders & Eike Rodenbäck & Tobia, 2025. "Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach," Data, MDPI, vol. 10(7), pages 1-10, July.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:7:p:113-:d:1697780
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