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

Robert Rettig (), Felix Becker, Alexander Berghoff, Tobias Binkele, Wolfram Michael Butter, Tilman Floehr, Martin Kumm, Carolin Leluschko, Florian Littau, Elmar Reinders, Eike Rodenbäck, Tobias Schmid, Sabine Schründer, Sören Schweigert, Michael Sinhuber, Jens Wellhausen, Frederic Stahl and Christoph Tholen
Additional contact information
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

Data, 2025, vol. 10, issue 7, 1-10

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.

Keywords: anthropogenic litter; plastic litter pollution; litter object detection; dataset annotation; multi-resolution; multi-sensor; multi-class; multispectral data; remote sensing (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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