Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach
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 addre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2306-5729/10/7/113 |
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| author | 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 Christoph Tholen |
| author_facet | 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 Christoph Tholen |
| author_sort | Robert Rettig |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-861fc54daf264a77a5ff1a53beb0c3d9 |
| institution | Kabale University |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-861fc54daf264a77a5ff1a53beb0c3d92025-08-20T03:58:31ZengMDPI AGData2306-57292025-07-0110711310.3390/data10070113Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor ApproachRobert Rettig0Felix Becker1Alexander Berghoff2Tobias Binkele3Wolfram Michael Butter4Tilman Floehr5Martin Kumm6Carolin Leluschko7Florian Littau8Elmar Reinders9Eike Rodenbäck10Tobias Schmid11Sabine Schründer12Sören Schweigert13Michael Sinhuber14Jens Wellhausen15Frederic Stahl16Christoph Tholen17German Research Center for Artificial Intelligence, 26129 Oldenburg, GermanyGerman Research Center for Artificial Intelligence, 26129 Oldenburg, GermanyOptimare Systems GmbH, 27572 Bremerhaven, GermanyOptimare Systems GmbH, 27572 Bremerhaven, GermanyGerman Research Center for Artificial Intelligence, 26129 Oldenburg, Germanyeverwave GmbH, 52062 Aachen, GermanyDepartment of Engineering Sciences, Jade University of Applied Sciences, 26389 Wilhelmshaven, GermanyGerman Research Center for Artificial Intelligence, 26129 Oldenburg, GermanyOptimare Systems GmbH, 27572 Bremerhaven, GermanyOptimare Systems GmbH, 27572 Bremerhaven, GermanyGerman Research Center for Artificial Intelligence, 26129 Oldenburg, GermanyDepartment of Engineering Sciences, Jade University of Applied Sciences, 26389 Wilhelmshaven, Germanyeverwave GmbH, 52062 Aachen, GermanyOptimare Systems GmbH, 27572 Bremerhaven, GermanyOptimare Systems GmbH, 27572 Bremerhaven, GermanyDepartment of Engineering Sciences, Jade University of Applied Sciences, 26389 Wilhelmshaven, GermanyGerman Research Center for Artificial Intelligence, 26129 Oldenburg, GermanyGerman Research Center for Artificial Intelligence, 26129 Oldenburg, GermanyThe 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.https://www.mdpi.com/2306-5729/10/7/113anthropogenic litterplastic litter pollutionlitter object detectiondataset annotationmulti-resolutionmulti-sensor |
| spellingShingle | 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 Christoph Tholen Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach Data anthropogenic litter plastic litter pollution litter object detection dataset annotation multi-resolution multi-sensor |
| title | Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach |
| title_full | Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach |
| title_fullStr | Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach |
| title_full_unstemmed | Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach |
| title_short | Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach |
| title_sort | multi resolution remote sensing dataset for the detection of anthropogenic litter a multi platform and multi sensor approach |
| topic | anthropogenic litter plastic litter pollution litter object detection dataset annotation multi-resolution multi-sensor |
| url | https://www.mdpi.com/2306-5729/10/7/113 |
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