Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach
Abstract
1. Summary
2. Data Description
2.1. Drone Data
2.2. Airplane Data
2.3. Additional Data
3. Methods for Data Acquisition and Processing
3.1. Drone Data Acquisition
3.2. Annotation of the Drone Data
3.3. VIS/Airplane Data Acquisition
4. Experimental Verification
5. User Notes
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
GCP | Ground control point |
IMU | Inertial Measurement Unit |
RGB | Red-green-blue |
RTK | Real-Time Kinematic |
Appendix A
Label (Object_Type-Material_Type-Main_Category-Condition 1) | Frequency |
---|---|
Others-Others-Others | 2348 |
Piece-Paper-Unknown | 1135 |
Piece-Plastic-Unknown | 931 |
Can-Metal-Food_and_Drink | 804 |
Cup-Plastic-Food_and_Drink | 755 |
Wrapper-Plastic-Food_and_Drink | 598 |
Bags_transparent-Plastic-Cleaning_and_Cosmetic | 339 |
Bags_transparent-Plastic-Food_and_Drink | 217 |
Bottle-Plastic-Food_and_Drink | 160 |
Tetra_Pak-Plastic-Food_and_Drink | 152 |
Bags_non_transparent-Plastic-Cleaning_and_Cosmetic | 139 |
Piece-Cardboard-Logistic_and_Transport | 130 |
Box-Cardboard-Logistic_and_Transport | 115 |
Fabric-Plastic-Unknown | 113 |
Bottle-Glas-Food_and_Drink | 101 |
Bags_non_transparent-Plastic-Food_and_Drink | 88 |
Cap-Plastic-Food_and_Drink | 86 |
Cup-Cardboard-Food_and_Drink | 74 |
Container-Plastic-Food_and_Drink | 74 |
Tray-Plastic-Food_and_Drink | 63 |
Bowl-Plastic-Food_and_Drink | 62 |
Box-Cardboard-Food_and_Drink | 61 |
Textiles-Textiles-Unknown | 58 |
Piece-Metal-Unknown | 57 |
Shoe-Textiles-Clothing | 53 |
Canister-Plastic-Food_and_Drink | 42 |
Food-Organic-Food_and_Drink | 35 |
Tray-Cardboard-Food_and_Drink | 33 |
Foam-Plastic-Logistic_and_Transport | 30 |
Bottle-Plastic-Cleaning_and_Cosmetic | 28 |
Lid-Plastic-Food_and_Drink | 24 |
Container-Plastic-Cleaning_and_Cosmetic | 23 |
Rope-Unknown-Unknown | 20 |
Straw-Plastic-Food_and_Drink | 15 |
Medical_package-Plastic-Cleaning_and_Cosmetic | 13 |
Sponge-Plastic-Cleaning_and_Cosmetic | 13 |
Piece-Rubber-Unknown | 9 |
Bottle-Metal-Cleaning_and_Cosmetic | 9 |
Coal-Others-Unknown | 9 |
Lid-Metal-Food_and_Drink | 7 |
Piece-Organic-Natural | 7 |
Piece-Glas-Unknown | 6 |
Pipe-Plastic-Construction | 5 |
Piece-Lumber-Construction | 3 |
Furniture-Lumber-Others | 3 |
Other_Net-Plastic-Unknown | 3 |
Trouser-Textiles-Clothing | 2 |
Piece-Ceramic-Unknown | 1 |
Label 1 | Frequency |
---|---|
1_Plastic | 3974 |
300_Others | 2655 |
5_Paper | 1548 |
7_Metal | 877 |
References
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Name | Resolution | Dates | Time (UTC) | Georeferenced | Type |
---|---|---|---|---|---|
Label Dataset RGB High-Resolution (660 images) | 0.2 cm 1 | 25.06.2024 | 10:35–13:29 | Yes | PNG |
0.2 cm 1 | 26.06.2024 | 08:26–13:39 | Yes | ||
0.2 cm 1 | 27.06.2024 | 06:37–08:45 | Yes | ||
0.2 cm 1 | 28.06.2024 | 09:44–14:09 | No | ||
Label Dataset Multispectral (blue, green, red, red edge, near-infrared) | 2.8 cm | 25.06.2024 | 11:45–13:29 | Yes | TIF |
4.7 cm | 25.06.2024 | 12:45–13:05 | Yes | ||
0.89 cm | 26.06.2024 | 10:16–11:34 | Yes | ||
2.8 cm | 26.06.2024 | 11:36–11:47 | Yes | ||
4.7 cm 1 | 26.06.2024 | 11:36–11:47 | Yes | ||
0.89 cm | 27.06.2024 | 07:28–09:15 | Yes | ||
2.8 cm | 27.06.2024 | 08:15–08:26 | Yes | ||
4.7 cm | 27.06.2024 | 08:35–08:43 | Yes | ||
Orthomosaics | 2.74 cm | 25.06.2024 | 11:45–13:29 | Yes | TIF |
2.79 cm | 26.06.2024 | 11:36–11:47 | Yes | ||
2.80 cm | 27.06.2024 | 08:15–08:26 | Yes | ||
2.82 cm | 28.06.2024 | 08:09–08:15 | Yes |
Name | Resolution | Dates | Georeferenced | Type |
---|---|---|---|---|
20240625_001_HRVIS_162 | - | 25.06.2024 | No | PNG |
15.2 cm | 25.06.2024 | Yes | TIF | |
20240626_002_HRVIS_119 | - | 26.06.2024 | No | PNG |
20240628_003_HRVIS_45 | - | 28.06.2024 | No | PNG |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rettig, R.; Becker, F.; Berghoff, A.; Binkele, T.; Butter, W.M.; Floehr, T.; Kumm, M.; Leluschko, C.; Littau, F.; Reinders, E.; et al. Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach. Data 2025, 10, 113. https://doi.org/10.3390/data10070113
Rettig R, Becker F, Berghoff A, Binkele T, Butter WM, Floehr T, Kumm M, Leluschko C, Littau F, Reinders E, et al. Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach. Data. 2025; 10(7):113. https://doi.org/10.3390/data10070113
Chicago/Turabian StyleRettig, Robert, Felix Becker, Alexander Berghoff, Tobias Binkele, Wolfram Michael Butter, Tilman Floehr, Martin Kumm, Carolin Leluschko, Florian Littau, Elmar Reinders, and et al. 2025. "Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach" Data 10, no. 7: 113. https://doi.org/10.3390/data10070113
APA StyleRettig, R., Becker, F., Berghoff, A., Binkele, T., Butter, W. M., Floehr, T., Kumm, M., Leluschko, C., Littau, F., Reinders, E., Rodenbäck, E., Schmid, T., Schründer, S., Schweigert, S., Sinhuber, M., Wellhausen, J., Stahl, F., & Tholen, C. (2025). Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach. Data, 10(7), 113. https://doi.org/10.3390/data10070113