Bibliographies

Pichel, W. G., Veenstra, T. S., Churnside, J. H., Arabini, E., Friedman, K. S., Foley, D. G., Brainard, R. E., Kiefer, D., Ogle, S., Clemente-Colón, P. and Li, X. (2012) GhostNet marine debris [...]

Mohsen, A., Kiss, T. and Kovács, F. (2023) Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery. Environ. Sci. Pollut. Res., 30, 67742–67757, [...]

Mifdal, J., Longépé, N. and Rußwurm, M. (2021) Towards detecting floating objects on a global scale with learned spatial features using Sentinel 2. ISPRS Ann. Photogramm. Remote Sens. Spatial [...]

Matthews, J. P., Ostrovsky, L., Yoshikawa, Y., Komori, S. and Tamura, H. (2017) Dynamics and early post-tsunami evolution of floating marine debris near Fukushima Daiichi. Nature Geosci., 10, [...]

Hu, C., Qi, L., Wang, M. and Park, Y.-J. (2023) Floating debris in the Northern Gulf of Mexico after Hurricane Katrina. Environ. Sci. Technol., 57(28), 10373−10381, [...]

Gómez, À. S., Scandolo, L., and Eisemann, E. (2022) A learning approach for river debris detection. Int. J. Appl. Earth Obs. Geoinformation, 107, 102682, https://doi.org/10.1016/j.jag.2022.102682

Duarte, M. M., and Azevedo, L. (2023) Automatic detection and identification of floating marine debris using multi-spectral satellite imagery. IEEE Trans. Geosci. Remote Sens., 1-15, [...]

Chia, K. Y., Chin, C. S., and See, S. (2023), Deep transfer learning application for intelligent marine debris detection, in Proceedings EANN 2023: Engineering Applications of Neural Networks, [...]

Cocking, J., Narayanaswamy, B. E., Waluda, C. M., and Williamson, B. J. (2022) Aerial detection of beached marine plastic using a novel, hyperspectral short-wave infrared (SWIR) camera. ICES J. [...]

Booth, H., Ma, W., and Karakuş, O. (2023) High-precision density mapping of marine debris and floating plastics via satellite imagery. Sci. Rep., 13(1), 6822, https://doi.org/10.1038/s41598-023-33612-2