Bibliographies

Zhang, P., Du, P., Guo, S., Zhang, W., Tang, P., Chen, J., and Zheng, H.(2022). A novel index for robust and large-scale mapping of plastic greenhouse from Sentinel-2 images, Remote Sens. [...]

Watanabe, J.-I., Shao, Y., and Miura, N. (2019). Underwater and airborne monitoring of marine ecosystems and debris, J. Appl. Rem. Sens., 13, 044509, https://doi.org/10.1117/1.JRS.13.044509

Valdenegro-Toro, M. (2019) . Deep neural networks for marine debris detection in sonar images. PhD Thesis Heirot-Watt University, Edinburgh, United Kingdom, ArXiv e-prints, 1-241, [...]

Taggio, N., Aiello, A., Ceriola, G., Kremezi, M., Kristollari, V., Kolokoussis, P., Karathanassi, V., and Barbone, E.(2022) A combination of machine learning algorithms for marine plastic litter [...]

Simpson, M. D., Marino, A., de Maagt, P., Gandini, E., Hunter, P., Spyrakos, E., Tyler, A., and Telfer, T. (2022) Monitoring of plastic islands in river environment using Sentinel-1 SAR data, [...]

Savastano, S., Cester, I., Perpinyà, M., and Romero, L. (2021) . A first approach to the automatic detection of marine litter in SAR images using artificial intelligence, in 2021 IEEE [...]

Serranti, S., Palmieri, R., Bonifazi, G., and Cózar, A.(2018).  Characterization of microplastic litter from oceans by an innovative approach based on hyperspectral imaging, Waste Management 76, [...]

Ryan, P. G.(2020). Using photographs to record plastic in seabird nests, Mar. Pollut. Bull., 156, 111262, https://doi.org/10.1016/j.marpolbul.2020.111262

Pinto, L., Andriolo, U., and Gonçalves, G. (2021). Detecting stranded macro-litter categories on drone orthophoto by a multi-class Neural Network, Mar. Pollut. Bull., 169, 112594, [...]

Nakashima, E., Isobe, A., Magome, S., Kako, S. i., and Deki, N. (2011). Using aerial photography and in situ measurements to estimate the quantity of macro-litter on beaches, Mar. Pollut. Bull., [...]