Floating marine debris is a global pollution problem which leads to the loss of marine and terrestrial biodiversity. Large swaths of marine debris are also navigational hazards to ocean vessels. The use of Earth observation data and artificial intelligence techniques can revolutionize the detection of floating marine debris on satellite imagery and pave the way to a global monitoring system for controlling and preventing the accumulation of marine debris in oceans. This dataset consists of images of marine debris which are 256 by 256 pixels in size and labels which are bounding boxes with geographical coordinates. The images were obtained from PlanetScope optical imagery which has a spatial resolution of approximately 3 meters. In this dataset, marine debris consists of floating objects on the ocean surface which can belong to one or more classes namely plastics, algae, sargassum, wood, and other artificial items. Several studies were used for data collection and validation. While a small percentage of the dataset represents the coastlines of Ghana and Greece, most of the observations surround the Bay Islands in Honduras. The marine debris detection models created and the relevant code for using this dataset can be found here.
Shah, A., Thomas, L., & Maskey, M. (2021) "Marine Debris Dataset for Object Detection in Planetscope Imagery", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.9r6ekg
from radiant_mlhub import Dataset ds = Dataset.fetch('nasa_marine_debris') for c in ds.collections: print(c.id)
Planetscope Source Imagery
Marine Debris Labels