{"ob_id":37680,"computationComponent":[{"ob_id":37679,"uuid":"d129f860b47b40fdafd57a5b69457926","title":"shiptrack_semantic_segmentation_v1","abstract":"A convolutional neural network with a Unet architecture, with a RESNET-152 backbone, trained to segment shiptrack clouds from enhanced day_microphysics imagery from AQUA MODIS Input Description AQUA MODIS level 1B day microphysics composite granules, enhanced with histogram stretch. Output Description Netcdf files with a single variable 'shiptracks' that contains shiptrack inference values and shares the coordinates of the original AQUA MODIS granule from which they are derived. Post-processing is required to extract contours and filter them by brightness temperature to obtain final results used in publication. Software Reference https://github.com/duncanwp/shiptrack-detection","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"identifier_set":[]}],"acquisitionComponent":[{"ob_id":26034,"independentInstrument":[],"instrumentplatformpair_set":[{"ob_id":11766,"platform":"https://catalogue.ceda.ac.uk/api/v2/platforms/10906/?format=json","instrument":"https://catalogue.ceda.ac.uk/api/v2/instruments/10898/?format=json","relatedTo":{"ob_id":26034,"uuid":"85bb8321bc8b42f9a39cb6d83fabe79e","short_code":"acq"}}]}],"identifier_set":[],"responsiblepartyinfo_set":["https://catalogue.ceda.ac.uk/api/v2/rpis/179709/?format=json","https://catalogue.ceda.ac.uk/api/v2/rpis/179710/?format=json"]}