TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITH LEARNED SPATIAL FEATURES USING SENTINEL 2

To navigate in the Arctic, vessels utilise maps of the local sea ice conditions, that are manually drawn daily. This is a time-consuming and labour-intensive task, which we are trying to automate using convolutional neural networks. In this article, we investigate combining the outputs of models trained with different objectives, leveraging the strengths and avoiding weaknesses to create precise and detailed sea ice concentration maps for navigation in the Arctic and climate or weather models.