Explainable Expert-in-the-loop sea-ice classification with statistical models

Abstract

Sea ice classification is often a crucial step to predict climatic insights and ensure safe marine navigation. In the last few decades, satellite information has been widely used to classify sea ice in broad areas for practical applications. However, common problems are: 1) Low resolution of satellite images to provide precise classification, 2) High computational need, and 3) Scarcity of general models to discover unknown patterns in the data, especially those that enable free selection of satellite sensors to fit the application at hand. We propose an explainable unsupervised model to integrate ice-experts’ inputs to models so that the problem of having low-resolution data can be overcome. In other words, the results of the models, given as semantic maps, can be further refined using inputs from ice-experts. Model explainability and visual interpretation of models serve as tools to talk to’ domain experts. The use of Explainable AI in such vital activities ensures trust and easy detection of error. We present an example from a sea ice classification with Sentinel-1 time-series in the scope of the Horizon 2020 project ExtremeEarth. A further example from the Horizon Europe project dAIEdge demonstrates the use of these explainable models for ‘on-the-edge’ inference.