# MEC: A Mesoscale Events Classifier for Oceanographic Imagery
## 6 Conclusions  
This work describes MEC, a system to classify mesoscale events (e.g., upwelling, eddies, thermal fronts), observed as patterns in SST data. It is known that the development of these phenomena shapes the hydrodynamics, habitat, and species distribution of a specific region. Therefore, the detection and tracking of the related features is of fundamental importance in what concerns fisheries management and exploitation, coastal monitoring, and climate change. MEC represents an attempt to introduce the benefits of a computer-based automation within a data analysis task which at present is, to the best of the authors' knowledge, extensively manual. The complete automation of such analysis encompasses a few challenges: in the first place the presence of noise, mainly due to clouds and other atmospheric phenomena, which implies that signal variations relevant for the description of the mesoscale features are corrupted or occluded to some extent. This is an important issue to take into account in case gradient-based methods are employed, since these methods are sensitive to the presence of spurious signals and may critically affect the classifier reliability. Within MEC, a preliminary filtering stage ensures that only major quality data, i.e., SST maps containing a percentage of reliable data above a user-defined threshold, are fed to the classifier. Additionally, the extension of the analysis from a single image approach to an entire sequence of consecutive images collected over a given time window allows to tackle problems related to cloud occlusions and ensures an increase in the accuracy in the statistics estimation. A second challenge is represented by the lack of valid analytical models for the geometric shapes of the structures emerging in a mesoscale event. Indeed, considering any single class of mesoscale events and analysing the corresponding occurrences in the dataset, the conclusion is that the modelling of a specific template is not feasible. This synthesis operation, that would enable the proposal of a pattern-matching approach to the problem, is prevented by the strong and erratic morphological variability, clearly assessed in the data. The approach proposed in MEC once more leverages on the joint analysis of the SST spatial and temporal variability. In the absence of well defined signal patterns the classification task is carried out within an enlarged domain, featuring both space and time parameters, where the clustering process takes simultaneous advantage of the peculiar SST spatial arrangement/configurations and also of the temporal factors that allow to detect the onset of an event and discern its category. The activities discussed in this paper have been realised within the framework of the EU H2020 project NAUTILOS [45]. Conceptualization, G.P., J.J., F.M., O.P. and M.R. ; methodology, G.P., J.J., F.M., O.P. and M.R. ; software, O.P. ; validation, J.J. and F.M. ; writing--original draft preparation, G.P., J.J., F.M., O.P. and M.R. ; writing--review and editing, G.P., J.J., F.M., O.P. and M.R. All authors have read and agreed to the published version of the manuscript. This paper is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101000825 (NAUTILOS; [[https://www.nautilos-h2020.eu/](https://www.nautilos-h2020.eu/)]([https://www.nautilos-h2020.eu/](https://www.nautilos-h2020.eu/)), accessed on 19 January 2023). This study had the support of Fundacao para a Ciencia e Tecnologia (FCT), under the projects LA/P/0069/2020 (Associate Laboratory ARNET) and UID/00350/2020 CIMA. Not applicable.