Future Works

Considering this thesis's time and resource limitations, further work can build upon what was done. Below are suggested some possible work that could take place:

• The CNN-AE architecture used with D2 could be further regularized to improve the generalization error.

• To understand whether the features extracted by approaches addressed in this thesis can be used as input to the MATISSE team's forecasting model currently in development.

• Considering that the Sentinel-3 data only dates back to 2017, Sentinel-2 data can be used to extract information from earlier years, which could prove helpful for the MATISSE project, considering that the marine toxin data in Portugal dates back to 2015.

• Data could be extracted from regions further out from the coastline to determine whether it contains valuable information for the toxin forecasting task.

• The effect of the patch sizes on the model performance and its utility for downstream tasks could also be explored.

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A

PCA-256 ^CLUSTERS

This appendix contains random patches of 4 random clusters resulting from the training of the DBSCAN model on the PCA-256 generated encodings presented in Section 6.3.5.