Few shot Classification for Meso-cloud formations

Abstract

Automated cloud classification is severely bottlenecked by the need for massive, region-specific annotated datasets. This thesis investigates few-shot learning (FSL) approaches for the classification of mesoscale cloud formations across different geographic regions under limited data conditions.

To address this problem, the influence of varying amounts of additional data on generalisation to novel cloud regimes is evaluated. The study compares representation learning strategies, including self-supervised and supervised approaches, to assess their effectiveness in structuring the latent space for distinguishing cloud types. In parallel, two learning paradigms, transfer learning and episodic meta-learning, are analysed to determine how effectively they incorporate additional data when adapting to novel classes.

The results show that self-supervised learning is most effective in the strict few-shot regime, while supervised transfer learning makes the most effective use of additional data. In particular, Barlow Twins achieves the strongest performance under minimal data by avoiding reliance on noisy labels. When additional out-of-domain data, such as ImageNet and auxiliary cloud datasets, is introduced, supervised pre-training combined with transfer learning attains performance comparable to standard supervised learning, while requiring only a small number of labelled examples.