## 6 Discussion and Conclusions  
In this paper, we present a comprehensive comparison of CNN and Transformer-based models for the classification of volcanic activity. Our results indicate that both model families have their strengths and weaknesses, with Transformer-based models, like the SwinT, excelling in specific challenging categories, such as 'Cooling or Lavaflow'. However, CNN models, like 'VGG16' and 'GoogLeNet', showed comparable or better performance in simpler and more localized tasks. While there are no direct comparisons with other studies using Transformers for volcanic activity classification, our work can be contextualized within the broader literature on image classification. Previous studies using CNNs for volcanic activity classification showed promising results, but our study demonstrated that Transformer-based models can offer additional advantages. For instance, the Transformers' ability to capture long-range dependencies and global context can be particularly beneficial for challenging classes where local features alone may not be sufficient. The scientific novelty of our study lay in the extensive comparison between the most representative Convolutional Neural Networks (CNNs) and the latest Transformer networks. Unlike previous studies, our work highlighted the potential of Transformer networks in specialized applications, such as volcanic activity monitoring, where accurate and reliable image classification is crucial. This study evaluated and compared the performance of CNN-based and Transformer-based classifiers for volcanic activity image classification. The results highlight key insights into the strengths and limitations of both approaches, offering a clearer perspective on their applicability to this specific task. Overall, for a relatively straightforward classification problem, the Transformer-based models did not exhibit a significant advantage over CNNs. Both approaches demonstrated strong performances, albeit with distinct characteristics. CNNs, exemplified by models like 'AlexNet' and 'ShuffleNet', were computationally efficient, with faster training and inference times, making them particularly suitable for real-time applications. Transformers, such as the 'SwinT' and 'ViT', provided slightly improved accuracy in certain scenarios, particularly when dealing with more complex or ambiguous classes, showcasing their potential for tasks requiring greater handling of inter-class confusion. An analysis of the confusion matrices indicated that while the Transformer-based models, such as the 'SwinT', excelled at specific challenging categories, like 'Cooling or Lavaflow', they did not consistently manage inter-class confusion more effectively than CNNs across all the classes. For instance, the CNN models showed comparable or better performance for classes such as 'Cloudy' and 'No Activity'. Both model families struggled with visually similar classes, such as 'Degassing' vs. 'No Activity' or 'Strombolian' vs. other eruptive behaviors. These challenges underscore the need for expanding and diversifying the dataset to capture subtle distinctions between similar classes. In terms of real-time monitoring, lightweight CNN architectures, like 'AlexNet' and 'ShuffleNet', excelled due to their speed and efficiency. Although the Transformer-based models were marginally slower, they remained competitive in terms of inference times while delivering high accuracies, making them a viable option for deployment in scenarios where accuracy is a higher priority. In conclusion, while Transformer-based models show promise, their advantages over CNNs for this specific application remain modest. CNNs continue to be a reliable, efficient, and highly effective choice for volcanic activity classification. However, the benefits of Transformer-based models may become more evident in future work, particularly when addressing more complex tasks, such as developing a unified classifier for monitoring multiple volcanic activity stations.