# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 6 Conclusions  
In recent years, CNNs have become one of the most popular deep learning methods for image classification across various domains. Thus, we explored the potential of a deep CNN model, which has efficient and robust image processing capabilities, to automatically detect thermal anomalies associated with the eruptive activity of a volcano. The most frequent applications of current remote sensing processing methods based on deep CNNs concern land use classification and feature target recognition [61; 62; 63], and very limited studies have been conducted for volcano monitoring and forecasting. However, the use of deep learning in volcanology is rapidly growing (i.e., [17]), even if the unavailability of large and labeled training datasets presents a key issue in improving the performance of CNNs for volcanic applications. CNNs have shown the ability to overcome limits related to purely intensity-based approaches in detecting the presence of volcanic activity. Different convolutional neural networks have already been developed and trained with enormous datasets to create a model robust and capable enough to detect objects and classify images with high performances. All of this knowledge can be transferred to a new domain, using the transfer learning approach, fine-tuning the parameters over the new images. In this way, it is possible to readapt a model for the same task of classification or detection in a new domain of images, without wasting time or computational resources. We used high spatial resolution infrared satellite data, which are fundamental to monitor hot temperature volcanic events, and adopted this transfer learning approach with the pre-trained SqueezeNet CNN. This allowed us to readjust the model more quickly and accurately to the volcanic domain, using a balanced dataset containing infrared images acquired by Sentinel-2 and Landsat 8 satellites. We collected 200 images representative of two different classes, one containing volcanic thermal anomalies (active flows and erupting events) and the other one not containing thermal anomalies (volcanoes at rest), to fine-tune the weights that the SqueezeNet model had learned with the ImageNet dataset and to test the SqueezeNet model. In this way, we obtained a robust model capable of detecting the presence of active flows or erupting events in the images under analysis. The comparison of this model with threshold algorithms and other machine learning approaches showed that the SqueezeNet achieves the highest performance score, making it possible to detect the presence of volcanic thermal anomalies with an accuracy of 98.3% using Sentinel-2 and Landsat 8 satellite data. Thus, the application of transfer and ensemble learning approaches greatly reduces computational costs and training times and improves the level of accuracy. However, scenes that contain subtle or thermal anomalies predominantly covered by clouds may be incorrectly classified as \"No Anomalies\". This situation further complicates the classification process, posing challenges to accurately distinguish and identify volcanic thermal activity. Future works are needed to improve this aspect by combining our approach with image segmentation techniques. The computational advances in deep learning frameworks, coupled with the ever-growing satellite constellation and continuing improvements in sensor technology, present unique opportunities for improving our ability to identify renewed volcanic activity using space-based observations. Moreover, virtual constellations of sensors such as Landsat 8 and Sentinel-2, that are in sun-synchronous orbits with different overpass times, provide the opportunity for more frequent and cloud-free surface observations when used together rather than alone. Combined together, the Landsat 8, Sentinel-2A and Sentinel-2B sensors permit a revisit interval of about 2-3 days [64]. Such frequent revisit times at a spatial resolution of 10 m to 30 m are better suited to capturing thermal changes that could indicate the beginning of renewed volcanic activity.