# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 1 Introduction
The SqueezeNet trained with the ImageNet dataset achieves a satisfactory balance between training time and classification accuracy [33, 31]. Moreover, the SqueezeNet network has the advantage of being among the most compact and fastest networks for classification tasks. Its principal repeating module, the \"fire\" module, is composed of a \"squeeze\" convolutional module, followed by an \"expand\" module, and finally, a softmax function is used for the classification [31]. Although some volcanological applications rely on high volumes of data, a general issue is the limited availability of labeled datasets as ground truth, i.e., the real labels that show the real belonging class of the data [25, 34, 35]. The performance of deep learning algorithms may be negatively influenced by the limited representative training samples, for example, due to the overfitting problem, where the model is excessively complex relative to the small dataset, and thus, is not being able to generalize to unseen data [2]. However, it is possible to apply pre-trained ImageNet models to small datasets and reduce training time using the technique of transfer learning in a deep learning framework (e.g., [36]). This technique is based on the concept in which a Neural Network already trained with a large dataset and with high performances is subsequently retrained with some images from a different domain and a small number of epochs [2], with the aim of classifying the images under analysis (or other tasks), using the fine-tuning approach [37]. In this way, it is possible to readapt the parameters (weights) of the original model to the new domain, exploiting the potentiality already developed with the previous training data, rapidly and in an efficient way, re-setting all the parameters and re-running all the network with the new dataset and few epochs, instead of learning from scratch [38]. Here, we present a monitoring system to automatically detect volcanic thermal anomalies associated with eruptive activity. In particular, we explore the potential of the SqueezeNet model already trained with the ImageNet dataset (SqueezeNet 1.0 model website) to identify the presence of high temperature volcanic features related to active flows (such as lava flows, pyroclastic flows, warm lahars) and erupting vents (such as lava lake, lava fountains, hot fumaroles) that would appear in the thermal infrared satellite measurements. We use advanced learning techniques, which strongly reduce training times and improve the accuracy level. Thus, we applied the transfer learning technique to readjust the parameters of the pre-trained SqueezeNet model, using a new image dataset containing high temperature volcanic features representative of two classes, those belonging to thermal anomalies related to active flows and vents (i.e., eruptive activity) and those not belonging to thermal anomalies (volcanoes at rest). SqueezeNet's training time and verification sample accuracy are optimal, using a small number of epochs and a limited number of images for the fine-tuning. In addition, an ensemble approach is used to achieve higher accuracy and robustness compared to individual models. We investigated the thermal features of several active volcanoes worldwide, using the thermal images (infrared) captured by the high spatial resolution ESA Sentinel-2 (from 10 m to 60 m) and NASA & USGS Landsat 8 satellites (from 30 m to 100 m). All the satellite images were obtained via Google Earth Engine (GEE) cloud-based platform. GEE is appealing not only because of its free access, but also because it makes available a powerful application programming interface (API) for processing the satellite data [39]. The SqueezeNet model was developed in Google Colaboratory (Colab), a cloud platform that can easily integrate GEE and machine learning and deep learning applications for data analysis [40].