# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 1 Introduction  
Thermal satellite imagery has the potential to offer important insights into the thermal behavior of the most active volcanoes around the world, which can help minimize the need for extensive fieldwork. As a result of the continuous improvement of Earth observation technology, a variety of thermal sensors (e.g., AVHRR, MODIS, SEVIRI, GOES and many others) are currently in orbit, providing coverage even for remote and unreachable volcanoes at different temporal resolutions, ranging from minutes to few days. These sensors acquire data over potentially hazardous, high-temperature phenomena, such as volcanic eruptions, with relatively low cost and no risk to the end user. However, the data volume collected by satellite thermal sensors is too large to be readily processed and analyzed on a daily basis and global scale with traditional techniques (i.e., those that employ a threshold). Therefore, an automated and fast approach to accurately detect these thermal anomalies in satellite data is a critical component to any operational volcano monitoring system. Overthe last years, Artificial Intelligence (AI) techniques have become the fastest-growing trend in remote sensing data analysis applications, which often involve large amounts of data. The new technologies of Artificial Intelligence, i.e., Machine Learning (ML) and Deep Learning (DL) in particular [1,2], could help the operator to manage these data efficiently. ML techniques are a type of AI in which computers learn from structured data, i.e., representative features are opportunely selected and extracted during the input feature engineering phase. In the specific context of satellite image processing, these values can be the set of pixel spectral intensities in three-bands images (e.g., RGB images) recording the spectral response of the monitored area under investigation [3, 4, 5, 6]. DL models are artificial neural networks capable of analyzing unstructured data, exploiting the spatial and/or temporal relationships between neighboring data to automatically extract significant features. The effectiveness of ML and DL algorithms in recognizing some volcanic features in optical and radar satellite images has already been tested. Machine learning models were used to identify and map the thermal emissions from active and cooling lava flows [5,7], and to estimate the areal coverage and the total volume of lava fields or deposits [8, 9, 10, 11, 12]. Machine learning and deep learning techniques were adopted to detect and characterize the main components of the volcanic plumes during explosive eruptions [13, 14, 15, 16]. Moreover, deep learning algorithms were applied to automatically recognize subtle to intense thermal anomalies exploiting the spatial relationships of the volcanic features [17]. The results from these studies show that the deep learning techniques, compared to traditional machine learning methods, achieve better accuracy in automatic analysis and recognition of the specific features in contexts in which spatial and temporal information aid characterizing satellite images. Different deep learning models have already been designed to automatically identify objects in different contexts [18, 19, 20, 21]. The most diffused DL models are the deep Convolutional Neural Networks (CNNs) [22, 23, 24], which have shown great promise in image classification. These data-driven methods are all based on hierarchical feature learning, which can easily be adapted to other specific applications without the need for manual feature extraction [25]. Examples of CNNs are LeNet, AlexNet, VGG16, GoogleNet, ResNet and SqueezeNet [26, 27, 28, 29, 30, 31]. Deep CNN models are usually trained over datasets containing millions of images across thousands of classes, such as ImageNet ([[http://www.image-net.org/](http://www.image-net.org/)]([http://www.image-net.org/](http://www.image-net.org/)); [32]). This dataset, in continuous development, is composed of around 14 million annotated images belonging to 1000 different classes.