Subtle volcanic deformations point to volcanic activities, and monitoring them helps predict eruptions. Today, it is possible to remotely detect volcanic deformation in mm/year scale thanks to advances in interferometric synthetic aperture radar (InSAR). This article proposes a framework based on a deep learning model to automatically discriminate subtle volcanic deformations from other deformation types in five-year-long InSAR stacks. Models are trained on a synthetic training set. To better understand and improve the models, explainable artificial intelligence (AI) analyses are performed. In initial models, Gradient-weighted Class Activation Mapping (Grad-CAM) linked new-found patterns of slope processes and salt lake deformations to false-positive detections. The models are then improved by fine-tuning (FT) with a hybrid synthetic-real data, and additional performance is extracted by low-pass spatial filtering (LSF) of the real test set. The t-distributed stochastic neighbor embedding (t-SNE) latent feature visualization confirmed the similarity and shortcomings of the FT set, highlighting the problem of elevation components in residual tropospheric noise. After fine-tuning, all the volcanic deformations are detected, including the smallest one, Lazufre, deforming 5 mm/year. The first time confirmed deformation of Cerro El Condor is observed, deforming 9.9–17.5 mm/year. Finally, sensitivity analysis uncovered the model’s minimal detectable deformation of 2 mm/year.

Change detection is a process of detecting differences with the objects or phenomena which are observed in the different time intervals. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1985 - 2013). The area observed in this study is the region of mountain Zlatibor (Serbia) with its surroundings. The methods represented in this study are vegetation indices differencing, Supervised classification and Object based classification. These methods gave different results in term of land cover area, and it is generally concluded that supervised classification gave the most accurate results with the images of medium spatial resolution. The results of this study can be used for urban and environmental planning. All information lead to conclusion that the surface under the forests is reduced for about 4% (or about 1000 ha) while the built up area has doubled (grown about 600 ha) during the examined period. The results also highlights the importance of change detection techniques in land cover for the areas that are developing rapidly, such as Zlatibor study area.

Monitoring volcanic deformations allows us to track dynamic states of a volcano and to know where an eruptions could happen. Spaceborne Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) techniques created an opportunity to track volcanoes globally, even in inaccessible regions without ground measuring stations.This paper proposes a convolutional neural network (CNN) for detection of volcanic deformations in InSAR velocity maps. We had only a small amount of velocity maps over the region of central South American Andes, therefore the synthetic data are used to train the model from scratch. In the region of interest, the velocity maps contain the patterns of salt lakes and slope induced signal which confuse CNN models trained on synthetic data.In order to bridge the gap between the synthetic and real data, the hybrid synthetic-real data set is used for fine-tuning the model. The hybrid set consists of the real background signal data and synthetic volcanic data. Four fine-tuning sets which were created by different combinations of the original hybrid data, the filtered hybrid data, and simulated data have been used and compared with each other. Besides, we compared four fine-tuning approaches to determine where and how to fine-tune the model. Results show significant improvement in performance by majority of the approaches, and training the last or last two layers have given the best results. In addition, using the FT1 (containing only hybrid set), and FT4 (containing all sets) improved the area under the curve receiver operating characteristic (AUC ROC) from 55% to 86% and 88% respectively.

TecVolSA (Tectonics and Volcanoes in South America) is a project with a goal of developing intelligent Earth Observation (EO) data processing and exploitation for monitoring various geophysical processes in central south American Andes. Large amount of Sentinel-1 data over the period of about 5 years has been processed using mixed Permanent Scatterer and Distributed Scatterer (PS/DS) approaches. The received products are velocity maps with InSAR relative error in the order of 1 mm/yr on a large scale (>100km). The second milestone of the project was automatic extraction of information from the data. In this work, the focus is on detecting volcanic deformations. Since the real data prepared in such manner is limited, to train a deep learning model for detection of volcanic deformations, a synthetic training set is used. Models are trained from scratch and InceptionResNet v2 was selected for further experiments as it was found to give best performance among the tested models. The explainable AI (XAI) techniques were used to understand and analyze the confidence of the model and to understand how to improve it. The models trained on synthetic training set underperformed on real test set. Using GradCAM technique, it was identified that slope induced signal and salt lake deformations were mistakenly identified as volcanic deformations. These patterns are difficult to simulate and were not contained in synthetic training set. Bridging this distribution gap was performed using hybrid synthetic-real fine-tuning set, consisting of the real slope induced signal data and synthetic volcanic data. Additionally, false positive rate of the model is reduced using low-pass spatial filtering of the real test set, and finally by adjustments of the temporal baseline received from a sensitivity analysis. The model successfully detected all 10 deforming volcanoes in the region, ranging from 0.4 - 1.8 cm/yr in deformation.

Volcanic eruptions are large-scale rare events causing extensive economic damage and loss of life each year. About 1500 volcanoes are considered active, and 800 million people live less than 100km away from them. Therefore, forecasting volcanic activity and eruptions are of great significance.The most precise way to monitor volcanoes and volcanic deformation is onsite monitoring; however, many active volcanoes are inaccessible. In the past years, Interferometric Synthetic Aperture Radar (InSAR) technology has been utilized with the help of deep learning (DL) to detect fast, intense volcanic deformations automatically. Previously, we employed the InSAR data with state-of-the-art processing to achieve high deformation accuracy over longer periods and apply DL to detect subtle long-term volcanic deformations automatically. Like the mentioned approaches, we face the challenges of small training sets and the gap between the train and test set.The DL model is trained on synthetic data and makes many false positive detections on the real test set. Grad-CAM analysis uncovered that the false detections are activated by the region-specific patterns of salt lake deformations, slope processes, and residual tropospheric noise. To increase the diversity of synthetic samples and reduce the false positives, we apply generative adversarial networks (GANs), to transfer the style of realistic terrain to synthetic data.This approach allows the generation of an infinite amount of synthetic data containing the regional deformation patterns and can be replicated for other regions. Since we are using real and synthetic data, it is significant that model can be trained with unpaired images. We employ a multi-domain and bidirectional state-of-the-art image-to-image translation model, StarGAN v2. We test the model on different tasks. The first task is to learn the transformation from synthetic background data to real background data. For volcanic deformations, we rely on established models for volcanic deformation simulations, like Mogi, Okada, or volumetric models. The second task demands the model to translate between synthetic and real and volcanic and non-volcanic domains. This model is capable of directly generating realistic-looking samples with volcanic deformations but with less control than the previous approach.

Globally, there are about 1400 active volcanoes, and each year, 20 to 50 volcanic eruptions occur, many of which lack on-site monitoring. Open-source InSAR technology, like Sentinel-1, allows tracking volcanic deformations globally, even in remote or hard-to-access locations. By utilizing persistent and distributed scatterer interferometry (PSI/DSI), InSAR data can reveal subtle, millimeter-scale deformations, enabling granular tracking of volcanic activity. Furthermore, deep learning (DL) models can automatically identify and flag these changes as an alert or for further analysis.This experiment utilizes a classification deep learning architecture, InceptionResNet v2, to detect volcanic deformations in InSAR data. The used dataset consists of 5-year-long deformation maps covering the Central Volcanic Zone in the South American Andes and reserves the known volcanic regions for testing. The remaining data and synthetic volcanic deformations are used to train the model.GradCAM, the explainability tool, shows that accurate identification and differentiation of deformation signals are difficult on the model due to the subtle volcanic deformations observed in InSAR data. To address this, we apply wavelet transformations and filtering techniques to enhance the data, thereby improving the performance of the deep learning model.Applying Daubechies 2 wavelet transform emphasizes subtle large-area, mostly volcanic, signals while removing the milder high-frequency patterns. The DL models are trained, and each is tested on the data with up to four wavelet transforms. The model trained and tested on original data achieves a 64.02% AUC ROC average, while when tested on data two times transformed by wavelet transform, it improves to 84.14% AUC ROC average.We show that Daubechies 2 wavelet transform cleans data while amplifying the volcanic deformation. A side effect is that it enlarges the small area deformations, significant in intensity. This issue can be solved by filtering the data in preprocessing. Utilizing this method, models can detect even the smallest deformations of 5 mm/year.