Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO₂ gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.

The extraction and description of keypoints as salient image parts has a long tradition within processing and analysis of 2D images. Nowadays, 3D data gains more and more importance. This paper discusses the benefits and limitations of keypoints for the task of fusing multiple 3D point clouds. For this goal, several combinations of 3D keypoint detectors and descriptors are tested. The experiments are based on 3D scenes with varying properties, including 3D scanner data as well as Kinect point clouds. The obtained results indicate that the specific method to extract and describe keypoints in 3D data has to be carefully chosen. In many cases the accuracy suffers from a too strong reduction of the available points to keypoints.

With the increasing importance of monitoring urban areas, the question arises which sensors are best suited to solve the corresponding challenges. This letter proposes novel node tests within the random forest (RF) framework, which allows them to apply them to optical RGB images, hyperspectral images, and light detection and ranging (LiDAR) data, either individually or in combination. This does not only allow to derive accurate classification results for many relevant urban classes without preprocessing or feature extraction but also provides insights into which sensor offers the most meaningful data to solve the given classification task. The achieved results on a public benchmark data set are superior to results obtained by deep learning approaches despite being based on only a fraction of training samples.

Despite the growing interest among researchers, satellite-based prediction of soil salinity remains highly uncertain. The improvements in prediction accuracy reported in previous studies are usually limited to a single area. We performed a meta-analysis of regional satellite-based soil salinity predictions combined with in situ soil sampling and machine learning. Based on $R^{2}$ and root-mean-square error (RMSE) collected, we evaluated the effects of various features on the model accuracy and established a Bayesian network to evaluate the joint causal effect of multifeatures. Most significant differences were found in soil sampling schemes and characteristics of the study area, including the mean and variability (averaged $R^{2}$ of 0.75 for soil sample sets with lower salinity variation and 0.62 for others) of the salinity, climate type ( $R^{2}$ of 0.64 in arid areas and 0.74 in others), soil texture ( $R^{2}$ of 0.66 in sandy areas and 0.57 in others), and the interval between sampling date and satellite data acquisition date ( $R^{2}$ of 0.53 under the condition of over 15 days and 0.65 in others). Generally, using different satellite data has limited effects on model performance among which Sentinel-2 performed better ( $R^{2} $ = 0.72) than Landsat ( $R^{2} $ = 0.66). The sampling of subsamples for each sample should focus on their subpixel-scale spatial heterogeneity across satellite data rather than the number of subsamples. It is also necessary to select appropriate vegetation and salinity indices for different satellite data under different vegetation conditions. Among algorithms, random forests ( $R^{2} $ = 0.70) and support vector machines ( $R^{2} $ = 0.71) performed best.