## 6 Conclusions  
We present an operational volcano monitoring system, based on the automated download and processing of multisensor satellite-based data (Sentinel-1 SAR, Sentinel-2 SWIR, Sentinel-5P TROPOMI). The recovered data aim at providing key parameters able to inform on the state of volcanic activity, namely: surface deformation, surface reflectivity changes, surface heat anomalies, and SO\({}_{2}\) gas emissions. The results are disseminated in NRT on a public website (www.mounts-project.com), where both geocoded images and multi-parametric time series help understand the activity. Moreover,we demonstrate how artificial intelligence can be used in such monitoring system to solve complex tasks. In particular, we designed and trained a convolutional neural network to detect large deformation signals in wrapped interferograms with no atmospheric corrections. The training was done on synthetically generated interferograms, and evaluated on \(>\)1360 real interferograms produced by MOUNTS. Due to the very good performances of the network, it is now incorporated into the operational processing chain, which delivers automatic email alerts to dedicated users when strong deformation is recorded at the monitored volcanoes. In addition to the set of parameters recovered from spaceborne sensors, we incorporate information available from global earthquake catalogues (GEOFON and USGS) to inform on the seismicity located in the vicinity of the volcano. The utility of integrating both satellite-based parameters (deformation, heat and gas) and ground-based parameters (seismicity) are demonstrated through a number of recent eruptions: Erta Ale 2017, Piton de la Fournaise 2018-2019, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, and Ambrym 2018. We show how this interdisciplinary approach allows for assessment of a variety of volcanic phenomena, ranging from subsurface magma migration, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and SO\({}_{2}\) gas emission into the atmosphere. The data processed by MOUNTS is providing insights into the eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of such multiparametric datasets can help better monitor volcanic hazards. The following are available online at [[http://www.mdpi.com/2072-4292/11/13/1528/s1](http://www.mdpi.com/2072-4292/11/13/1528/s1)]([http://www.mdpi.com/2072-4292/11/13/1528/s1](http://www.mdpi.com/2072-4292/11/13/1528/s1)). S1: Details on DInSAR, SAR, and SWIR processing chains; S2: Details on neural network architecture, training, and synthetic data generation; S3: Video of the summit crater morphological changes detected from SAR imagery during the 2018 eruptive episodes at Kilauea (Hawai'i); S4: Video of the island growth and destruction before/after the December 2018 eruption of Krakatau (Indonesia); S5: Kilauea (Hawai'i) East Rift Zone flank eruption monitored by MOUNTS on large spatial scale. Conceptualization: (S.V., T.R.W., A.L. ), inspired by MIROVA (D.C., M.L. ); Methodology: system architecture and management (S.V., A.L. ), website development (S.V. ), Convolutional Neural Network (A.L. ), SWIR hot pixel detection (F.M. ), SAR speckle filtering and change detection, (O.D. ), SO\({}_{2}\) emission analysis (S.V., M.L. ); Writing: original draft preparation (S.V. ), review and editing by (A.L., D.C., D.L., F.M., O.D., M.L., T.R.W., O.H.). This publication was financially supported by Geo.X, the Research Network for Geosciences in Berlin and Potsdam (Project Number: SO_087_GeoX). We thank four anonymous reviewers for their comments which helped improve the manuscript.