## 1 Introduction
Furthermore, they are \"big\" in the space-domain on the basis of the total number of persistent or coherent targets themselves that the interferogram network created during InSAR processing has allowed to identify over the long time span investigated (despite the relatively small region of interest of this work). 3. Third, to prove that such big InSAR datasets can be handled through post-processing and data integration methodologies that have been established by the geohazard community in the last decade, and be transformed into higher-level geospatial information to characterize geological hazards of potential concern of local stakeholders. Without claiming to fully develop specific user-oriented or user-driven applications, this paper does not restrict to the presentation of InSAR results only (which is the dominant feature of most of the published literature on InSAR processing using cloud/grid computing), but also encompasses selected use-cases of geological interpretation. To address the first two aims, in this study, six full years of Sentinel-1 SAR ascending and descending mode imagery compose the big data volume used as input. Multi-temporal InSAR processing was carried out using the parallelized implementation of the SBAS differential InSAR approach, also referred to as Parallel-SBAS (P-SBAS) [19], which was developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council (CNR) of Italy and integrated in the European Space Agency (ESA)'s Geohazards Exploitation Platform (GEP). The latter is a cloud-based environment sourced with data (both optical and SAR), tools, and processing services, aimed to support satellite Earth observation (EO) exploitation for geohazards [20]. Developed as an ESA Thematic Exploitation Platform (TEP) in 2015, and conducted by a consortium led by Terradue SrI, the GEP follows the SuperSites Exploitation Platform (SSEP), which was originally initiated in the context of the Geohazard Supersites and Natural Laboratories initiative. Over the years, the platform has expanded to address broader objectives of the geohazards community, including several activities and projects of the Committee on Earth Observation Satellites (CEOS) Working Group on Disasters [21]. The GEP is now used by a large community of researchers and stakeholders working in the field of EO, with a focus on mapping hazard-prone areas and monitoring terrain deformation. Information on how to access the GEP and exploit its on-demand and systematic services is available on the dedicated website ([[https://geohazards-tep.eu](https://geohazards-tep.eu)]([https://geohazards-tep.eu](https://geohazards-tep.eu))), along with a description of all thematic apps, services, datasets, and other resources. To the best of our knowledge, the present big data processing experiment with P-SBAS is the one exploiting the longest-ever Sentinel-1 SAR interferometric stacks used so far for a single study site, not only among the studies employing the P-SBAS method, but also globally, as the longest Sentinel-1 stacks published in the literature consisted of no more than 140-190 acquisitions each (e.g., [22; 23; 24]) or up to 240-260 (e.g., [25]). Therefore, in terms of the size of the input SAR data stack processed, this study aspires to act as a pioneering proof-of-concept of big SAR data analysis with multi-interferogram algorithms, and in particular the P-SBAS implementation. Compared to the more paroxystic and catastrophic hazards that are commonly observed with the GEP (e.g., floods and tectonic deformation), this study focuses on more \"silent\" and subtle hazards, i.e., slow-moving landslides and coastal subsidence, whose regular monitoring and early-stage characterization could provide evidence-based input into land management, land-use planning, and environmental assessments.