Rheticus: a cloud-based Geo-Information Service for the Detection and Monitoring of Geohazards and Infrastructural Instabilities

Geospatial information is today essential for organizations and professionals working in several industries. More and more, huge information is collected from multiple data sources and is freely available to anyone as open data. Rheticus® is an innovative cloud-based data and services hub able to deliver Earth Observation added-value products through automatic complex processes and, if appropriate, a minimum interaction with human operators. This target is achieved by means of programmable components working as different software layers in a modern enterprise system which relies on SOA (Service-Oriented-Architecture) model. Due to its spread architecture, where every functionality is defined and encapsulated in a standalone component, Rheticus is potentially highly scalable and distributable allowing different configurations depending on the user needs. This approach makes the system very flexible with respect to the services implementation, ensuring the ability to rethink and redesign the whole process with little effort. In this work, we outline the overall cloud-based platform and focus on the ""Rheticus Displacement"" service, aimed at providing accurate information to monitor movements occurring across landslide features or structural instabilities that could affect buildings or infrastructures. Using Sentinel-1 (S1) open data images and Multi-Temporal SAR Interferometry techniques (MTInSAR), the service is complementary to traditional survey methods, providing a long-term solution to slope instability monitoring. Rheticus automatically browses and accesses (on a weekly basis) the products of the rolling archive of ESA S1 Scientific Data Hub. S1 data are then processed by SPINUA (Stable Point Interferometry even in Unurbanized Areas), a robust MTInSAR algorithm, which is responsible of producing displacement maps immediately usable to measure movements of point and distributed scatterers, with sub-centimetric precision. We outline the automatic generation process of displacement maps and we provide examples of the detection and monitoring of geohazard and infrastructure instabilities.