Intervention areas

Static infrastructure monitoring is a necessity to prevent their degradation, to allow a rapid intervention in case of need, to anticipate as far as possible future events and to optimize the allocation of investments and resources.

The Smart Cities. must have the ability to monitor themselves and to offer citizens, companies and Administrations operating in the territory the tools to analyze, detect and intervene.

The main intervention areas concern:

• transport infrastructures;
• viaducts, under and overpasses, tunnels;
• buildings, monuments, historical goods;
• technical infrastructures, pylons, cabins.

There is a wide range of possible interventions, starting from an early assessment, up to targeted interventions “in loco” with the help of structuralist engineers and more or less sophisticated, expensive and invasive sensor networks.

Interferometric monitoring

The Umpi solution for early assessment is based on interferometric monitoring, a technique that uses satellite images, available starting from 1994 and analyzed with modern techniques of interferometry and image processing.

The SAR Interferometry technique can be used for monitoring both the ground surface deformations over a wide area (thousands of Km2) and the structures and infrastructures movements with sub-centimeter precision.

It is possible to analyze long series of historical data and to extract extremely accurate displacement measurements based on reference points called Persistent Scattered (PS), which correspond to objects on the ground that have a stable behavior over time (railway lines, roads, monuments, etc.).

The output consist of average displacement velocities maps and deformations time series.

Early Assessment advantages

The advantages of this solution are:

• Remote Sensing over large areas;
• Early Warning Monitoring;
• Back analysis on the study area movement;
• Monitoring of anthropic (structures and infrastructures) and natural elements (landslides, rock slopes, volcanoes, etc ...);
• Hydrogeological risk mitigation;
• Reduced costs and high accuracy;
• Possible application of machine learning techniques and predictive maintenance.