As a geologist and a PhD student of the National PhD in Earth Observation at Sapienza University of Rome (Italy), I am very enthusiastic about the opportunity to present my work and to participate in the 36th Annual GRSG Conference. If accepted, my presentation will focus on the incorporation of advanced satellite InSAR analysis into a multi-source geospatial platform, designed to support the monitoring of urban environments and to contribute to the transition towards smart cities. This work is being carried out within the framework of my PhD research, in collaboration with the technological company TITAN4 (https://www.titan4.it/).

The paradigm of modern cities is evolving into the concept of Smart Cities, where diverse technological approaches are adopted to enhance sustainability, improve citizens’ quality of life, and address geohazards (Zambom Santana et al 2017). Within this framework, Earth Observation (EO) products represent powerful tools to support decision-making, by providing timely and spatially consistent information on different natural processes and their impact in urban dynamics.

Within urban environments, land subsidence is a well-recognized geohazard that can severely damage infrastructure, generate substantial economic losses, and even threaten cultural heritage in certain locations. In this context, satellite InSAR has proven to be a highly effective technique for mapping ground deformation with millimeter-level precision, enabling detailed assessments of its impact on urban infrastructure (Gao et al 2022). While conventional applications of the technique often focus on producing velocity maps of surface deformation, there is considerable potential in moving further and distinguishing the multiple deformation behaviors that may coexist in the same urban setting.

By applying statistical tools such as Independent Component Analysis (ICA), it becomes possible to separate the signals embedded in InSAR time series. This allows the identification of areas where subsidence is accelerating, decelerating, or exhibiting seasonal behavior. Such decomposition of deformation signals provides a deeper understanding of subsidence dynamics and offers valuable insights for both mitigation measures and urban planning strategies.

Therefore, the main objective of this work is to extract the different components of ground motion present in satellite InSAR deformation time-series through ICA statistical analysis, and to integrate these outcomes into a multi-source geospatial platform aimed at supporting the development of smart cities. The methodology will be applied to the city of Parma, Italy, where TITAN4 company is currently developing integrated geospatial solutions.

For this study, Cosmo-SkyMed SAR data between January 2022 and June 2025 is being processed by using the SBAS algorithm (Berardino et al 2002), in order to obtain the mean deformation velocity and the displacement time-series. Afterwards, these series will be analyzed by using the FastICA algorithm originally developed by Hyvärinen & Oja (1997), and implemented in MATLAB. Finally, the results will be integrated with other geospatial and EO derived data on TITAN4’s platform.

As results of the analysis, two key outcomes are expected:

The retrieval of the different deformation patterns for the city of Parma in the last three years, identifying areas where an acceleration/deceleration of the process has occurred and providing a more detailed understanding of this geohazard dynamics.

The integration for the first time of this statistical algorithm into a geospatial multisource platform, revealing how remote sensing and post processing statistical strategies, combined with other geospatial information (i.e. cadastral maps, infrastructure networks and geohazard layers) can generate accessible and actionable information for municipalities managers. This integrated framework will support local authorities in identifying vulnerable areas, prioritizing interventions, and designing evidence-based mitigation strategies.

To sum up, this work foresees to demonstrate the benefits of performing an additional statistical analysis to the satellite InSAR processing and of integrating the results into a multi-source geospatial platform for smart cities monitoring. Next steps include the development of EO strategies to evaluate other geohazards, such as flooding risk, by using multi-satellite approaches (i.e. SAR and multi-spectral satellite information).