Urban ground deformation is an often overlooked but critical issue in rapidly developing coastal cities, particularly in arid regions where groundwater extraction and land modification are common. While time-series InSAR techniques are widely used in research, their integration into practical geospatial workflows for city-level applications remains limited.
This presentation demonstrates how time-series InSAR results can be effectively integrated into the QGIS environment to support ground deformation monitoring in Dammam, Saudi Arabia. Using Sentinel-1 data from 2017 to 2025, more than 1,500 interferograms were processed through an open-source workflow combining ASF HyP3, ISCE2, and MintPy. The analysis reveals localized subsidence with rates reaching up to −1.7 cm/year, forming a spatially coherent deformation pattern.
The focus of this work is on transforming these advanced remote sensing outputs into actionable geospatial information within QGIS. InSAR-derived products, including cumulative displacement maps, velocity layers, and time-series point data, are imported and managed in QGIS to enable visualization, spatial analysis, and integration with urban datasets. This allows users to identify deformation-prone zones and relate them to infrastructure, land use, and drainage systems.
A validation step using historical optical imagery shows that detected deformation corresponds to real surface changes, including infilled depressions and post-construction consolidation. This highlights the capability of QGIS to serve as a central platform for combining multi-source geospatial data into a coherent monitoring framework.
The presented workflow demonstrates how open-source tools can bridge the gap between advanced satellite analysis and practical urban management. It provides a scalable and cost-effective approach for municipalities and organizations seeking to incorporate ground deformation monitoring into their geospatial decision-making processes using QGIS.

Urban environmental challenges such as illegal dumping, land degradation, and ground instability increasingly require integrated, data-driven responses that go beyond isolated geospatial analysis. While a wide range of tools exists for satellite processing, spatial modelling, and field data collection, these systems often operate independently, limiting their effectiveness in real-world operational environments. This workshop introduces a practical framework for building an open System of Systems (SoS) using QGIS as a central integration platform for urban environmental monitoring and decision support.

The workshop positions QGIS not merely as a desktop GIS tool, but as an orchestration layer capable of connecting heterogeneous subsystems, including satellite-based Earth observation (e.g., Sentinel-1 InSAR products), AI-assisted detection workflows, multi-criteria decision analysis (MCDA), and field-based data collection. Participants will learn how to integrate these components into a coherent workflow that transforms raw geospatial data into actionable intelligence.

Through guided, hands-on sessions, participants will work with real-world datasets to (1) import and visualize time-series InSAR outputs, (2) integrate multiple spatial indicators relevant to urban environmental conditions, (3) implement MCDA techniques within QGIS for spatial prioritization, and (4) conceptualize how these analytical components can be embedded within a broader System of Systems architecture supporting operational decision-making.

The workshop emphasizes open-source tools, reproducible workflows, and interoperability, aligning with current discussions on GIS sovereignty and sustainable digital infrastructure. By the end of the session, participants will gain both technical skills and conceptual understanding of how QGIS can be leveraged as a strategic platform for integrating data, analytics, and operations in complex urban systems.

This workshop is suitable for GIS practitioners, urban planners, researchers, and decision-makers interested in advancing from standalone spatial analysis toward integrated, system-level geospatial solutions.