Conference Agenda

InSAR Norway (InSAR.no) is one of the world’s first fully operational, open-access national ground-motion services. Jointly operated by NGU, NVE and the Norwegian Space Agency, with processing by NORCE on NGU’s high-performance computing cluster, it provides nationwide deformation time-series from Copernicus Sentinel-1 data. The service delivers more than five billion measurement points annually through a public web portal and is widely used for landslide mapping, infrastructure monitoring and climate-related research. It has transformed how Norway identifies and manages unstable slopes, supports early warning and infrastructure safety, and integrates satellite data with in-situ monitoring through networks of snow-protected corner reflectors.

Experience from InSAR Norway directly informed the European Ground Motion Service (EGMS) under the Copernicus Land Monitoring Service, which scales the same operational principles to continental level. EGMS demonstrates that harmonized, validated and open InSAR products can be maintained across national borders.

Building on these achievements, this paper outlines the concept of a Global Ground Motion Service (GGMS)—a federated system providing standardized, GNSS-anchored ground-motion data worldwide. Such a service would combine open satellite data, interoperable processing frameworks and regional capacity-building to support disaster-risk reduction and resilience globally. As the global community invests in disaster-risk reduction, an open GGMS could become one of the most tangible and enduring legacies of the Copernicus era.

Satellite geodesy has become a cornerstone for mapping tectonic deformation, fault activity, and seismic hazard through measurements of surface velocities and strain rates. Yet, in vast regions of diffuse continental deformation, such as the Alpine–Himalayan Belt, observational coverage remains limited. Historically, large-scale studies have relied on sparse GNSS networks, which cannot resolve shorter-wavelength deformation features in many areas. To address this gap, we processed Sentinel-1 radar acquisitions from 2016 to 2024 to generate transnational surface velocity fields and time series at 1 km resolution, spanning more than 11,000 km from southern Europe to eastern China and covering over 20 million km². Our solution integrates more than 220,000 Sentinel 1 SAR images with a newly compiled GNSS dataset, all referenced consistently to the Eurasian frame. From these velocities, we compute horizontal strain rates by taking spatial gradients, providing near-continuous deformation maps across the planet’s largest actively deforming zone. Horizontal motions and strain patterns are primarily tectonic, exhibiting a dual character: strongly localised along major faults yet broadly distributed elsewhere. In contrast, short-wavelength vertical signals largely reflect non-tectonic processes, especially widespread groundwater depletion. These new velocity and strain-rate products constitute foundational datasets, offering a detailed view of continental deformation at a transcontinental scale that feed into the Disaster Risk Management cycle.

The west coast of Canada occupies an active subduction zone and is the host of an often underestimated threat of volcanic eruption. This tectonically active region is the home of 348 known volcanic vents that have been active since the Pleistocene, 54 of which are Holocene in age or younger. The annual probability of any eruption has been estimated at 1/200, while the annual probability of a major explosive eruption has been estimated at 1/3333.

In 2021 the Geological Survey of Canada published a volcanic threat ranking study) which used a threat score assignment methodology developed by the United States Geological Survey. In this study, we describe how the results of this threat ranking guide the acquisition strategy of routine RCM SAR data over the highest threat volcanoes in and around Canada.

We describe the architecture of the fully automated, cloud-based processing system that routinely searches for fresh RCM SAR data, ingests and processes the raw data and displays processed InSAR data on a purpose-built interface for scientific analysis.

With the proliferation of the heavily automated InSAR measurements, human analysis of vast volumes of data becomes challenging. In this research, we also describe the application and performance of an open weight deep learning model trained specifically for the purpose of detecting magmatic unrest in InSAR data. We demonstrate a deformation detection threshold of 6.2 cm and a true positive rate of 0.98 using observations from a real magmatic unrest event in Reykjanes, Iceland through 2023-2024.

We provide updates on the NASA-ISRO NISAR synthetic aperture radar mission and the NASA OPERA project. NISAR launched in June 2025 and began science operations in November 2025. The mission status will be presented and products for different science applications shown. The OPERA project produces four different product streams to support agency information needs, with the Dynamic Surface Water Extent (DSWx), Surface Disturbance (DIST), and Surface Displacement (DISP) products already available, and algorithm development underway for a future Vertical Land Motion product. These are generated from a variety of sensor data, including harmonized Landsat/Sentinel-2, Sentinel-1, NISAR, and SWOT. Examples shown will focus on the DISP products, currently generated from Sentinel-1 data and with a new product line using NISAR data to roll out in early 2027.

Monitoring precursory ground deformation is essential for assessing landslide hazard in regions where hydrological conditions strongly influence surface stability. In Québec’s Saguenay–Lac-Saint-Jean (SLSJ) region, numerous surface failures have occurred in highly sensitive postglacial marine clays, where rainfall, snowmelt, and groundwater fluctuations act as dominant triggers. Although Persistent Scatterer InSAR (PSInSAR) enables regional monitoring of slow ground deformation, its utility for short-term prediction remains limited by the temporal gap between Sentinel-1 acquisitions. This study investigates whether hydrological time-series, when integrated with PSInSAR displacement trends, can be used to forecast the line-of-sight (LOS) displacement observed at the satellite acquisition immediately preceding documented failure events.

A dataset of 102 historical failures (2018–2024) was assembled and paired with 168 Sentinel-1 ascending scenes processed through the StaMPS PSInSAR workflow. Daily precipitation, air temperature, groundwater level, and terrain slope were compiled and temporally synchronized with LOS displacement time series.

An XGBoost regression model was trained to predict the LOS displacement at the subsequent Sentinel-1 acquisition, using an 80/20 train–test split and five-fold cross-validation. Model performance was evaluated using Pearson’s r, MAE, and RMSE. Results show strong predictive skill, with r = 0.82, MAE = 4.36 mm, and RMSE = 6.26 mm. Feature importance analysis highlights the dominant role of recent PSInSAR displacement and groundwater variability. These findings demonstrate the feasibility of integrating hydrological and InSAR time-series to forecast pre-failure surface displacement, supporting the development of satellite-based early warning strategies for hydrologically sensitive terrain.

This research validates social media geospatial tags using Sentinel-1A Interferometric Synthetic Aperture Radar (InSAR) data processed on Google Earth Engine, focusing on Hurricane Harvey as a case study. The study addresses critical uncertainties regarding the spatial reliability of crowdsourced disaster information, which has limited integration of social media data into operational disaster management frameworks.

Methodology: The methodology integrated 144,546 geotagged posts from Twitter, Facebook, and Instagram collected during Hurricane Harvey (August 25 - September 3, 2017) with Sentinel-1A SAR imagery processed on the Google Earth Engine cloud platform. InSAR analysis identified 1,247 square kilometers of flooded areas in the Houston metropolitan region. Spatial validation employed buffer zone analysis at 500m, 1km, and 2km distances, with temporal alignment matching social media timestamps to SAR acquisition dates.

Results: Results demonstrate that 68.3% of flood-related social media tags fell within actively flooded areas using 1km buffers, with accuracy increasing to 82.1% within 500m buffers, compared to only 12.7% random expectation. Temporal analysis revealed social media activity peaked 6-18 hours before peak SAR-detected flooding, suggesting potential early warning capabilities. The cloud computing paradigm reduced processing time from weeks to 4-6 hours, enabling near-real-time validation.

Conclusion: This study establishes that validated social media geospatial information can effectively augment satellite-based disaster monitoring systems, particularly during initial response phases when temporal resolution is critical. The integration framework demonstrates operational feasibility for multi-source geospatial data fusion in disaster risk reduction applications.

The paper presents an overview of the European Ground Motion Service (EGMS), a CLMS product that delivers continent-wide, high-resolution measurements of ground motion to users based on Sentinel-1 data. It explains the EGMS architecture, which integrates Persistent and Distributed Scatterer techniques to generate standardised products—Basic, Calibrated, and Ortho—allowing millimetric monitoring of land motion across Europe. The paper emphasises how EGMS fills a critical gap between localised ground measurements and global geodetic frameworks, offering harmonised datasets for hazard assessment, infrastructure management, and policy-making. Applications discussed include subsidence and uplift detection, landslide mapping, and analysis of critical infrastructure.

Looking forward, the paper outlines a potential evolution towards an expansion of the EGMS concept beyond Europe. This would enable standardised, freely accessible deformation data to support global hazard mitigation and climate adaptation. The paper concludes that while technically feasible, a global implementation will require strategic GNSS densification and international cooperation to ensure reliability and equitable access.