Conference Agenda

We present a residual-aware, multi-sensor 3-D coseismic displacement decomposition applied to the 2025 Mw 7.7 Myanmar earthquake. The workflow combines multi-track Sentinel-1 SAR pixel offsets (range and azimuth) with Sentinel-2 optical pixel offsets, using only the north–south component where the signal clearly exceeds the optical noise level.

The key innovation is to handle sensor- and mosaicking-related residuals within a robust inversion framework rather than as ad hoc preprocessing. Strip-wise and inter-track trends are removed by MAD–Tukey IRLS plane fitting that suppresses long-wavelength orbital and viewing-geometry errors while preserving sharp near-fault steps in overlap zones. A residual-aware weighted least-squares inversion is then performed per pixel to recover east–west, north–south and vertical displacements and their fault-parallel projection. The resulting fields provide spatially continuous, cross-sensor-consistent constraints on fault-parallel slip along this exceptionally long rupture.

In Kolkata, potential land subsidence occurred primarily due to excessive groundwater extraction, which has been one of the major environmental crises, along with rapid urbanization and soft soil characteristics. This study investigates Kolkata's land surface deformation patterns from 2017 to 2023 using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology. The study comprehensively analyzes deformation scenarios from 2017 to 2022; additionally, a detailed examination of the 2023 deformation scenario reveals continued trends and localized changes in subsidence patterns. The result shows that the mean ground velocity between 2017 and 2022 varies between -2.8 and -5.5 mm/year, and the area under the subsidence zone shows an increasing trend. Predictive models for 2024 and 2025 are developed based on historical data, providing forecasts of future subsidence trends. The prediction indicates that in 2024, the area under the high deformation class will be relatively higher compared with 2025. Spatial association analyses explore correlations between subsidence patterns of different years in Kolkata. The findings of this study may facilitate the assessment of the possible effects of ground-level movement on resource management, safety, and economics in this densely populated city.

Rapid and accurate detection of landslides after extreme climate events, such as heavy rainfalls or hurricanes, is essential for hazard response and mitigation. Traditional mapping methods rely on manual interpretation or labelled datasets, limiting scalability. This paper presents an integrated workflow combining unsupervised autoencoder-based + KMeans change detection and deep learning semantic segmentation to improve landslide identification in Western North Carolina following Hurricane Helene (September 2024). The approach leverages Planetscope RGB-NIR imagery at 3 m spatial resolution and North Carolina Department of Environmental Quality post-event landslide inventory points. The unsupervised autoencoder extracts latent features and highlights change zones, while segmentation models such as UNet learn spatial–contextual patterns from semi-automated labels. Results demonstrate high detection accuracy with segmentation models achieving strong overlap with ground-truth inventories and minimal false positives with an F1-score of 92%. This hybrid pipeline bridges rapid unsupervised detection and precise pixel-level segmentation, enabling scalable, near-real-time landslide mapping.

Rapid and reliable landslide mapping is critical for post-disaster response, yet Synthetic Aperture Radar (SAR)-based detection remains challenging due to speckle noise, geometric distortions, and complex terrain. This study develops an operational post-event landslide extraction framework using a UNet segmentation architecture with multimodal geospatial data fusion. High-resolution COSMO- SkyMed SAR imagery is combined with terrain representations derived from Digital Elevation Models (DEM), Red Relief Image Maps (RRIM), and rainfall indices to evaluate the contribution of complementary geospatial information to segmentation performance.

Experiments were conducted across three major landslide-triggering events in Japan (Kyushu, Hokkaido, and Kumamoto), comparing SAR-only and multimodal configurations. Results demonstrate that integrating terrain information and rainfall data improves landslide detection performance compared with SAR-only inputs. RRIM consistently outperforms DEM as a topographic descriptor, particularly in steep or heterogeneous terrain, while rainfall information provides moderate gains in recall. Boundary-based metrics further indicate improved geometric fidelity of mapped landslides when multimodal inputs are incorporated. The framework requires only a single post-event SAR acquisition supplemented with publicly available ancillary datasets, enabling rapid and scalable generation of landslide inventories without reliance on pre-disaster imagery. These findings establish a reproducible baseline for SAR-driven landslide segmentation and highlight the potential of multimodal geospatial data fusion for operational disaster response and hazard monitoring.

In this study, we integrated information from the French avalanche database, high-resolution digital elevation models (DEMs), and Sentinel-1 SAR images to model avalanche extents for events occurring across three distinct time periods in three French massifs. The modelled avalanche extents were compared with manually delineated polygons mapped over SAR RGB composites generated using the principle applied in colour-based change detection algorithms. The comparison revealed a strong correspondence between the two independent approaches, with IoU values ranging from 0.42 to 0.47 and F1 scores between 0.58 and 0.63 across the different massifs. We further analyzed the distribution of SAR backscatter values in pre- and post-event images across different zones of the avalanche paths. The results indicated that a fixed 3 dB threshold would most likely be insufficient to capture the complete avalanche extent, as certain zones exhibited backscatter increases of less than 3 dB in post-event SAR imagery. As a result, a multi-threshold approach based on different avalanche zones is recommended. Finally, we assessed the potential of Sentinel-2 optical imagery to detect surface changes and characterize the physical behaviour of avalanche-affected paths following intense avalanche

events. However, the results were inconsistent, exhibiting the expected trends in one study area but nearly opposite patterns in the other, indicating that the integration of optical data for automated avalanche mapping may not always be reliable.