Conference Agenda

Aging bridge infrastructure requires efficient, network-scale monitoring, especially in remote areas where traditional in-situ sensors are costly and logistically challenging. This paper presents a remote sensing framework for structural health monitoring based on spaceborne Synthetic Aperture Radar (SAR). The approach combines Persistent Scatterer Interferometry (PSI) and Least Squares Collocation (LSC), implemented through the PHASE open-source MATLAB software, to derive a millimeter-level spatio-temporal displacement model.

The methodology is applied to a reinforced-concrete viaduct in the Alpine foothills of Lombardy, Italy, using five years of Copernicus Sentinel-1 data. A custom elevation-based spatial filtering strategy enables the isolation of structural displacements from the surrounding topography. The resulting spatio-temporal displacement model captures the expected seasonal thermal behavior of the structure and highlights localized deviations from the dominant cyclic response.

Finally, the SAR-derived model is integrated with UAV photogrammetry and official inspection reports within the P.O.N.T.I. 3D viewer. This multi-source, Digital Twin-like environment facilitates the joint interpretation of remote sensing observations and in-situ evidence, providing a scalable framework to support infrastructure monitoring and management.

Large-scale land surface deformation monitoring using Interferometric Synthetic Aperture Radar (InSAR) requires robust detection of changes in long-term deformation trends. However, accurate change point (CP) detection remains challenging due to complex time series characteristics, including seasonal and quasi-periodic components and noise. Classical methods and many existing deep learning approaches rely on restrictive assumptions and training data that do not fully represent real-world InSAR time series, limiting their generalization and scalability in large-scale, real-world applications. In this study, we propose an integrated, fully supervised framework for CP detection in InSAR displacement time series based on Temporal Convolutional Networks (TCNs). The proposed TCN model employs dilated convolutions with multi-scale receptive fields to capture long-term temporal dependencies and complex deformation patterns, enabling robust identification of significant trend changes under noisy conditions. To effectively train this model, we introduce a deep learning-based InSAR time series simulation framework trained on real time series. This simulation framework produces physically consistent InSAR time series that retain essential temporal characteristics while introducing predefined, credible trend changes. Finally, we integrate the trained model into a large-scale anomalous change-detection pipeline that aggregates detected CPs from individual time series into spatially coherent deformation heatmaps suitable for operational monitoring. The proposed framework is evaluated using simulated data and real InSAR time series from the Phlegraean Fields caldera (Campi Flegrei), Italy. The results show clusters of anomalous behavior in the central Campi Flegrei–Pozzuoli area and in parts of Ischia and Procida, consistent with known unrest zones, associated periods, and independent measurements.

Electro-optical (EO) imagery is intuitive but highly dependent on weather and illumination, whereas synthetic aperture radar (SAR) imagery provides reliable all-weather observations yet offers limited spectral information. To complement these modalities, recent studies have applied cGAN-based image-to-image translation for SAR-to-EO translation. However, side-looking SAR introduces spatial distortions such as foreshortening and layover that cause relative misalignment with EO imagery, undermining pixelwise supervision and yielding structural discrepancies between translated outputs and reference EO imagery. In this study, we propose Geometry-Conditioned Pix2Pix (GC-Pix2Pix), which explicitly conditions on projected local incidence angle (PLIA) information derived from SAR imagery to better preserve structure and alignment in translated EO imagery. The method is based on Pix2Pix and comprises a 2-branch generator and a PatchGAN discriminator. The generator consists of a main network that processes SAR polarimetric channels (VV, VH) and a conditioning subnetwork that extracts PLIA features. The subnetwork uses multi-layer convolutional blocks to capture local PLIA patterns, and the extracted features are then fused with features from the main branch and emphasized via a spatial attention module. For training and evaluation, we assembled a dataset over South Korea that combines Sentinel-1A GRD VV/VH with PLIA and Sentinel-2B Level-2A RGB imagery. We compared GC-Pix2Pix against representative baselines. Across multiple image quality assessment metrics and complementary qualitative analyses, the proposed approach consistently improved SAR-to-EO translation performance.

Cloud cover in multispectral imagery (MSI) significantly hinders early-season crop mapping by corrupting spectral information. Existing Vision Transformer(ViT)-based time-series reconstruction methods, like SMTS-ViT, often employ coarse temporal embeddings that aggregate entire sequences, causing substantial information loss and reducing reconstruction accuracy. To address these limitations, a Video Vision Transformer (ViViT)-based framework with temporal-spatial fusion embedding for MSI reconstruction in cloud-covered regions is proposed in this study. Non-overlapping tubelets are extracted via 3D convolution with constrained temporal span t=2, ensuring local temporal coherence while reducing cross-day information degradation. Both MSI-only and SAR-MSI fusion scenarios are considered during the experiments. Comprehensive experiments on 2020 Traill County data demonstrate notable performance improvements: MTS-ViViT achieves a 2.23% reduction in MSE compared to the MTS-ViT baseline, while SMTS-ViViT achieves a 10.33% improvement with SAR integration over the SMTS-ViT baseline. The proposed framework effectively enhances spectral reconstruction quality for robust agricultural monitoring.

This paper focuses on cross-modal image matching between Synthetic Aperture Radar (SAR) and optical imagery, a long-standing challenge due to disparate sensing physics, radiometric behaviour and geometric distortions. Beyond applicational needs in satellite data fusion and downstream mapping, the study is additionally motivated by the rapid advances of feature matching in the field of Computer Vision. Under a unified, lightweight pipeline, the authors evaluate a classical handcrafted baseline (SIFT) against modern deep matchers, including a modality-invariant approach (MINIMA), as well as a SuperPoint+LightGlue pipeline, using the SpaceNet9 dataset with provided ground truth. The aim is to assess each models' ability to establish reliable correspondences without retraining or modality-specific adaptation, aiming to provide practical guidance for other researchers working with SAR-optical fusion. The paper highlights where pretrained multimodal models already yield consistent correspondences, where they still struggle and outlines possible next steps.

Marine pollution, including Marine Debris and Oil Spills, poses a serious environmental threat that requires systematic monitoring. While satellite observations and machine learning models have been widely applied in this domain, the use of advanced deep learning techniques remains limited. To support progress in this area, we construct a new annotated Sentinel-1 SAR dataset derived from the MADOS Sentinel-2 marine pollution dataset, including labels for oil spills, sea surface, look-alikes, ships, and offshore platforms. We evaluate several deep learning architectures on this dataset, including traditional models such as U-Net, state-of-the-art segmentation models such as SegNeXt and domain-specific frameworks such as MariNeXt. Our results show that MariNeXt achieves the best performance with an F₁-macro score of 92.7%, significantly outperforming U-Net and SegNeXt. Qualitative analysis using paired Sentinel-2 imagery further validates these findings. The study also highlights the persistent difficulty of detecting marine debris in SAR imagery, particularly when complementary optical data are unavailable.

This paper introduces a coarse-to-fine cross-view localization framework that unifies image-level retrieval and geometry-level alignment within a single pipeline. The proposed approach first employs a Bird’s-Eye-View (BEV)-guided retrieval module to establish a perspective-consistent intermediary space, enhancing cross-view consistency and retrieval precision. In the fine stage, a geometry-aware alignment module estimates the 3-DoF pose through interpretable point-plane matching based on BEV correspondences. This hierarchical design bridges global retrieval and local geometric reasoning, achieving both scalability and high localization accuracy. Extensive experiments on the VIGOR benchmark demonstrate that the proposed framework achieves state-of-the-art performance in both retrieval and alignment, significantly improving end-to-end localization precision while maintaining computational efficiency.