Conference Agenda

This paper addresses the heavy reliance on manual operations in control point acquisition for UAV photogrammetry and proposes an encoded control point deployment and recognition method based on a Light-Marker UAV (LMUAV). Conventional approaches rely on manual placement of control points and manual identification and measurement in images for aerial triangulation, resulting in low efficiency. To address this limitation, an LMUAV equipped with an LED array actively broadcasts its positional information as quaternary optical signals. The observing UAV performs coarse localization of the target region by integrating communication priors with the imaging model, followed by light spot segmentation and graph construction within the region of interest (ROI). Node correspondences are then recovered by constructing a template graph and an observation graph and applying Reweighted Random Walks (RRWM) graph matching. The matching robustness is further enhanced by incorporating directional point constraints and RANSAC-based geometric filtering. Based on the recovered correspondences, the encoded information is decoded through color recognition and validation, enabling automatic control point recovery. Experimental results in a cross-flight-line scenario with a single target UAV demonstrate that the proposed method achieves stable node matching and encoding–decoding, with a sequence-level accuracy of 76.32%, and a final effective decoding rate of 71.05%, while maintaining centimeter-level positioning accuracy, thereby validating its effectiveness for automatic control point acquisition in UAV mapping.

Automated and selective harvesting of fruits is increasingly vital due to high costs and seasonal labor shortages in advanced economies. This paper explores 6D pose estimation of strawberries using synthetic data generated through a procedural pipeline for photorealistic rendering. We utilize the YOLOX-6D-Pose algorithm, a single-shot method leveraging the YOLOX backbone, known for its balance of speed and accuracy and its suitability for edge inference. To counter the lack of training data, we develop a robust and flexible pipeline for generating synthetic strawberry data from various 3D models in Blender, focusing on enhancing realism compared to prior efforts, thus providing a valuable resource for training pose estimation algorithms.

Quantitative evaluations show that our models achieve comparable accuracy on both the NVIDIA RTX 3090 and Jetson Orin Nano across several ADD-S metrics, with the RTX 3090 offering superior processing speed. However, the Jetson Orin Nano is particularly effective for resource-constrained environments, making it ideal for deployment in agricultural robotics. Qualitative assessments further validate the model's performance, demonstrating accurate pose inference for ripe and partially ripe strawberries, although challenges remain in detecting unripe specimens. This highlights opportunities for future enhancements, particularly in improving detection for unripe strawberries by exploring color variations. Moreover, the presented methodology can be easily adapted for other fruits, such as apples, peaches, and plums, broadening its applicability in agricultural automation.

Photogrammetric 3D reconstruction has long relied on traditional Structure-from-Motion (SfM) and Multi-View Stereo (MVS) methods, which provide high accuracy but face challenges in speed and scalability. Recently, learning-based MVS methods have emerged, aiming for faster and more efficient reconstruction. This work presents a comparative evaluation between a representative traditional MVS pipeline (COLMAP) and state-of-the-art learning-based approaches, including geometry-guided methods (MVSNet, PatchmatchNet, MVSAnywhere, MVSFormer++) and end-to-end frameworks (Stereo4D, FoundationStereo, DUSt3R, MASt3R, Fast3R, VGGT). Two experiments were conducted on different aerial scenarios. The first experiment used the MARS-LVIG dataset, where ground-truth 3D reconstruction was provided by LiDAR point clouds. The second experiment used a public scene from the Pix4D official website, with ground truth generated by Pix4Dmapper. We evaluated accuracy, coverage, and runtime across all methods. Experimental results show that although COLMAP can provide reliable and geometrically consistent reconstruction results, it requires more computation time. In cases where traditional methods fail in image registration, learning-based approaches exhibit stronger feature-matching capability and greater robustness. Geometry-guided methods usually require careful dataset preparation and often depend on camera pose or depth priors generated by COLMAP. End-to-end methods such as DUSt3R and VGGT achieve competitive accuracy and reasonable coverage while offering substantially faster reconstruction. However, they exhibit relatively large residuals in 3D reconstruction, particularly in challenging scenarios.

This study presents a comprehensive evaluation of six deep learning models for building exterior crack detection using UAV imagery. Our framework systematically compares Standard U-Net, ResNet34-UNet, UNet-Attention, UNet-Residual, HybridUNet, and TransUNet through rigorous ablation experiments. The models were trained on dedicated drone-captured crack imagery and evaluated using multiple loss functions and performance metrics.

Results show that TransUNet achieves optimal performance (87.66% F1 Score, 90.43% Precision, 89.99% Recall) by leveraging Transformer-based global context modeling. Notably, the performance gap among all six models remains minimal (<0.5% F1 Score difference), suggesting limited returns from increased architectural complexity alone. F1 Loss demonstrates the most balanced performance across architectures, while Focal-Dice-Loss offers superior optimization stability.

The study provides practical guidance for model selection: TransUNet with F1 Loss suits high-accuracy requirements, while simpler attention-enhanced U-Net variants offer cost-effective solutions for large-scale applications. These findings advance intelligent crack detection methodologies and emphasize balancing accuracy with computational efficiency for real-world structural health monitoring.

Unmanned Aerial Vehicles (UAVs) have contributed to a wide range of applications, becoming faster and more sustainable nowadays. However, given the significant increase in the number of UAVs, concerns regarding operational safety have grown.

Autonomous UAV path planning must ensure compliance with safety requirements. This study proposes a real-time path replanning method focused on ensuring compliance with regulations governing UAV operations. Considering no-fly zones (NFZs) defined by both static (buildings) and dynamic (people) obstacles, a low-cost and replicable solution was implemented in four main steps: 3D offline path planning using the A* algorithm and Digital Elevation Models; human detection in UAV imagery using the YOLO11m model; estimation of the person’s 3D coordinates using Monoplotting; and experiments of real-time path replanning. During flight

execution, imagery acquired by the UAV is transmitted to a server and, if a person is detected, path replanning is performed. The replanned route is then sent to the UAV controller to be executed via an SDK-based application. For flights at reduced speeds, the proposed method demonstrated feasibility in a computational environment (replanning time of 2.79 s). Simulated flight execution using the DJI Mobile SDK was successful. However, when relying on data transmission over Wi-Fi, the replanning duration on a local server (17.96 s) remained unsuitable for real-time operations. As future work, alternative solutions should be explored to ensure real-time processing. Despite the challenges, this study contributes by validating the open and free DJI MSDK application for path execution in a simulated environment, integrated with a listener application.

Point cloud registration involves aligning one point cloud with another or with a three-dimensional (3D) model, enabling the integration of multimodal data into a unified representation. This is essential in applications such as construction monitoring, autonomous driving, robotics, and virtual or augmented reality (VR/AR).With the increasing accessibility of point cloud acquisition technologies, such as Light Detection and Ranging (LiDAR) and structured light scanning, along with recent advances in deep learning, the research focus has increasingly shifted towards downstream tasks, particularly point cloud-to-model (PC2Model) registration. While data-driven methods aim to automate this process, they struggle with sparsity, noise, clutter, and occlusions in real-world scans, which limit their performance. To address these challenges, this paper introduces the PC2Model benchmark, a publicly available dataset designed to support the training and evaluation of both classical and data-driven methods. Developed under the leadership of ICWG II/Ib, the PC2Model benchmark adopts a hybrid design that combines simulated point clouds with, in some cases, real-world scans and their corresponding 3D models. Simulated data provide precise ground truth and controlled conditions, while real-world data introduce sensor and environmental artefacts. This design supports robust training and evaluation across domains and enables the systematic analysis of model transferability from simulated to real-world scenarios. The dataset is publicly accessible at: https://zenodo.org/records/17581812.

Kelp forests are vital marine habitats with significant ecological, cultural, and economic importance. These ecosystems, found along coastlines, are susceptible to regional and global stressors (such as coastal development and climate change). This paper presents Satellite-based Kelp Mapping (SKeMa), a novel framework for automatically mapping canopy-forming kelp forests using Sentinel-2 satellite imagery along the British Columbia coast, specifically to support First Nations marine planning for these species. SKeMa employs a deep learning semantic segmentation model, offering an efficient alternative to traditional, labor-intensive, and time-consuming kelp mapping methods. A cross-validation study with independent test sets yields a mean Intersection over Union (IoU) of 0.5326, demonstrating the model’s capability to detect kelp canopies across diverse coastal regions, particularly for larger kelp beds.

The research extended DualSeq, an advanced

machine-learning model for predicting sea surface temperature

(SST), crucial for understanding oceanic ecosystems and climate

patterns. Traditional SST prediction methods typically employ

time-series regressions focusing on nonlinear temporal patterns,

but often overlook vital spatial correlations in SST dynamics,

limiting their accuracy. DualSeq addresses this by integrating

spatial and temporal uncertainty quantification, with a particular

focus on the Arabian Sea. It utilises LSTM and GRU networks to

effectively harness the SEVIRI-IO-SST dataset, which contains

five years of remote-sensing data. A distinctive aspect of DualSeq

is its incorporation of a weighted normalized linear equation,

which significantly improves the accuracy of SST predictions and

enhances the dependability of spatial and temporal uncertainty

assessments. The model stands out in its ability to forecast up to

one month in advance, significantly outperforming others. For 1-

month forecasts, DualSeq shows a remarkable R² value of 0.983,

surpassing the LSTM-attention model by 7.4% and reducing

RMSE and MAE by about 65.4% and 82.4%, respectively.

This performance illustrates DualSeq’s superior capability in

capturing both short-term and long-term uncertainties in SST

forecasting.

This study presents a Station-Centric Geographically weighted Regression (SCGWR) framework for Chlorophyll-a prediction in the optically complex waters of the Gulf of İzmir using Sentinel-2 imagery. Unlike traditional global multiple regression model, the proposed approach calibrates an individual model for each sampling station while using 16 outer Moore-neighbor pixels (range 2) from surrounding stations as independent validation data in the model optimization, thereby preventing adjacency bias and information leakage in performance assessment. Compared to multiple linear regression (MLR) against 20 independent in-situ measurements, SCGWR method offers a robust, reproducible alternative for local-scale water-quality mapping in coastal environments where bio-optical variability is high.

Coastal areas play an important role economically, socially and environmentally due to their many functions.

However, these regions are at risk of erosion, which is further exacerbated by human-driven climate change.

Tracking and monitoring coastal boundaries enable efficient allocation of conservation and protection efforts.

Due to the vast size and complexity of coastal areas, on-site monitoring to track erosion is inefficient.

Artificial intelligence has shown impressive results in segmenting and extracting these boundaries from remote sensing imagery.

Historical remote sensing data make it possible to track long-term erosion but remain challenging due to the coarse resolution of older data.

Our work proposes studying the impact of super-resolution on coastal boundary segmentation using high-resolution imagery.

ESRGAN and SRCNN have proven highly beneficial in improving the quality of coarse-resolution samples, achieving superior performance compared to bicubic interpolation across scaling factors ranging from ×2 to ×12.

ESRGAN super-resolved samples achieved F1-scores ranging from 97.75% to 89.92% for scaling factors ×2 to ×12, while bicubic interpolation achieved between 97.34% and 65.27%.

These improvements demonstrate that SR enhances boundary delineation and robustness across scales.

Our work also explores the applicability of tracking erosion through historical data.

Results demonstrate a coastal boundary change of 0.23 m per year over seven years, which is on par with expected values.

This study introduces a novel framework that integrates a region-aware Full-Waveform Figure Descriptor (FWFD) with a Convolutional Vision Transformer (CvT) for underwater terrain classification using bathymetric LiDAR data. The FWFD converts sequential waveform returns into a multi-directional image-like representation, enabling the preservation of spatial correlations among neighboring laser footprints. By combining convolutional token embedding and self-attention mechanisms, the CvT effectively learns both local and global waveform features. Experiments on a YellowScan full-waveform LiDAR dataset over coastal Australia demonstrate that the proposed FWFD-CvT model achieves 95.55 % overall accuracy under moderate waveform smoothing and exceeds 98 % accuracy for underwater objects. The framework shows robust performance across complex seafloor morphologies and maintains consistency in mixed land-water environments. This research contributes a transferable paradigm for region-aware waveform interpretation and establishes a foundation for extending full-waveform analysis to terrestrial, multispectral, and topographic LiDAR applications requiring fine-scale surface characterization.

Shallow-water cultural heritage occupies a dynamic land-sea interface where coastal erosion, sediment transport, limited visibility and burial processes hinder conventional archaeological investigation. This paper presents an integrated geoinformatics framework for reconstructing the cultural dynamics of coastal and shallow submerged archaeological landscapes in southeastern Crete, Greece. The methodology combines multispectral remote sensing, satellite-derived and in situ bathymetry, UAV and shallow-water photogrammetry, marine geophysics, GIS-based coastal vulnerability, fuzzy logic multi-criteria risk assessment and digital dissemination through augmented reality. The workflow was applied at five representative case studies, including Stomio, Ierapetra harbour, Koufonisi, Chryse and associated coastal sectors. Optical data from Pleiades-1A, PlanetScope, and Sentinel-2A were used for shoreline mapping, feature enhancement, and satellite-derived bathymetry. Geophysical and bathymetric surveys covered more the 4.5 and 10 hectares respectively. UAV photogrammetry produced high resolution orthomosaics, while the proposed experimental Remote Control (RC) boat extends documentation potential to very shallow submerged environments. Integrated interpretation clarified palaeo-shorelines, submerged harbour structures, fish tanks, architectural continuities and archaeological risk hotspots. The results demonstrate a scalable and transferable framework for documenting, interpreting, monitoring, and communicating endangered shallow-water cultural landscapes.

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.