Conference Agenda

The cryosphere responds to variations in the climate. Monitoring glaciers requires research into their dynamics. The surface velocity of the Gangotri glacier was obtained in this study using the Sentinel-1 dataset. Modifying the laminar flow model improved estimates of ice thickness. Moreover, the glacier mass balance has been calculated using changes in ice thickness between 2017 and 2022. An average velocity of 0.09 m/day was observed with stretches from 0.12 to 0.23 m/day in the central trunk. A mean thickness of 189 ± 17.01 m was determined for the glacial ice. The thickest areas, with the least drag, were measured to be 587 ± 52.83 m in the middle part. Negative mass rates of -1.3 to -0.5 m.w.e./year were observed for the glacier system (with thickness changes of -3 to -0.6 m/year) due to the glacier's decreased thickness throughout time.

The Himalayan region hosts the largest reservoir of snow and ice outside the polar regions. However, ongoing climate change has resulted in widespread glacier retreat, heightening the frequency and magnitude of extreme events, including flashfloods, landslides, and Glacier Lake Outburst Floods. The Beas Basin in the northwestern Himalaya exemplifies this vulnerability, where cryospheric transformations directly threaten downstream communities, hydropower systems, and infrastructure. Despite its critical importance, long-term basin-scale records remain limited. Therefore, this study investigates the long-term cryospheric evolution of the Beas Basin and identifies emerging glacial lakes using an integrated remote-sensing and modelling-approach. Glacier mass balance from 1951 to 2024 was estimated using an Improved Accumulation-Area-Ratio method, incorporating equilibrium-line-altitudes derived from ASTER-DEM and meteorological data, alongside glacier extents from Landsat and Sentinel imagery. Current glacier ice reserves were quantified using laminar-flow and volume–area scaling methods, with surface velocities derived from sub-pixel Landsat image correlation, and slope from DEMs. Future glacial lake formation was assessed using the HIGTHIM tool, which integrates ice thickness, bed topography, and moraines. Results indicate a mean area-weighted mass balance of –0.46±0.26m.w.e.a⁻¹, corresponding to 17.75Gt cumulative ice loss (~48% of glacier-stored mass) since 1951 and a current ice reserve of 19.60±3.5 Gt. Sixty-three potential glacial lake sites were identified, with four existing lakes projected to expand, totalling 122±22 million-m³of water. These findings reveal extensive cryospheric reorganisation, with significant implications for hydrology, water security, and hazard management. The study demonstrates the value of combining satellite observations with process-based modelling for monitoring Himalayan glacier dynamics in data-sparse regions.

The intrusion of relatively warm ocean waters beneath Antarctic ice shelves is a key driver of basal melting and strongly influences ice-shelf stability. However, previous studies investigating warm-water pathways have largely relied on single-source datasets, such as ship-based Conductivity–Temperature–Depth (CTD) measurements, which are spatially sparse and limited to a few well-surveyed regions. Recent advances in multi-source remote sensing datasets provide new opportunities to address these limitations. In this study, a multi-source remote sensing–based framework is developed to identify potential pathways of relatively warm water intrusion beneath Antarctic ice shelves and to quantify the associated basal melting. The Moscow University Ice Shelf (MUIS) is used as a case study. Across the continental shelf, CTD observations, sub-ice-shelf bathymetry, and modeled ocean circulation are integrated to infer potential intrusion routes. At the ice-shelf front and base, EN4 reanalysis data are used to characterize seawater properties, while satellite-derived basal melt products are applied to analyze spatial and vertical patterns of basal melting. Results indicate that relatively warm water is mainly concentrated at depths of 300–500 m, coinciding with bathymetric depressions that facilitate its intrusion beneath MUIS. Enhanced basal melting occurs near the ice front and grounding line, primarily within the upper 0–500 m of the ice-shelf draft, with an average melt rate of ~6 m yr⁻¹. The proposed framework provides a transferable approach for investigating ocean-driven melting beneath Antarctic ice shelves.

The Antarctic Ice Sheet (AIS), the largest terrestrial ice mass on Earth, contains approximately 90% of the planet's total ice volume. This study quantifies ice discharge and associated uncertainties in AIS estimates through Input-Output method, evaluating the impact of flux gate locations on discharge magnitude and measurement uncertainty. Through analysis of key factors contributing to discharge uncertainty, we propose a gate positioning strategy that optimizes the balance between proximity to the grounding line and uncertainty minimization.

NASA’s ICESat-2, a single-photon lidar satellite launched in 2018, has for six years delivered pole-wide elevation data with <0.4 cm/yr precision. To verify and exploit these data over Antarctica, we built a “space-air-ground” calibration chain. (1) A cross-track array of corner-cube retro-reflectors (CCRs) was installed at Kunlun, Taishan and Zhongshan stations; one deployment captures both ascending and descending passes, doubling efficiency. GNSS-PPP/RTK solutions overcome the absence of fixed reference points and position CCRs to within 1 cm; comparison with ICESat-2 tracks shows sub-4 cm vertical accuracy, confirming stable on-orbit performance. (2) UAV photogrammetry during the 36th CHINARE expedition produced 5 cm-resolution DEMs of crevassed ice margins at Zhongshan/Prydz Bay. Fused with RTK ground control, these reveal ICESat-2 planimetric offsets of 2–5 m and serve as “truth” for a new Photon-Cloud algorithm that corrects slope-induced positioning errors and extends the mission’s utility in rugged terrain. (3) Whole-continent cross-over analysis of repeat tracks shows millimetre-level consistency between ascending and descending orbits; an improved cross-track model extracts robust elevation-change time series for stable ice interiors. The integrated framework provides ICESat-2 Antarctic accuracy metrics, refined processing tools and a transferable protocol for future polar photon-counting altimetry missions.

Accurate mapping of exposed rock is fundamental for cryospheric and geospatial analyses in Antarctica, yet existing products are of limited resolution and tend to underestimate true rock exposure. We present a weakly supervised deep-learning framework that refines existing rock masks by combining Sentinel-2 multispectral imagery with elevation and slope data from the Reference Elevation Model of Antarctica (REMA). A U-Net with eight input channels (six spectral bands, elevation, slope) is trained using imperfect Landsat- and GeoMap based labels. Trained on data from the Antarctic Peninsula, the model produces a 10~m rock mask that delineates small and shaded outcrops more effectively than existing datasets. While quantitative evaluation is constrained by imperfect reference data, qualitative inspection indicates improved rock–snow separation. The workflow is fully automated, requires no manual annotation, and scales efficiently to all rock-hosting regions of the continent reachable by Sentinel-2 multispectral coverage. Beyond rock mapping, the framework is transferable to other scenarios with incomplete or uncertain reference data, such as vegetation, snow, or water mapping. The resulting rock mask for complete Antarctica, together with the trained model and preprocessing scripts, will be released to support reproducible large-scale mapping and future cryospheric research.

This paper presents an efficient approach to the accessibility assessment of tram transport based on a simulation within a digital twin environment. We propose a novel framework that integrates several advanced data acquisition and processing steps: mobile mapping of the tram routes, detection of rail tracks and tram stops, and the final assessment of tram accessibility by simulating the MAL deployment in the digital twin. Our experimental evaluation demonstrates that the digital twin provides a practical and reliable tool for assessing tram accessibility.

As-built Building Information Models (BIMs) are crucial for building digitalisation, structural analysis, and life cycle management. Despite recent advances, automated reconstruction of structural elements from point clouds remains a challenging task, particularly in ensuring geometric accuracy and topological consistency within a storey and across consecutive storeys. This paper proposes an automated method for refining topological inconsistency between columns, beams, and slabs, ensuring consistent as-built BIMs. The method places Constructive Solid Geometry (CSG) at the core of the refinement process, driven by fundamental structural principles. The method starts by creating solid rectangular prisms from labelled point clouds. Beams are then aligned both vertically and horizontally within each storey. Columns are vertically aligned across consecutive storeys. Topology relationships between the elements are retrieved and encoded in graphs. These graphs, together with a set of Boolean operations, are used to resolve gaps and trim overlaps between the connected elements. The refined elements are represented in accordance with the IFC standards. The proposed method was validated on two multi-storey case studies representing frame and flat-slab building structures. Both qualitative and quantitative evaluations confirmed the effectiveness of the approach, achieving significant geometric accuracy and topological consistency. In addition, the method exhibits efficient runtime performance, indicating its promise for scalable Scan-to-BIM automation.

Bridge infrastructure plays a critical role in transportation networks, requiring reliable and efficient methods to detect missing bolts to ensure structural integrity and prevent failures. This study proposed a novel methodology integrating point cloud-based Digital Twins (DTs) with Topological Data Analysis (TDA), specifically using Persistent Homology (PH), for robust and accurate missing bolt detection. The framework combines 3D photogrammetric reconstruction to generate point cloud-based DTs, Convolutional Neural Networks (CNNs) for precise bolt localization, and PH to identify and quantify missing bolts. Through parameter evaluations and a real-world bridge case study, the proposed approach demonstrated high detection accuracy, effectively identifying missing bolts with a false positive rate below 10%. These findings confirm the reliability and effectiveness of integrating DTs with TDA as an advanced data-driven approach for automated structural inspection and bridge health monitoring.

Semantic segmentation of indoor point clouds is a fundamental task in 3D scene understanding, supporting applications such as virtual reality, indoor navigation, and building management. Point-based transformer models achieve high accuracy but require substantial computational resources, while superpoint-based methods are more efficient yet often less precise. To address this trade-off, we propose LGFormer, a lightweight framework that integrates Graph Convolutional Networks (GCN) and transformers to jointly capture local and global contextual features. The method constructs a superpoint-based topology graph, where local features are extracted using GCN and global dependencies are modeled through transformer layers. Experiments on the S3DIS and ScanNet++ datasets demonstrate that LGFormer achieves 90.7% and 88.5% segmentation accuracy, respectively, while reducing inference time by more than 99% compared with point-based transformers. By effectively leveraging superpoints and local-global feature fusion, LGFormer dlivers competitive accuracy with significantly lower computational cost, making it suitable for large-scale indoor scene analysis.

Corroding reinforcement leads to cross section loss and reduced structural capacity of concrete bridges. Detecting exposed rebars

(ER) is crucial during bridge inspection to plan countermeasures early and prevent further corrosion. With advancements in deep

learning, several public datasets derived from inspection imagery have been released to identify ER and other concrete damage

automatically. At the same time, Uncrewed Aerial Systems (UAS) have become more capable of navigating even underneath the

bridge deck. This combination holds promise to improve efficiency of bridge inspection methods, but obtained imagery differs

from available datasets, featuring very small damages and complex backgrounds. To address this mismatch, this work reviews

publicly available ER datasets, presents a UAS-based bridge inspection dataset for evaluating ER damage (UBID-ER-val), and

quantifies similarities and differences between them. We train several YOLOv8 models on conventional inspection documentation

images and benchmark the reviewed datasets, scoring F2 = 0.229 at S2DS, F2 = 0.430 at CODEBRIM, F2 = 0.584 at Dacl10k,

compared to F2 = 0.505 at UBID-ER-val. We analyse factors influencing performance and find that tiled inference raises Recall

(+0.166) but drastically reduces Precision (−0.309), while matching training and validation image resolution underperforms across

all datasets (−0.061 to −0.129). The differences in best-performing combinations underscore the underlying domain shift that

complicates practical deployment. As a practical outcome of this work, UBID-ER-val is made publicly available to enable objective

benchmarking of ER detection models and to assess their reliability under field conditions.

Detecting visible electrical utilities is a prerequisite for developing advanced reasoning strategies to reconstruct hidden in-wall networks. This paper investigates the detection of visible power-related utilities using a domain-adaptive deep learning-based vision pipeline based on the YOLOv11-L, object detection model. Four publicly available datasets containing power sockets, power strips, and light switches were curated, relabeled, and merged into a unified training dataset of 3,459 images. The resulting model achieved a mean average precision (mAP) of 0.74 for power sockets and strips and 0.98 for light switches, demonstrating strong detection performance. Real-time evaluation on a low-cost smartphone via the Ultralytics HUB App indicates reliable detection in small-scale real-world environments and detected utilities could be integrated automatically into semantic indoor models using a marker-less referencing approach. The work further highlights broader applications, including Augmented Reality-based visualization to reduce cognitive load for project managers and inspectors or construction workers and electricians, and its potential use as input for existing and future reasoning methods for hidden-utility reconstruction. The prepared dataset, trained model and source code is available at: https://github.com/hcu-cml/indoor-electrical-facility-detection

Accurate large-scale forest canopy height mapping is critical for biomass estimation and carbon monitoring, yet remains constrained by the limitations of individual remote sensing systems. This study presents a multisensor machine learning framework that integrates GEDI LiDAR with Sentinel-2, Sentinel-1, ALOS-2 PALSAR-2, and 3DEP terrain data to generate a 25 m resolution canopy height model (CHM) for the Northeastern United States in 2022. A key contribution is an adaptive GEDI relative height (RHad) strategy that selects optimal RH metrics based on canopy density, improving generalization across heterogeneous forest conditions compared to any single fixed RH metric. Independent validation against airborne LiDAR and USDA FIA plot data confirms that RHad achieves the highest accuracy and lowest bias of all configurations tested. The resulting regional canopy height map provides a reliable baseline for large-scale forest monitoring and future multitemporal analyses.

Timely and accurate detection of deforestation is essential for managing tropical forests, yet individual Earth observation sensors have inherent limitations. Multispectral imagery offers detailed spectral information on vegetation properties but is frequently hindered by cloud cover, while Synthetic Aperture Radar (SAR) imagery provides insights on vegetation structure independent of weather conditions but is sensitive to moisture variability and residual vegetation post-clearing. The complementary nature of these data has motivated multi-source fusion approaches, though most existing methods rely on offline processing or decision-level integration, limiting their real-time applicability.

This study generalizes a Bayesian Online Changepoint Detection (BOCD) framework based on the recursive estimation of the number of acquisitions since the last change to asynchronous, irregularly sampled Sentinel-1 SAR and Sentinel-2 multispectral time series. A dynamically weighted fusion mechanism is implemented, in which each sensor’s relevance reduces with increasing time since its last observation, according to a physical decay model. The resulting method, named ms-BOCD, enables interpretable, and Near Real-Time (NRT) detection of forest loss.

The ms-BOCD method is validated using MapBiomas Alerta reference data spanning deforestation polygons ranging from 0.1 to 50 hectares in the Brazilian Amazon.

Compared to $VH$-BOCD (BOCD using Sentinel-1 cross-polarization only) and the operational RADD and TropiSCO systems, ms-BOCD achieves a 25% improvement in detection performance and maintains 13% fewer false alarms than Global Forest Watch (GFW), a platform that aggregates multiple independent deforestation alert products. Overall, these results demonstrate the strong potential of multi-source Bayesian fusion for operational tropical forest monitoring.

This study assesses long-term forest change in Liberia’s Community Forest Management Areas for Conservation (CFMACs) and Protected Areas (PAs) from 2002 to 2024 using an integrated Landsat–Google Earth Engine (GEE) and an ArcGIS Pro workflow. Annual dry-season composites for three time periods were classified using a Random Forest model with 81.7% accuracy (Kappa = 0.781). Results show contrasting governance outcomes: CFMACs experienced modest forest gains from 2002–2014 and localized losses thereafter, while PAs exhibited larger overall gains but also greater cumulative forest loss, particularly along concession boundaries. Stability analysis revealed that PAs retained a higher proportion of Mature Forest over the 20-year period, whereas CFMACs showed more dynamic turnover and localized regrowth. The combined GEE/ArcGIS approach provides a scalable, transparent monitoring framework and demonstrates how governance type influences forest persistence, degradation, and recovery across Liberia’s tropical landscapes.

Accurate assessment of tree cover change is essential for monitoring deforestation, carbon emissions, and restoration progress. However, validation of global forest change products remains limited by the scarcity of consistent reference data. We present a benchmark dataset for tree canopy cover change evaluation across North America, derived from multitemporal airborne LiDAR data from the National Ecological Observatory Network (NEON). Using canopy cover maps from 2016–2022, we identified tree cover loss as a decrease of at least 20% in canopy cover persisting across multiple time steps. Thirty NEON sites spanning diverse biomes were included, forming a spatially and temporally robust reference for change detection.

We demonstrate the benchmark applicability by evaluating two global products: Forest Carbon Diligence (FCD) from Planet Labs, and the Global Forest Change (GFC) from University of Maryland. Across all sites, both products showed strong agreement with the LiDAR benchmark (r = 0.90 for FCD; r = 0.88 for GFC), though both underestimated change extent. Categorical metrics revealed higher precision than recall, indicating conservative detection thresholds relative to the benchmark.

This study establishes the NEON LiDAR-based benchmark as a valuable open resource for assessing and improving large-scale canopy cover change datasets. The approach highlights the importance of high-resolution, temporally consistent reference data for evaluating the accuracy of global monitoring products and guiding improvements in forest carbon accounting and conservation applications.

Under climate and human pressures, vegetation in Inner Mongolia exhibits complex fragmentation and degradation. Scientifically inverting its spatiotemporal dynamics is crucial for regional ecological restoration. To address the challenges faced by traditional cellular automata (CA) models in large-scale complex ecological transition zones—such as computing power bottlenecks and subjective transition rules—this study proposes a cloud-based vegetation degradation simulation and inversion framework (CA-VDS) via Google Earth Engine. By coupling Random Forest (RF) and an Improved Genetic Algorithm (IGA) with CA, the framework extracts nonlinear driving potentials and automates the optimization of bidirectional transition thresholds. Validation against the 2020 baseline shows CA-VDS effectively resolves manual parameter tuning limitations. Furthermore, it smooths the spectral fluctuations caused by short-term sporadic disturbances through the underlying spatial neighborhood mechanism, demonstrating its value in simulating potential ecological degradation risks and developmental trajectories. This work not only verifies the reliability of CA-VDS in analyzing complex nonlinear ecological processes, but also establishes a reliable parameter baseline and model paradigm for subsequent integration with CMIP6 and other multi-scenario data to conduct long-term future ecological predictions.

This study explores the application of Particle Swarm Optimization (PSO) to enhance vegetation indices (VIs) for the assessment of woody vegetation in a semi-arid savannah ecosystem. By optimizing VIs, the research aims to improve the discrimination between vegetated and non-vegetated areas, facilitating a more accurate random forest classification for habitat quality assessment. The optimization process preserves minimum VI values across different sensors to maintain lower bounds of reflectance, ensuring ecologically valid signals are represented, particularly in low-vegetated areas. Results indicate that maximum VI values increase post-optimization, enhancing sensitivity to canopy vigor, stress, health, and presence. The study highlights the effectiveness of UAV-derived indices, such as NDVI, NDRE, and SAVI, in capturing the dynamics of vegetation health and dryness, thereby contributing valuable insights into remote sensing methodologies for ecological monitoring.

The paper proposes the STF-Net (Spatial-Textural-Frequency Network) framework, designed to achieve a paradigm shift from traditional "change detection" to "suspected area identification," precisely identifying suspected abandonment areas and effectively suppressing pseudo-changes. The core of this framework lies in its fine-grained four-level annotation system and a three-stream parallel feature extraction architecture. The four-level annotation system includes "confirmed abandonment," "suspected abandonment," "non-abandonment change," and "no change," providing a robust data foundation for the model to learn the "suspected" concept, thereby compensating for the lack of "user-oriented" definitions in existing research. The three-stream parallel feature extraction architecture captures changes in geometric information (location, shape) via the spatial stream; quantifies the transition of surface texture from ordered to disordered, capturing structural degradation due to abandonment, through the textural stream; and analyzes periodic structural information in images, identifying the disappearance of periodic structures caused by cessation of cultivation, using the frequency stream. These three types of features are deeply fused, comprehensively utilizing information from different modalities, significantly enhancing the model's adaptability and identification accuracy in complex scenarios.