Conference Agenda

This paper presents a hierarchical method for collapsing a polygon to point within a structured multi-scale representation. The approach is based on the straight skeleton, which drives the shrinking process through event-based transformations such as edge and split events. These events define how the polygon changes during collapse and produce a hierarchy of intermediate geometric states between the initial polygon and the final point.

The resulting hierarchy is integrated into a Dual Half-Edge (DHE) structure, where the primal space represents successive geometric states and the dual space represents the hierarchical relations between them. This produces a connected 2D+1D representation in which the third dimension corresponds to scale rather than physical height. The resulting model is interpreted as a LoD Transition Space (LTS), allowing the full polygon-to-point transition to be represented continuously across scale.

The proposed framework contributes to model-based multi-scale representation by explicitly linking geometric transformation, topological change, and hierarchical structure within a unified representation. In addition to its relevance for vario-scale cartography and generalisation, the method also has potential applicability in domains where gradual geometric transformation is required, such as procedural modeling, animation, and related geometric applications.

The CGCS2000 (China Geodetic Coordinate System 2000) reference framework, which has been employed since July 1, 2008 is based on the ITRF97 reference framework and only meets the application requirements of China's regional. With the sustained development of China's economy and society, and the globalization of the applications of BeiDou navigation satellite system (BDS), there is a need to establish global CGCS2000 reference framework. This paper studies mathematical method for construction Global CGCS2000 reference framework, the theory and algorithm of two-step method with the inner constraints theory is analysed. The constraint conditions of coordinate reference are redefined according to the minimum standard of frame transition parameters and rate variation. As a result, the adjusted network enjoys the highest degree of fitting to the shape of the initial network and maintain the inherent purity of the coordinate network using different observation technologies, this research result can improve the basic theory of terrestrial reference framework determination, and provide scientific methods for the globalization of the CGCS2000.

Interpreting multi-storey property rights is difficult when information is scattered across 2D plans and text or locked inside desktop projects. We present a web-based pathway that communicates strata lots and common property consistently across levels in a standard browser. Aligned with the 3D Cadastral Survey Data Model and Exchange (3D CSDM) of Australia, we propose an open-source, web-first approach. The method couples a lightweight browser viewer (level/tenure filters, plan overlay, search, readable legend) with an explicit conversion step that standardises common GIS inputs into a fixed core JSON profile, with limited official CSDM-aligned JSON-LD hooks applied only to selected keys that have exact matches in the published vocabularies. Using a New South Wales case study, we evaluated the viewer against ISO 9241-11 criteria (effectiveness, efficiency). Across repeated trials (cache disabled/enabled), mean page-open times were 0.60 s (Chrome) and 1.48 s (Edge); interaction averaged 50–60 FPS; level filters applied in 40–55 ms; all five tasks succeeded. Practically, this delivers fast, consistent 3D communication of lots and common property without installs, lowering access barriers for agencies and owners while aligning with 3D CSDM’s web-first direction. Next, we will finalise parity between Upload-and-View and the Reference Viewer and add a light in-viewer validation panel.

This research presents a methodology for conducting shadow geometric analysis, specifically the shadow boundary in an urban model. Input data include a georeferenced CityGML LoD2 and terrain model. Additional land cover data is used to exclude some parts of the model from analysis. Shadow computation is based on a sunray vector, which is computed based on the sun position on the given day and time. The geometry of original models are divided into parts classified as either exposed to the sun or shaded. It can be used for analytical purposes in other applications, such as urban planning, energy assessment, and photovoltaic potentiality analysis, by accurately identifying sunlit and shaded areas within 3D city models. The analysis is performed in the FME software package, which is a general-purpose ETL tool.

It is desirable that a 3D city model at level of detail 2 (LOD2) has texture images mapped on building surfaces. Owing to the cost of image collection, it would be the best way to use aerial images for texture mapping at present. Although aerial oblique images provide higher-resolution texture images, using aerial oblique images has a major issue of occlusion. Accordingly, we develop software for texture mapping to a 3D city model using aerial nadir and oblique images, aiming to minimize the impact of occlusion. The software designed to be used in ordinary operation includes the features of automatically detecting occlusions on building surfaces within images by utilizing the geometry of a 3D city model and automatically selecting appropriate oblique and nadir images for texture mapping. The major feature of the developed software is its ability to process grid by grid on a building surface. The validation experiment results confirm the software's satisfactory performance in practice. Moreover, the experiment results indicate that the performance of the software depends on the ability of a 3D city model to represent buildings. Since we have recognized that it would be effective if each pixel of a texture image has its own resolution, we plan to modify the software so that each pixel can have its own resolution.

Rapid urbanization in developing countries such as Iran has intensified pressure on agricultural land, highlighting the need for sustainable and efficient food production systems. Agricultural Plastic Greenhouses (APGs) have become a scalable alternative by enabling year-round cultivation and optimized land utilization. However, their rapid expansion necessitates continuous monitoring to evaluate structural integrity and environmental impacts, including soil degradation, plastic waste accumulation, and water consumption. This study presents a deep learning-based framework for the automated detection and condition assessment of APGs using 0.5~m resolution Google Earth imagery across four major agricultural regions in Tehran County: Pakdasht, Qarchak, Pishva, and Varamin. The proposed pipeline integrates YOLOv11 for precise APG segmentation with a U-Net architecture employing a MobileNetV2 backbone for classifying damaged and intact structures. Out of 158,912 analyzed image tiles, 6,835 contained APGs, covering an estimated area of 18.73~km\textsuperscript{2}. Among these, 1,863 damaged structures were identified, predominantly located in Pakdasht and Pishva. Around 20\% of the annotated greenhouses were verified on-site, improving labeling reliability, and the relatively standardized design of APGs in Iran suggests the model could generalize to similar regions, with minor fine-tuning using local samples if necessary. GIS-based spatial analysis further delineated potential plastic waste risk zones, supporting targeted environmental management. Comparison with government statistics and Sentinel-2 imagery from 2021 and 2024 revealed a continued shift toward greenhouse farming in response to declining cropland availability. The proposed framework provides a scalable and replicable tool for periodic APG monitoring, facilitating data-driven policymaking and sustainable agricultural planning.

Combining visual semantics with language representations has made 3D interpretation more flexible and intuitive. Recent advances in Gaussian Splatting extend this to efficient 3D language fields supporting open-vocabulary queries. However, existing approaches show limited generalization in large urban scenes, especially for detailed building segmentation. Semantic 3D city models such as CityGML, by contrast, provide hierarchical and geometry-aligned structural semantics that complement appearance driven visual cues. We introduce CityLangSplat, which integrates CityGML semantics into 3D Language Gaussian Splatting for urban environments. CityLangSplat rasterizes CityGML into pixel-aligned semantic maps, extracts vision-language features from SAM-derived segments and CityGML regions, and compresses both sources into a shared latent space via a lightweight autoencoder. 3D Gaussians are then optimized with a coverage-aware loss that balances accurate, building-focused CityGML supervision with broader SAM supervision, enabling geometry-aligned open-vocabulary reasoning in urban scenes. Experiments on TUM2TWIN and ZAHA datasets show consistent gains over LangSplat, with relative improvements of 22.9% in 2D and 15.1% in 3D evaluation while preserving real-time rendering. CityLangSplat provides a practical framework for combining semantic city models with language-embedded 3D Gaussian Splatting for geometry-aligned urban scene interpretation. Code will be released at https://github.com/zqlin0521/CityLangSplat.

Accurate building roof modeling is fundamental to urban analytics, digital twins, and 3D city reconstruction; however, progress in deep learning–based reconstruction is constrained by the limited availability of diverse, high-resolution datasets with detailed geometric annotations. This study introduces the RoofVIP dataset, a large-scale benchmark featuring very high-resolution RGB orthophotos paired with 2D roof vectors that capture diverse urban morphologies across Munich, Germany. Following Level of Detail (LoD) 2.0 principles, RoofVIP encompasses a wide range of roof geometries and architectural complexities, enabling evaluation of both segmentation- and vectorization-based reconstruction methods. Two paradigms are examined: a two-step segmentation-based approach (Cascade Mask R-CNN, Mask R-CNN, SOLOV2, YOLACT) and a one-step direct vector prediction approach (HEAT, PolyRoof). ImageNet-pretrained region-based models, particularly Mask R-CNN and Cascade Mask R-CNN, achieve the highest segmentation accuracy, effectively delineating complex roof boundaries while revealing limitations on small or irregular structures. Geometry-based models show complementary strengths, with HEAT emphasizing topological regularity and PolyRoof focusing on geometric precision. Although performance is lower than on simpler datasets such as HEAT and Roof Intuitive, RoofVIP exposes challenges related to geometric diversity and scale variation, serving as a rigorous benchmark. The dataset includes predefined training, validation, and test splits, enabling consistent benchmarking across methods. By providing a challenging and diverse geometric landscape, RoofVIP aims to advance geometry-aware deep learning approaches and support scalable, high-fidelity 3D urban modeling. The dataset is publicly available through the project page at https://chaikalamrullah.github.io/RoofVIP/.

The proliferation of continuous Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) has shifted the paradigm of 3D aerial reconstruction from relying solely on geometric stereo matching to inverse rendering optimization. However, while these emerging rendering-based frameworks excel in synthesizing photo-realistic novel views, their capability to extract accurate surfaces in complex aerial scenarios remains ambiguous compared to traditional methods. To establish a clearer understanding, this study presents a comprehensive evaluation of five representative frameworks spanning traditional Structure from Motion (SfM), purely Signed Distance Field (SDF) representations, unstructured 3D Gaussians, hybrid voxel-Gaussians, and strictly explicit sparse voxels. By systematically standardizing identical computational environments, inputs, and unified mesh-extraction pipelines on both real-world airborne LiDAR datasets and synthetic cityscapes, we assess their performance regarding visual fidelity, geometric accuracy, and resource efficiency. The experimental results reveal that while traditional MVS produces the highest overall geometric precision by strictly enforcing multi-view rigid geometry, it is prone to failures in texture-less regions. Among rendering-based representations, a fundamental trade-off exists: highly flexible, unstructured 3DGS achieve highest visual scores but degrade the underlying geometric surfaces; conversely, explicitly structured techniques demonstrate distinct superiority in regularizing topological coherence and floating artifact suppression. Furthermore, we observe that integrating structured voxels avoids the severe memory bottlenecks associated with extracting geometries from chaotic unorganized primitives. These empirical findings emphasize that for large-scale aerial photogrammetry, integrating explicit spatial structuralization into differentiable rendering pipelines is imperative for achieving scalable operations and bridging the geometric accuracy gap with traditional methods.

In dense urban environments, aerial image acquisition often suffers from occlusions and redundant data due to the lack of quantitative evaluation tools at the flight-planning stage. To address this issue, this study develops a flight-planning support system that enables pre-acquisition visibility analysis for both terrain and building surfaces using existing 3D city models. The system performs ray-casting simulations based on user-defined flight parameters to quantify and visualize occluded and visible regions before flight, allowing planners to evaluate data quality and optimize image acquisition efficiency. Experiments were conducted using real flight plans with two representative aerial cameras: the Leica CityMapper-2 for multi-directional texture mapping and the Vexcel UltraCam Eagle 4.1 for nadir-based topographic mapping. The results show that the system effectively visualizes occlusions on roofs and walls, predicts building lean in nadir imagery, and assesses the influence of overlap ratios on ground visibility. These analyses enable users to design more cost-effective and geometrically consistent flight plans by identifying redundant overlaps and ensuring sufficient coverage for DSM and true-orthophoto generation. The proposed framework provides a quantitative and objective approach to improving the transparency and reliability of aerial survey planning, and it offers a foundation for integrating visibility simulation with subsequent photogrammetric workflows such as surface reconstruction and texture mapping.

There is a high demand for accurate and up-to-date territorial digital twins for human activities, but their production and updating costs remain prohibitive for many applications. Their generation relies on acquiring LiDAR and/or image data over the territory of interest. Each modality has its advantages: LiDAR is more accurate but more costly, while images are noisier but less costly and more easily accessible. Combining these two technologies to produce and update digital twins is thus a promising avenue.In this paper, we propose a pipeline based on 3D change detection to update a LiDAR point cloud using newer aerial imagery. First, triangle meshes are generated from LiDAR data and image-based dense matching. Then, 3D ray tracing is used to detect changes. After removing the changed parts, the point clouds are fused to update the scene.The proposed method is demonstrated on two datasets in France.The code will be open source on Github: https://github.com/whuwuteng/ChangeUpdateJN.

Lightweight building surface models are crucial for digital city, navigation, and fast geospatial analytics, yet conventional multi-view geometry pipelines remain cumbersome and quality-sensitive due to their reliance on dense reconstruction, meshing, and subsequent simplification. This work presents SF-Recon, a method that directly reconstructs lightweight building surfaces from multi-view images without post-hoc mesh simplification. We first train an initial 3D Gaussian Splatting (3DGS) field to obtain a view-consistent representation. Building structure is then distilled by a normal-gradient–guided Gaussian optimization that selects primitives aligned with roof and wall boundaries, followed by multi-view edge-consistency pruning to enhance structural sharpness and suppress non-structural artifacts without external supervision. Finally, a multi-view depth–constrained Delaunay triangulation converts the structured Gaussian field into a lightweight, structurally faithful building mesh. Based on a proposed SF dataset, the experimental results demonstrate that our SF-Recon can directly reconstruct lightweight building models from multi-view imagery, achieving substantially fewer faces and vertices while maintaining computational efficiency.

We present CARS, a multiview stereo pipeline developed by CNES. This pipeline will be integrated into the CO3D mission processing chain, a mission whose goal is to generate a 3D model of the Earth in less than four years. Because this is an operational mission involving massive production, particular attention has been paid to ensuring that the software is robust, efficient and includes a set of advanced automatic processing features. The paper will provide a comprehensive overview of all the features developed since its creation to achieve this goal.

We present SatGeo-NeRF, a geometrically regularized NeRF for satellite imagery that mitigates overfitting-induced geometric artifacts observed in current state-of-the-art models using three model-agnostic regularizers. Gravity-Aligned Planarity Regularization aligns depth-inferred, approximated surface normals with the gravity axis to promote local planarity, coupling adjacent rays via a corresponding surface approximation to facilitate cross-ray gradient flow. Granularity Regularization enforces a coarse-to-fine geometry-learning scheme, and Depth-Supervised Regularization stabilizes early training for improved geometric accuracy. On the DFC2019 satellite reconstruction benchmark, SatGeo-NeRF improves the Mean Altitude Error by 13.9% and 11.7% relative to state-of-the-art baselines such as EO-NeRF and EO-GS.

High dynamic range (HDR) variations in satellite optical imagery arise from extreme differences in surface reflectance and illumination conditions. Conventional satellite NeRF frameworks are typically trained on tone-mapped or radiometrically enhanced images, where nonlinear preprocessing alters the physical relationship between measured pixel values and true scene radiance. This leads to biased photometric optimization and loss of geometric fidelity, especially under strong illumination contrasts. To address these limitations, we propose an HDR-consistent learning framework that integrates RawNeRF-style radiance supervision with shadow regularization. The method trains directly on raw satellite imagery using a logarithmic, tone mapping–aware loss that preserves linear radiance and stabilizes optimization under high dynamic range conditions. In parallel, a soft shadow regularization constrains network-predicted shadows using geometric cues derived from solar ray casting, promoting physically consistent irradiance decomposition. Experiments on four AOIs from the DFC2019 dataset demonstrate that HDR-aware radiance learning significantly improves DSM accuracy by maintaining linear radiometric consistency. The proposed shadow regularization also improves geometric consistency in structure-dominated urban scenes, although its effect is limited in vegetation-dominant areas where shadow cues are less informative. Although performance gains are smaller in vegetation-dominant areas, the results confirm that combining HDR radiance learning with geometric shadow regularization yields more radiometrically consistent and geometrically accurate 3D reconstruction from satellite imagery.

Recently, 3D Gaussian Splatting has been introduced as a compelling alternative to NeRF for Earth observation, offering competitive reconstruction quality with significantly reduced training times.

In this work, we extend the EOGS framework to propose \namemodel, a novel method tailored for satellite imagery that directly operates on raw high-resolution panchromatic data %and multispectral data

without requiring external preprocessing.

Furthermore, we embed bundle adjustment directly within the training process with optical flow techniques, avoiding reliance on external optimization tools while improving camera pose estimation.

We also introduce several improvements to the original implementation, including early stopping and TSDF post-processing, all contributing to sharper reconstructions and better geometric accuracy.

Experiments on the IARPA 2016 and DFC2019 datasets demonstrate that EOGS++ achieves state-of-the-art performance in terms of reconstruction quality and efficiency, outperforming the original EOGS method and other NeRF-based methods while maintaining the computational advantages of Gaussian Splatting. Our model demonstrates an improvement from 1.33 to 1.19 mean MAE errors on buildings compared to the original EOGS models.

The emergence of satellite constellations enables near-synchronous multi-view optical imaging, offering new opportunities for large-scale 3D city modeling. Yet a practically promising configuration, in which a primary near-nadir view is complemented by multiple oblique side-looking viewpoints, remains under-examined. This study develops a controlled semi-simulation framework to analyze how multi-view imaging geometry affects the recoverability of urban 3D structures. Under idealized conditions with imaging perturbations removed, e.g., radiometric, illumination, and sensor model errors, the experiments focus on three practical factors: the number of side-looking views, view obliqueness, and the constellation’s azimuthal orientation relative to the scene. With parameter sweep analysis, it reveals an asymmetric U-shaped trend between reconstruction performance and both the view count and the obliqueness: moderate angular diversity markedly strengthens urban scene recoverability. In contrast, large obliqueness reduces inter-view overlap and destabilizes matching, while excessive redundancy introduces consistency issues that ultimately degrade reconstruction performance. Furthermore, the results shows that geometric accuracy, completeness, and texture appearance each peak at different parameter combinations, revealing intrinsic trade-offs in multi-view urban reconstruction, as different evaluation criteria favor distinct optimal configurations. The study provides practical guidance for the geometric design and mission planning of multi-satellite constellations aimed at improving satellite-based 3D modeling in urban areas.

This work presents an illumination-prior-based reconstruction model for high-resolution DEM generation from single-view lunar imagery, developed for the extreme illumination conditions and rugged terrain of the lunar south pole. The model integrates an initial DEM prior with multi-scale monocular image features and incorporates illumination priors derived from solar geometry to enhance stability in shadowed, low-texture, and terrain-transition regions. Through cross-modal feature fusion, it effectively aligns geometric structure with shading and photometric cues, enabling accurate recovery of fine-scale topography even when visual information is severely degraded. Experimental evaluations across multiple south-polar regions show that the proposed reconstruction model outperforms existing deep learning approaches and the classical Shape-from-Shading method in elevation, slope, and aspect accuracy, with independent validation using LOLA laser altimetry points confirming its improved geometric reliability. Visual comparisons demonstrate clear advantages in reconstructing crater rims, steep slopes, and permanently shadowed areas where conventional methods often fail or produce blurred terrain structures. The model also maintains robust performance under varying solar azimuths, highlighting the effectiveness of incorporating illumination priors to improve generalization in challenging environments. Overall, the proposed reconstruction model provides a reliable and effective solution for detailed lunar terrain recovery from monocular images and offers valuable support for scientific investigation, resource assessment, landing-site evaluation, and mission planning in the lunar south polar region.

To address the critical demand for high-precision mapping of the Lunar South Pole (LSP)—a region pivotal for deep space resource utilization yet plagued by extreme illumination variations, extensive permanent shadow regions (PSRs), and weak texture—this study proposes a control network construction method for multi-temporal Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images, anchored in lunar impact crater matching. Leveraging the morphological stability and spatial consistency of impact craters, we first created a dedicated dataset: 94 multi-temporal LSP orthophotos (1 meter/pixel resolution) with manual annotations, allocating 70% for YOLOv8 model training and 30% for validation to ensure accurate crater detection (extracting center coordinates and semi-major/semi-minor axes). For virtual feature point matching, we integrated crater geometric attributes (coordinates, aspect ratio) and inter-crater topological relationships (distance, azimuth angle) to build local descriptors, enhanced by KD-tree indexing for efficient neighborhood queries, multi-attribute similarity measurement, and bidirectional voting to eliminate mismatches. For large craters, normalized cross-correlation (NCC) was used for secondary matching to refine accuracy. Post-matching, tie points were back-projected from orthophoto to original image space via ground coordinates. Experiments on 1,208 LRO NAC images showed the method outperforms SIFT and SuperPoint: it generated 938,029 tie points (even in dark shadows) with 2,347,629 measurements, and bundle adjustment achieved a sigma naught of 0.68. This work enables automatic high-quality control network construction, supporting reliable LSP topographic mapping for deep space exploration.