Road change detection is essential for maintaining up-to-date digital maps; however, conventional update processes rely heavily on the manual interpretation of aerial imagery, leading to high labor costs and inconsistent outcomes. To address these limitations, this study proposes an automated road change detection method that integrates aerial orthophoto-based segmentation with geometric boundary deviation analysis. Road areas are first extracted from high-resolution aerial orthophotos using SegFormer, a Transformer-based semantic segmentation model. The segmentation results are then converted into vector polygons for geometric analysis. Structural changes, such as newly constructed or removed roads, are detected through a difference-based comparison with historical digital maps. Simultaneously, shape changes are quantitatively analyzed by measuring geometric deviations between road boundaries. Specifically, vertex-wise distances between corresponding boundaries are computed, and the overall deformation is evaluated using Root Mean Square Error (RMSE), incorporating Z-score-based outlier removal to ensure robustness against noise.Experimental results demonstrate that the proposed method effectively detects both structural changes and subtle geometric variations, including road expansions and boundary shifts. Furthermore, the method enables clear object-level classification of change types, providing a practical and efficient framework for digital map updating workflows.
Accurate camera pose estimation in urban environments remains challenging when reference imagery is generated from low-detailed, textureless 3D city models and must be matched against real world imagery. In this work we (i) extend our existing iterative object-basesd visual localization approach with an additional semantic feature and (ii) conduct a systematic baseline study of contemporary feature matching techniques on such cross-domain query-reference image pairs. As a first step to close the domain gap, we augment our iterative object-based visual localization pipeline with semantic masks derived from a pretrained semantic segmentation model. Intersection-over-Union between query and rendered masks is incorporated into the matching score, leading to a better pose accuracy. For the baseline study, we use a range of feature matching techniques: handcrafted (SIFT, AKAZE, ORB, FAST), learned detectors (XFeat, Key.Net, DeDoDe, DISK, AffNet), learned descriptors (XFeat, DISK, DeDoDe, HardNet), learned matchers (LightGlue, LoFTR), the line matcher SOLD2, and the learned matchers MINIMA-RoMa, MINIMA-LoFTR, MINIMA-XoFTR, and MatchAnything, which were trained on cross-modality datasets. The cross-modality focused matchers achieved the best results. For 20% 10%, 9%, and 7% of the evaluated query images the estimated camera pose had a translation error less than 5m and a rotation error less than 5◦. In this context, the other methods were only able to achieve a maximum success rate of 1.4%.
Point cloud semantic segmentation of indoor environments is a fundamental task in 3D scene understanding. However, existing methods mainly rely on geometric structures and color information, which are prone to error results in scenarios involving occlusion, sparse sampling, and geometrically similar structures. To address this issue, this paper proposes a text-knowledge-guided method for the point cloud semantic segmentation of indoor 3D scene. Built upon RandLA-Net as the baseline, the proposed method first constructs the textual semantic prototypes using multi-template prompts, and further enhances the stability of semantic anchors through periodic prototype refreshing. Then, a cross-modal semantic feature alignment mechanism is introduced at both the shallow and the high-level feature stages. Through feature alignment, bidirectional semantic interaction, and gated fusion, textual priors are progressively injected into the point cloud feature learning process. Finally, the model is jointly trained with a point-wise classification loss, a text-prototype alignment constraint, and a boundary optimization constraint to improve the semantic feature discrimination and segmentation boundary quality. Experimental results on the S3DIS dataset demonstrate that the proposed method achieves 86.8% OA, 81.6% mAcc, and 67.2% mIoU, exhibiting more stable segmentation performance in complex indoor scenes. As a consequence, these results indicate that incorporating textual semantic priors can effectively enhance high-level semantic representations of point clouds, providing a feasible solution for indoor 3D scene understand.
Urban building clusters can significantly alter the low-altitude urban wind field, creating local regions with high wind speed, intense turbulence, and strong non-uniform disturbances. These conditions may cause lateral deviation, attitude instability, increased trajectory error, and even exceed the wind-resistance limits of low-altitude UAVs. To address the lack of explicit modeling of local high-risk wind regions in traditional path planning methods, this paper proposes a wind-risk-aware path planning method, termed RP- A* (Wind Risk-Aware Probability A*). First, a steady-state RANS urban airflow model with the standard k-ε turbulence model is used to obtain the mean wind field and turbulence statistics in the low-altitude flight region. Second, the CFD results are reconstructed on a grid to build a wind risk model consisting of gust risk, crosswind risk, and overall wind-limit-exceedance risk. Third, a direction-dependent integrated risk cost is introduced into the A* search framework, and the RP-A* algorithm is developed to achieve path planning that balances route efficiency and flight safety. Finally, Monte Carlo simulations driven by turbulence-based wind perturbation samples are conducted to estimate the empirical failure rate of planned paths. Results show that RP-A* significantly reduces path failure risk compared with baseline shortest-path methods while requiring only a limited increase in path length. The proposed framework provides an effective approach for safe UAV path planning in complex urban low-altitude environments.
Superpoint matching is a critical step in coarse-to-fine point cloud registration, and its performance directly affects the accuracy of subsequent point matching and pose estimation. However, most existing methods establish correspondences mainly relying on feature similarity, without explicit modeling of spatial structure, which easily leads to unstable matching in complex scenarios such as noise, occlusion, and low overlap. To address these issues, this paper proposes a coarse-to-fine point cloud registration method guided by prior correspondences. First, prior superpoint correspondences are constructed using rigid transformations estimated by existing SOTA methods, and are serially encoded via a prior encoding module to provide explicit constraints for feature learning. Furthermore, multiple geometric information including pairwise distances, angles, and normals is introduced and uniformly encoded to enhance spatial struc-ture representation. On this basis, a prior-guided sparse mixture-of-experts attention mechanism is designed to differentially model features in overlapping and non-overlapping regions, thereby improving feature discriminability and structural consistency. Using the learned features, the model gradually establishes correspondences through superpoint matching and point matching, and estimates the final rigid transformation with RANSAC. Experiments on the 3DMatch dataset show that when sampling 1000 point correspondences, the proposed method achieves an inlier ratio of 80.7% and a registration recall of 92.9%, which are 5.5% and 1.1% higher than the baseline method respectively, verifying the effectiveness of the proposed method in terms of accuracy and robustness.
Indoor localization in complex environments is challenged by signal instability, environmental occlusion, and cumulative motion-estimation errors. To address these issues, this study proposes a fusion localization method that combines Wi-Fi fingerprint trajectories and pedestrian dead reckoning (PDR) trajectories in a library scene. A dual-device collaborative acquisition scheme is adopted, in which an ESP32-C5 board collects Wi-Fi data and a smartphone collects inertial and attitude data. Weighted K-nearest neighbor (WKNN) is used for Wi-Fi fingerprint positioning, while PDR trajectory propagation is derived from linear acceleration and a roll-derived directional signal for stepwise propagation. On this basis, a 9-dimensional augmented multi-step extended Kalman filter is constructed to fuse Wi-Fi and PDR information. Experiments were conducted in two walking scenarios, namely a U-shaped trajectory and an L-shaped trajectory, within the same library environment. Results show that the proposed fusion method consistently improves overall trajectory fitting and demonstrates good applicability under different trajectory geometries. Although the improvement in local point-to-point errors remains scenario-dependent, the proposed framework effectively combines Wi-Fi absolute position constraints with continuous PDR motion information, thereby improving trajectory localization performance in complex indoor environments.
Building Information Modelling (BIM) is increasingly mandated worldwide as part of the construction industry's digital transformation. While widely used in design and construction, its potential for managing construction and demolition waste (C&DW) remains underexplored, despite demolition accounting for 70–90% of building-related waste and 30–40% of global solid waste. BIM Revit native models provide rich data but are computationally intensive and require specialist expertise, limiting their direct use for waste quantification and sustainability evaluation. This paper develops a BIM-enabled data integration and visualization framework that automates waste estimation, material classification, and environmental evaluation by linking BIM data with heterogeneous datasets through Speckle connectors and Power BI dashboards. Supplementary datasets included material densities, expansion coefficients, recycling rates, and environmental factors such as CO2 emissions and energy intensities. A case study of York University’s Bergeron Centre illustrates the framework’s effectiveness across three demolition stages. The non-invasive dismantling phase highlighted significant opportunities for material recovery, while semi-invasive deconstruction captured recyclable structural components with moderate landfill requirements. The final core demolition stage revealed the greatest potential for recycling, particularly in concrete and steel, though it also underscored the challenges of diverting large volumes of residual waste from disposal. By integrating BIM with environmental datasets and interactive dashboards, the system delivered holistic insights into recovery, landfill diversion, and CO2 reduction. Findings confirm its scalability, accessibility, and value as a decision-support tool for sustainable demolition and circular economy objectives.
ASAM OpenDRIVE is widely used as a standardized geometric road network description format supporting simulation-based development of advanced driver assistance and autonomous driving systems. While the standard primarily focuses on static road geometry and topology, the increasing need for realistic simulation inputs highlights the importance of incorporating empirically observed pavement conditions into digital road models. This paper proposes a conceptual framework for integrating automatically detected pavement defects into OpenDRIVE without introducing any schema extensions. Road surface anomalies identified from smartphone-based image acquisition using deep neural networks (DNN) are represented as JSON-based intermediate data and transformed by a dedicated mapping module into OpenDRIVE-compliant structures. The approach formalizes the geometric transformation from WGS84 coordinates to the OpenDRIVE curvilinear reference system and encodes defects either as discrete object-level entities or as lane-level material modifications along the road reference line. The main contribution lies in demonstrating that OpenDRIVE, although originally conceived as a geometric exchange format, inherently supports condition-aware road modeling when its existing hierarchical elements are used consistently. The proposed integration pathway establishes a structured link between perception-level data acquisition and simulation-compatible digital road descriptions, thereby contributing to more realistic and context-aware virtual testing environments.
Simulation-based testing is essential for autonomous vehicle development and depends on high-fidelity digital road network models. Although prior research focuses on geometric accuracy and semantic completeness of HD maps, topology remains underexplored. Topological inconsistencies, such as disconnected lane segments, invalid predecessor-successor links, or incomplete junction definitions, can invalidate simulations by preventing realistic vehicle navigation and traffic flow. This study presents a formal topological analysis framework for OpenDRIVE models, representing road networks as directed graphs and applying graph-based methods to analyze connectivity and verify consistency. We define connectivity relationships at three hierarchical levels: road-level predecessor-successor relations, lane-level adjacency, and junction-level merging-diverging structures. The framework introduces explicit consistency predicates for topological validity and enables algorithmic verification through adjacency matrix analysis, reachability computation, and connected component decomposition. A Python-based verification system processes OpenDRIVE XML files and evaluates consistency predicates across the network hierarchy. The approach is validated on four datasets: two synthetic scenarios and two real-world maps from Budapest and Karlsruhe. Results reveal widespread topological defects, with error-to-road ratios between 2.68 and 3.94. Lane-level connectivity violations account for 56-61% of errors in production datasets, while the professionally generated Karlsruhe dataset fragments into 467 isolated components despite containing 886 lanes and centimeter-level geometric accuracy. These findings show that topological verification is essential for HD map quality assurance revealing critical structural defects invisible to conventional geometric validation and simulation-based autonomous vehicle testing.
This practice paper presents the design, implementation, and first evaluation of Team-Based Learning (TBL) activities in university-level Geomatics courses taught in English to multicultural and international student groups. The study documents a structured pathway for adapting TBL to technically demanding subjects, including GIS suitability analysis, network analysis, remote sensing classification, and heat-risk assessment. Its main contribution lies in showing how a pedagogical model widely discussed in general higher education can be translated into software-based Geomatics teaching while supporting both disciplinary learning and intercultural collaboration. The paper also identifies the main organizational conditions for successful adoption, including team formation, workload calibration, and suitable classroom settings. Results from 12 TBL implementations involving 187 students and 470 total participations show clear benefits of teamwork: average team test performance was markedly higher than individual performance, repeated participation was associated with improved results, and student satisfaction increased after the introduction of TBL. Qualitative evidence further indicates gains in communication, teamwork, and intercultural interaction. Although the first implementation required substantial preparation effort, the approach proved replicable and scalable in subsequent editions, making TBL an effective instructional model for Geomatics education.
Carrier phase observations enable millimeter-level GNSS positioning, but their continuity is frequently disrupted by signal blockages and cycle slips. This limitation is particularly critical for low-cost and smartphone receivers, where weak antennas, urban multipath, and duty cycling cause frequent phase gaps that prevent reliable ambiguity resolution. Before addressing the full complexity of mass-market observations, the prediction methodology must be validated under controlled conditions. In this work we investigate whether machine learning, supported by precise satellite orbits and clocks, can predict carrier phase observations during signal gaps with millimeter-level accuracy. Twenty-four hours of Galileo data from the TORI permanent station (SPIN3 network, Torino, Italy) are processed at 30 s sampling using GFZ final SP3 and CLK products. After forming the ionosphere-free combination, an iterative carrier-phase based estimator removes the receiver clock, tropospheric delay, and ambiguity, reducing the residuals to a median standard deviation of 60 mm. Synthetic gaps from 60 s to 1800 s are introduced (1045 gaps total) and four prediction strategies are compared: polynomial fitting (degrees 3 and 5), Fourier-augmented polynomial, Gradient Boosting Regression with satellite geometry features, and Gaussian Process Regression. The Gradient Boosting model achieves the best overall performance, reaching 4.4 mm RMS for 60 s gaps, 9.4 mm for 5 min gaps, and 21 mm for 30 min gaps, well below the half-wavelength threshold required for cycle slip repair. These results demonstrate that geometry-aware gap prediction is feasible at the sub-wavelength level, providing a validated foundation for extending the approach to low-cost and smartphone GNSS receivers.
The low-altitude economy has emerged as a strategic emerging industry in China, involving economic activities within airspace below 1,000 meters, such as manned/unmanned cargo/passenger transport. This three-dimensional economic form faces core challenges in ensuring flight safety, optimizing operational efficiency, and achieving large-scale commercialization, primarily due to the complexity of low-altitude environments characterized by dense urban structures, dynamic meteorological factors, and mixed aircraft operations. This demands innovative digital management solutions. A low-altitude three-dimensional data space, defined as a digitally integrated environment for organizing, managing, and applying multi-source heterogeneous data within this airspace, serves as a critical foundation for supporting safe, efficient, and intelligent development. This study proposes a comprehensive low-altitude three-dimensional data space framework grounded in China's National 3D Realistic Geospatial Landscape Model (3dRGLM). The framework establishes a spatiotemporally integrated digital environment supporting critical applications in low-altitude airspace planning, intelligent 3D navigation, and safety-guaranteed airspace management. It is structured around entity-based digital modeling of low-altitude elements, multi-source spatiotemporal data fusion via grid-discretized management, and trusted data circulation mechanisms. By leveraging 3dRGLM's capabilities in realistic 3D representation and dynamic geographic entity association, the proposed data space enables end-to-end digital twin modeling of low-altitude operational scenarios. Pilot programs in Wuhu and Deqing demonstrate the framework's effectiveness in enhancing decision-making precision for air route network design, 3D dynamic navigation, and risk-aware flight control, confirming its value as a replicable model for scalable low-altitude economy development.
This case study details the integration of official large-scale open 2D and 3D geospatial data of the city of Berlin, Germany, into the Virtual Battlespace 4 (VBS4) simulator for security applications. Realistic scenery with elements specific to the target area is obtained from a digital terrain model, true-ortho mosaic, and high-resolution land use/land cover layer rasterized from OpenStreetMap vector primitives. For the central Mitte borough with its government institutions and foreign embassies, almost 20000 buildings are prepared from textured CityGML data in an automatic multi-stage process. This process involves pre-wrapping the texture images, which are referenced by the semantic 3D models using non-canonical coordinates, and the rapid creation of compact atlases to reduce the bitmap count by three orders of magnitude. To ensure that the building meshes blend seamlessly into the terrain, vertical adjustment methods are discussed, and ground extrusion is implemented to approach the model’s base surfaces from below. Data import into VBS4 happens through its Geo interface for the terrain, ortho, and land cover, while the buildings are compiled into an add-on with a custom workflow that involves reprojection, collision component setup, and damage behavior configuration. During interactive convoy training in the virtual environment, a high recognition value compared to the real landscape could be attested visually. Simulation exhibited acceptable frame rates, but required considerable computing resources.
This paper presents a comprehensive toolkit for semi-automated High-Definition Maps (HD Maps) production that integrates Artificial Intelligence (AI)-driven feature extraction with 3D human-in-the-loop validation. High-definition maps provide centimeter-level road geometry and traffic asset information, but large-scale production remains costly due to dense mobile mapping data and manual digitization. The proposed workflow consists of two self-developed components: a Semi-automated HD Maps Production Tool for batch extraction and a 3D HD Maps Validation and Editing Tool for structured review. The project-based pipeline ingests georeferenced mobile laser scanning point clouds, Inertial Navigation System / Global Navigation Satellite System (INS/GNSS) trajectories, and camera imagery, and applies configurable chains of ground filtering, road-marking extraction, voxel down-sampling, clustering, oriented bounding box analysis, and AI-based traffic asset detection. Candidate features with confidence indicators and basic attributes are stored in a project database and edited in a tightly coupled 3D environment that supports snapping, constrained adjustments, and semantic reclassification while logging all user edits. The toolkit is evaluated on a closed proving ground (CARLab, Shalun) and a freeway section of Taiwan National Highway No. 3. At CARLab, semi-automated extraction achieves F1-scores of 0.85–0.95 for key layers. For a one-kilometer highway section, operator time is reduced from 90–120 minutes in a purely manual Geographic Information System (GIS) workflow to about 45 minutes with the proposed approach, while maintaining comparable geometric accuracy. These results demonstrate a practical path towards scalable, traceable HD Maps production for autonomous driving applications.
In recent years, the increasing frequency of natural disasters in remote and rugged areas has underscored the importance of unmanned aerial vehicles (UAVs) for rapid emergency response. This paper presents a novel approach for optimizing the placement of UAV docking stations in mountainous terrain for emergency operations. We develop a comprehensive, 3D Geodata framework that integrates 3D Digital Elevation Models (3D DEM), building infrastructure, and road network data to create a realistic three-dimensional optimization environment. The proposed system employs an Enhanced Adaptive Particle Swarm Optimization (EAPSO) algorithm with adaptive parameters, diversity maintenance mechanisms, and intelligent convergence detection to effectively handle the complex constraints of mountainous environments. Experimental results demonstrate that our 3D-aware EAPSO approach achieves superior performance in balancing coverage efficiency, energy consumption, and network connectivity compared to conventional optimization methods. The proposed system provides a scientific foundation for improving emergency response capabilities in challenging geographical environments.
The Low-Altitude Economy (LAE), driven by the widespread deployment of UAVs and eVTOL aircraft, demands a high-fidelity Digital Twin that extends far beyond static geographic representation. This study presents a critical review of 39 peer-reviewed papers to propose a three-layer mapping framework — Geospatial Infrastructure Layer, Environmental Sensing Layer, and Interaction Layer — and evaluates the Technology Readiness Level (TRL) of each sub-domain. The Geospatial Infrastructure Layer encompasses terrain models, ground facilities, airspace structures, and semantic navigation landmarks. The Environmental Sensing Layer covers electromagnetic modeling, target sensing and countermeasure, and micro-meteorological mapping. The Interaction Layer addresses network trust, data security, swarm coordination, and platform reliability. Our TRL assessment reveals that Environmental Sensing is the most mature layer (mean TRL 4.4, 8 field-validated papers), while cross-layer integration remains the weakest link (mean TRL 3.5, zero field-validated demonstrations). We identify standardization of low-altitude spatial data products, AI-enabled predictive mapping, crowdsourced Digital Twin updating, and closed-loop cross-layer integration as the four priority research directions.
Urban digital twins are virtual city replicas that can greatly support urban planning by simulating infrastructure and mobility scenarios. However, keeping a digital twin up-to-date with fine-grained, real-world urban conditions is challenging. This paper proposes a novel system that leverages multi-modal AI models to bridge the gap between physical urban data collection and a 3D city digital twin. In proposed approach, ordinary smartphones carried in vehicles act as mobile sensors, continuously capturing multimodal data (road images, GPS coordinates, and speed). Advanced vision-language models then analyze the data to automatically extract information from the traffic and road infrastructure and detect road anomalies. The extracted information such as the locations of traffic signs, traffic signals, road surface, and potential blind spots at intersections is geo-tagged and streamed into vision-language models to interpret data and stream human readable insights into the digital twin model. The case study is the digital twin of the city of Toronto. By aggregating data from many drivers and analyzing it (in post-processing for high accuracy), the digital twin evolves into a living model of the built environment. This enriched and dynamic twin provides urban planners with up-to-date insights on traffic signage, road conditions, and other relevant road infrastructure elements, enabling proactive maintenance and informed decision-making for city planning.
The increasing frequency and magnitude of landslides necessitates a fundamental shift from reactive mitigation to proactive, predictive risk governance. To define the necessary tools for this transition, this study conducts a systematic literature review and operational analysis of current Digital Twin (DT) implementations in the geosciences. Through this review, we identify four primary target user groups (emergency responders, technical experts, public administrators, and citizens) and map their specific 4D data requirements and interaction logics. Our findings highlight that most existing systems function as "Digital Shadows" characterised by unidirectional data flows and a topography gap, where dynamic sensor data is superimposed onto static, outdated 3D meshes. Based on these requirements, we propose a theoretical layered architectural framework for a Data Hub designed to bridge these gaps. The conceptual architecture is structured into three interconnected tiers: an Acquisition Layer for multi-scale data ingestion; a Modelling and Processing Layer for AI and physics-based stability assessment; and an Application and Service Layer for translating complex data into actionable intelligence. Finally, this work investigates a possible implementation path for landslides DT projects by outlining technical recommendations. This includes the adoption of cloud-native formats (e.g., Cloud Optimized GeoTIFF, Zarr) and unified interoperability standards (e.g., OGC SensorThings API) to evaluate the feasibility of transitioning towards a true bi-directional cyber-physical system for landslide risk management.
The paper presents the objectives and initial outcomes of the TNE-GPSEducation project, “Green & Pink for Sustainable Education”, funded by the Italian PNRR and coordinated by the University of Foggia, involving ten Italian universities including Politecnico di Milano (POLIMI). The project promotes multidisciplinary international cooperation with partners across Africa, Asia, the Middle East, and South America. Within this framework, POLIMI contributes through the Advanced Skills programme, aimed at developing a transdisciplinary capacity-building model integrating sustainability, health, and gender equity. This model is implemented through modular Lifelong Learning Courses (LLLCs) and the establishment of an EO Hub designed to support long-term institutional collaboration and knowledge transfer. Five LLLCs have been developed, including two fully dedicated to EO. These follow a structured workflow that progresses from global EO data analysis—based on rGEE/GEE time series—to site-specific applications within the LULUCF framework, with a focus on land degradation processes in Mediterranean regions. The other three LLLCs on XR, ecosystem services, decarbonization, and Nature-Based Solutions strengthened the transdisciplinary approach, linking environmental monitoring with mitigation and adaptation strategies. Preliminary results demonstrate the effectiveness of this model in fostering shared methodologies and identifying thematic pathways for future collaboration. Among these, soil erosion emerges as a priority domain, offering a concrete entry point for joint research activities. Future developments aim to scale up the EO Hub across additional African countries, consolidating the role of ENSMR and UIR as regional nodes for EO-based capacity building and sustainable development.
5*S is a STEM outreach programme designed to “attract those not normally engaged with STEM”, by introducing spatial data into the secondary school curriculum. The objective was to move beyond the traditional ‘school visit’ model where a speaker might present samples of their work and instead develop teacher-focussed training and student-focussed content with clear value, encouraging adoption by teachers in their lesson plans. To enthuse the next generation, a team of experts in the geospatial and education fields collaborated to visit schools and inform them to consider a STEM career by bringing interactive content and sharing their own experiences in the classroom. In evaluation of feedback received from over 75 teachers, and workshops reaching almost 19,000 students in 22 counties, teachers expressed that students were more engaged following the workshops and recognised the positive benefits of exposing students to the content. The value of STEM careers, and the role of NMAs was also evident. From a teacher’s perspective there was an increase in their own confidence when working with spatial data and a recognition of the potential to enable students to be in charge of their own learning. The value of curriculum-focussed content proposed in this paper was validated, with 98% of teachers indicating that they would use 5*S content again in their teaching and 100% confirmed that they would recommend GIS/spatial data and 5*S to a colleague. This research shows that space, satellites and STEM are relevant to many more than just the traditional subjects.
