Conference Agenda

This study compares visualization types (3D, 2D, Oblique 3D, and Combined 2D+3D, coupled in a pairwise fashion for different tasks and scenarios), investigates their influence on decision-making across selected urban planning tasks (Site selection, Scenario Selection), and Distance Estimation as a baseline task that we assumed is relevant in both. Our goal was to inform the participatory urban planning process. In a controlled user study with 40 participants, we evaluated whether visualization type affects decision outcomes and distance estimation, complemented by participants’ visualization preferences before and after the experiment. The results confirm the previously well documented evidence that participants are considerably more successful with distance estimation with 2D visualizations, and their decisions vary depending on the examined visualization and task types. We observe that different formats support different task requirements, i.e., each visualization type exhibits distinct strengths depending on the task. These findings indicate that visualization choice in urban planning should be adapted to the task and context rather than treated as an interchangeable artifact.

With the rising number and severity of WUI wildfire episodes and the necessity to improve community preparedness, planning strategies have to be devised that integrate foresight into wildfires, with active community participation. This paper presents an intelligent collaborative environment that seeks to engage citizens, planners, and emergency services in co-creation of fire-resilient strategies through Agentic AI-driven wildfire simulations and mixed-reality visualization. A serious game environment is designed for hands-on exploration of alternative wildfire spread scenarios and community-scale prevention practices such as prescribed burning, fuel treatment through vegetation control, and structural hardening measures. The objective is to promote public awareness and adaptive behavior as well as provide science-based operational decision support to emergency responders in evaluating tactical options specific to terrain, infrastructure, and fuel conditions of the locale. The system operates on a Large Language Model (LLM)–powered agentic AI architecture designed to automate and orchestrate 2D and 3D wildfire simulations, providing guidance that supports users from diverse technical backgrounds. To give the results of the simulations, 3D web visualization and immersive holographic display were used to enable cycles of iterated explorations into fire spread in dense urban settings. With AI-assisted wildfire intelligence, this particular flow works through a set of intuitive interaction mechanics so that communities can evaluate risk levels, weigh alternatives for mitigation, and better prepare for an actual fire event.

Accurate spatial alignment is a key requirement for situated Augmented Reality (AR) in urban planning, where citizens and planners can visualize proposed designs in real outdoor environments. However, existing AR localization approaches often rely on smartphone GNSS, vendor-specific cloud anchors, or cloud-based visual positioning, which introduce accuracy limitations, privacy concerns, or dependencies that restrict their use in participatory planning workflows. This paper presents a privacy-aware on-device localization pipeline for outdoor urban planning scenarios. The approach aligns LiDAR scans captured on smartphones with pre-scanned reference point cloud tiles to enable stable and accurate placement of urban planning models. Approximate GNSS is used only to retrieve a relevant reference tile, while all preprocessing and registration steps are performed locally on the device. The pipeline combines voxel downsampling, local geometric descriptors, and global registration to estimate alignment without relying on GNSS for pose estimation or on cloud-based visual localization services. A mobile demonstrator was developed to support situated AR in urban planning scenarios, allowing users to explore design proposals directly in context. Initial validation under controlled conditions showed that the system can recover translations and rotations with errors on the order of a few centimeters, while processing times remained suitable for mobile use. The approach was also deployed in an urban planning case study and enabled stable outdoor visualization of planning elements on-site.

We present a new method for assessing visual walkability using 360° videos and an eye-tracking in Cinematic Virtual Reality (CVR). Visual walkability refers to the walkability perceived by pedestrians through visual stimuli in the urban environment. Our method uses semantic segmentation, viewport exposure, gaze measures, and a custom walkability questionnaire, enabling comparison between scene content, participant's viewport, and their gaze focus. The 10 videos used, including 2 calibration videos, exhibit distinct semantic characteristics, validated by segmentation analysis. Analysis of the 35 participants’ responses shows that walkability ratings at the video level correlate with several environmental parameters (e.g., road, sidewalk, sky) consistent with previous studies. However, these parameters do not have a similar influence in gaze-based visual attention analysis within the CVR setting, suggesting that CVR attention would requiere further work. Furthermore, our results suggest that unexpected semantic classes may also play a role in perceived walkability and should be considered exploratory pending further validation. This paves the way for further research on using CVR as an assessment tool for visual walkability and for developing methodological guidance on which visual cues are robust across measures (content/viewport).

Oceanographic data and their related simulation have a key role in addressing EU and UN societal challenges in marine environments. Visualising marine data is challenging for different visual-communication intents and audiences, despite existing guidelines on the subject. A main visual-design limitation for existing techniques is the co-visualization of multiple hydrodynamic field attributes in an accessible, comprehensible and engaging manner. This paper addresses this limitation in two ways: first, existing techniques for cartographic-oriented design of waterlines are adopted and extended towards multivariate hydrodynamic field datasets. Secondly, experimental results on the intermixing different visual-channel mapping of hydrodynamic attribute data are presented in a case study on ocean-flow patterns around the Hebrides island chain (UK). The results demonstrate a simultaneous co-visualization of up to five unique, independent scalar attributes in a comprehensible manner while preserving the geographic context. Moreover, best-practice guidelines are stated in conclusion of the experimental case study to help oceanographic practitioners adopt the presented technology in their professional workflows.

Artificial light at night (ALAN) degrades nocturnal ecosystems and complicates astronomical observation. Although all-sky imaging and GIS-based light-pollution mapping are well established in the analysis of light pollution, identifying local contributors to ALAN still requires time-consuming cross-comparisons, done in separate views, making light halo--source attribution slow and manual. We present an interactive system that addresses this gap by co-registering Sky Quality Camera all-sky imagery and OSM-derived candidate emitters (e.g., settlements, roads, aerodromes, industrial sites) in one observer-centered scene. The viewer is placed at the locations of the captured all-sky images in 3D digital terrain model-based scenes, realistically illuminated by the sky under selected conditions for an immersive view of nighttime scenarios. OpenStreetMap features are projected onto a surrounding sphere via inverse stereographic projection, with point markers and horizontal-extent indicators to support rapid visual matching between observed halos and plausible sources. Users can switch scenes and processed sky images, adjust projection parameters, and inspect scenes in VR or in an additional cylindrical projection for a panoramic desktop view. A companion web tool configures location classes and display ranges. The presented system primarily targets exploratory analysis, with its main contribution being the novel co-visualization of light sources and light halos; expert interviews positively validated this analytical focus. As a secondary outcome, the system's immersive first-person representation may also enrich educational communication and outreach on ALAN impacts.

Accurate and intuitive visualization of urban development projects is a persistent challenge in spatial planning and public participation. Recent advances in Extended Reality (XR) offer new opportunities to integrate geospatial data directly within the user’s real environment. This paper introduces GeoWebXR, an extension of the WebXR API designed to provide absolute georeferencing of the XR reference space via a standardized geopose. We present an outdoor proof-of-concept implementation that integrates a dual-antenna RTK GNSS receiver mounted on an XR headset. High-precision GNSS measurements are fused with the device’s local pose estimates to compute a consistent and accurate geopose, enabling decimeter-level alignment between virtual and physical environments.

Leveraging GeoWebXR, WebGL applications can render georeferenced 3D content in situ through a web browser. We demonstrate this capability using the iTowns geospatial visualization framework to deliver an XR experience for urban planning. The system supports both 1:1-scale in-situ visualization and reduced-scale overview modes, enabling seamless multiscale exploration of planning scenarios. To mitigate cognitive overload in dense urban contexts, we implement and evaluate several visualization and interaction strategies. We assess the usability and spatial appropriation enabled by our system, and discuss how it may support both expert analysis and citizen participation in urban planning processes.