[ { "id": "grammatikaki-2026-rootscape", "type_id": "poster", "tu_id": null, "repositum_id": "20.500.12708/229436", "title": "RootScape: Visual Analytics for Subsurface-Aware Urban Tree Planning", "date": "2026-04-27", "abstract": "We present RootScape, a browser-based visual analytics system fusing eight open geospatial datasets for Vienna into a depth-coherent 3D scene from tree crown to tunnel. Four coordinated views support the full planning workflow: a 3D Mapping environment with user-controlled temporal simulation of root-halo and crown growth, a Cross-Section view revealing subsurface geometry at true depth, a Viability Planner scoring eight species against ten substrate constraints with per-factor breakdown and conflict-year prediction, and a District Analytics view for strategic stress assessment. Retrospective validation against the Vienna Baumkataster surfaces 81 unexplained high-stress trees in our study area as inspection candidates and produces zero false high-risk alarms. RootScape is the first system to expose the invisible subsurface conflict between urban greenery and gray infrastructure in an interactive, open-data, planning-oriented context. The codebase and demo are publicly available at https://github.com/AngelikiGram/rootscape.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 4700, "image_height": 2198, "name": "grammatikaki-2026-rootscape-teaser.png", "type": "image/png", "size": 7859242, "path": "Publication:grammatikaki-2026-rootscape", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/grammatikaki-2026-rootscape-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/grammatikaki-2026-rootscape-teaser:thumb{{size}}.png" }, "sync_repositum_override": "authors,date", "repositum_presentation_id": null, "authors": [ 1937, 5485, 1110 ], "booktitle": "EuroVis 2026 : 28th Eurographics Conference on Visualization 2026 : Posters", "cfp": { "name": "call_for_papers_eurovis_posters.pdf", "type": "application/pdf", "error": 0, "size": 1161902, "orig_name": "call_for_papers_eurovis_posters.pdf", "ext": "pdf" }, "date_from": "2026-06-08", "date_to": "2026-06-12", "doi": "10.2312/evpd.20261025", "editor": "Elshealhy, Mai and Furmanova, Katarina and Garrison, Laura and Médoc, Nicolas", "event": "28th Eurographics Conference on Visualization (EuroVis 2026)", "isbn": "978-3-03868-305-6", "lecturer": [ 1937 ], "location": "Nottingham", "open_access": "yes", "pages": "3", "publisher": "The Eurographics Association", "research_areas": [], "keywords": [ "Visual analytics", "Geographic visualization", "Rendering" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "grammatikaki-2026-rootscape-paper.pdf", "type": "application/pdf", "size": 2947037, "path": "Publication:grammatikaki-2026-rootscape", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/grammatikaki-2026-rootscape-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/grammatikaki-2026-rootscape-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "poster", "main_file": false, "use_in_gallery": false, "access": "public", "name": "grammatikaki-2026-rootscape-poster.pdf", "type": "application/pdf", "size": 7624224, "path": "Publication:grammatikaki-2026-rootscape", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/grammatikaki-2026-rootscape-poster.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/grammatikaki-2026-rootscape-poster:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 4700, "image_height": 2198, "name": "grammatikaki-2026-rootscape-teaser.png", "type": "image/png", "size": 7859242, "path": "Publication:grammatikaki-2026-rootscape", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/grammatikaki-2026-rootscape-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/grammatikaki-2026-rootscape-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "d10164" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-rootscape/", "__class": "Publication" }, { "id": "grammatikaki-2026-treesformer", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/229409", "title": "TreesFormer: Multimodal Grammar-Based 3D Tree Reconstruction from Sparse Geodata", "date": "2026-04-24", "abstract": "We present TreesFormer, the first grammar-based framework for reconstructing hierarchical 3D tree structures directly from sparse top-down geodata using only a single orthophoto and its corresponding Digital Surface Model (DSM). It employs a multi-modal autoregressive transformer that predicts compact parametric L-system grammars from DSM point clouds and orthophoto features, jointly predicting symbolic structure and geometric parameters while enforcing grammar constraints during decoding. To enable supervision in the absence of real-world grammar annotations, we introduce a synthetic multimodal dataset of procedurally generated trees with aligned aerial inputs and ground-truth L-system labels. Experiments show that DSMs drive overall geometric accuracy and crown shape, while orthophoto conditioning improves structural regularity and branching depth; their combination consistently outperforms either modality alone. The model generalizes to real-world Austrian and French aerial data, producing interpretable branching structures suitable for large-scale rural 3D mapping. The codebase, synthetic dataset, and pretrained model are publicly available at https://angelikigram.github.io/treesformer/.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2800, "image_height": 1042, "name": "grammatikaki-2026-treesformer-teaser.png", "type": "image/png", "size": 1993656, "path": "Publication:grammatikaki-2026-treesformer", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treesformer/grammatikaki-2026-treesformer-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treesformer/grammatikaki-2026-treesformer-teaser:thumb{{size}}.png" }, "sync_repositum_override": "date,pages_from,pages_to", "repositum_presentation_id": null, "authors": [ 1937, 5485, 1110 ], "booktitle": "Computer Graphics & Visual Computing (CGVC) 2026", "date_from": "2026-06-08", "date_to": "2026-06-12", "doi": "10.2312/cgvc.20261008", "editor": "Sheng, Yun and Elshehaly, Mai", "event": "28th Eurographics Conference on Visualization (EuroVis 2026)", "isbn": "978-3-03868-319-3", "lecturer": [ 1937 ], "location": "Nottingham", "open_access": "yes", "pages": "10", "pages_from": "1", "pages_to": "10", "publisher": "The Eurographics Association", "research_areas": [], "keywords": [ "Neural networks", "Mesh models", "Reconstruction" ], "weblinks": [], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "grammatikaki-2026-treesformer-paper.pdf", "type": "application/pdf", "size": 21512091, "path": "Publication:grammatikaki-2026-treesformer", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treesformer/grammatikaki-2026-treesformer-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treesformer/grammatikaki-2026-treesformer-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2800, "image_height": 1042, "name": "grammatikaki-2026-treesformer-teaser.png", "type": "image/png", "size": 1993656, "path": "Publication:grammatikaki-2026-treesformer", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treesformer/grammatikaki-2026-treesformer-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treesformer/grammatikaki-2026-treesformer-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "d10164", "VRVis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treesformer/", "__class": "Publication" }, { "id": "grammatikaki-2026-treeon", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/228171", "title": "TreeON: Reconstructing 3D Tree Point Clouds from Orthophotos and Heightmaps", "date": "2026", "abstract": "We present TreeON, a novel neural-based framework for reconstructing detailed 3D tree point clouds from sparse top-down geodata, using only a single orthophoto and its corresponding Digital Surface Model (DSM). Our method introduces a new training supervision strategy that combines both geometric supervision and a differentiable shadow and silhouette loss to learn point cloud representations of trees without requiring species labels, procedural rules, detailed terrestrial reconstruction data, or ground laser scan data. To address the lack of ground truth data, we generate a synthetic dataset of point clouds from procedurally modeled trees and train our network on it. Quantitative and qualitative experiments demonstrate better reconstruction quality and coverage compared to existing methods, as well as strong generalization to real-world data, leading to visually appealing and structurally plausible tree point cloud representations that can be integrated into interactive digital 3D maps. The codebase, synthetic dataset, and pretrained model are publicly available at https://angelikigram.github.io/treeON/.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1698, "image_height": 496, "name": "grammatikaki-2026-treeon-teaser.png", "type": "image/png", "size": 1175153, "path": "Publication:grammatikaki-2026-treeon", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treeon/grammatikaki-2026-treeon-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treeon/grammatikaki-2026-treeon-teaser:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1937, 1653, 5415, 5462, 1110 ], "articleno": "e70366", "doi": "10.1111/cgf.70366", "issn": "1467-8659", "journal": "Computer Graphics Forum", "open_access": "yes", "pages": "14", "publisher": "WILEY", "research_areas": [], "keywords": [ "Computing methodologies", "Point-based models", "Reconstruction", "Neural Networks" ], "weblinks": [ { "href": "https://angelikigram.github.io/treeON/", "caption": null, "description": null, "main_file": 0 } ], "files": [ { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "grammatikaki-2026-treeon-paper.pdf", "type": "application/pdf", "size": 70465234, "path": "Publication:grammatikaki-2026-treeon", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treeon/grammatikaki-2026-treeon-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treeon/grammatikaki-2026-treeon-paper:thumb{{size}}.png" }, { "description": null, "filetitle": "teaser", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1698, "image_height": 496, "name": "grammatikaki-2026-treeon-teaser.png", "type": "image/png", "size": 1175153, "path": "Publication:grammatikaki-2026-treeon", "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treeon/grammatikaki-2026-treeon-teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treeon/grammatikaki-2026-treeon-teaser:thumb{{size}}.png" } ], "projects_workgroups": [ "d10164", "d9275" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2026/grammatikaki-2026-treeon/", "__class": "Publication" }, { "id": "grammatikaki-2025-htr", "type_id": "journalpaper_notalk", "tu_id": null, "repositum_id": "20.500.12708/215590", "title": "How to represent landmark trees in digital 3D maps? An automated workflow and user study", "date": "2025-05", "abstract": "Digital 3D maps created from digital elevation models (DEMs) cannot properly capture trees due to the 2.5D nature of the DEMs. Leveraging publicly available DEMs and orthophotos as the only input data sources, we present a fully automatic pipeline that models landmark trees. We conducted two crowdsourced user studies to evaluate visual appeal and scene recognizability using two different levels of detail of tree representations generated using our pipeline. Users found highly detailed trees much more appealing, and also easier and more trustworthy to recognize, with open-ended responses revealing key factors like realism, coherence, and tree shape influencing their preferences. However, the ability to recognize a location seems to depend more on the surrounding environment than the representation of the landmark tree in focus. We discuss the implications of these results for digital 3D outdoor maps.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2200, "image_height": 370, "name": "grammatikaki-2025-htr-image.jpg", "type": "image/jpeg", "size": 181956, "path": "Publication:grammatikaki-2025-htr", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/grammatikaki-2025-htr/grammatikaki-2025-htr-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/grammatikaki-2025-htr/grammatikaki-2025-htr-image:thumb{{size}}.png" }, "sync_repositum_override": "date,projects,pages_from,pages_to,volume", "repositum_presentation_id": null, "authors": [ 1937, 1653, 5463, 5462, 1110 ], "doi": "10.1080/15230406.2025.2489543", "issn": "1545-0465", "journal": "Cartography and Geographic Information Science", "open_access": "yes", "pages": "18", "pages_from": "1", "pages_to": "18", "publisher": "TAYLOR & FRANCIS INC", "volume": "Number 0", "research_areas": [], "keywords": [ "Digital 3D maps", "landmarks", "trees", "user study", "modeling", "rendering" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 2200, "image_height": 370, "name": "grammatikaki-2025-htr-image.jpg", "type": "image/jpeg", "size": 181956, "path": "Publication:grammatikaki-2025-htr", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/grammatikaki-2025-htr/grammatikaki-2025-htr-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/grammatikaki-2025-htr/grammatikaki-2025-htr-image:thumb{{size}}.png" } ], "projects_workgroups": [ "vis" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/grammatikaki-2025-htr/", "__class": "Publication" }, { "id": "xmas-2024", "type_id": "xmascard", "tu_id": null, "repositum_id": null, "title": "X-Mas Card 2024", "date": "2024-12", "abstract": null, "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1863, "image_height": 1318, "name": "xmas-2024-.PNG", "type": "image/png", "size": 6124629, "path": "Publication:xmas-2024", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/xmas-2024/xmas-2024-.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/xmas-2024/xmas-2024-:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 1937 ], "research_areas": [ "Modeling" ], "keywords": [ "Christmas", "Digital Elevation Models", "L-system-based modeling" ], "weblinks": [], "files": [ { "description": null, "filetitle": "", "main_file": false, "use_in_gallery": false, "access": "public", "image_width": 1863, "image_height": 1318, "name": "xmas-2024-.PNG", "type": "image/png", "size": 6124629, "path": "Publication:xmas-2024", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/xmas-2024/xmas-2024-.PNG", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/xmas-2024/xmas-2024-:thumb{{size}}.png" }, { "description": "Christmas Card as PDF", "filetitle": "Card", "main_file": true, "use_in_gallery": false, "access": "public", "name": "xmas-2024-Card.pdf", "type": "application/pdf", "size": 3113633, "path": "Publication:xmas-2024", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/xmas-2024/xmas-2024-Card.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/xmas-2024/xmas-2024-Card:thumb{{size}}.png" } ], "projects_workgroups": [ "xmas" ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/xmas-2024/", "__class": "Publication" }, { "id": "tanaka-2024-vor", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/209946", "title": "Visualization of Relationships between Precipitation and River Water Levels", "date": "2024", "abstract": "Observation of precipitation changes is important for a variety of purposes such as predicting river levels. Previous studies for data visualization of precipitation and river water levels plotted graphs and color bars were many stations on a map. Instead of such visualizations on a map, we construct a graph to imitate a connected structure such as a tributary of a river in this study. Our method displays two pseudo-coloring sparklines at nodes of the graph as the stations. The method can visualize the time difference between the increase in precipitation upstream and the increase in river water level downstream. Users can observe precipitation and river water levels at different observation points. Our method uses a Delaunay diagram connecting gauging positions to interpolate and calculate precipitation at river level observation points. This avoids the discrepancy between observation points.In addition, we adjust the amount of visualized information by skipping the display of several observation points based on the similarity of the time-series data at each station, which is calculated by applying the dynamic time-stretching method. The visualization results show that downstream, once the water level rises, it tends to take longer for the water level to drop. In addition, the results show that a time lag occurs between the increase in precipitation and the rise in river levels in the mainstream, while tributaries have little time lag. In addition, data on rainfall and river levels at the same station over multiple periods and their relationship are plotted as scatter plots. The scatter plots make it easier to compare data from multiple periods at the same time than two-tone pseudo coloring sparklines.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ 5438, 1937, 1850, 1410, 166, 1754 ], "booktitle": "2024 28th International Conference Information Visualisation (IV)", "date_from": "2024-07-22", "date_to": "2024-07-26", "doi": "10.1109/IV64223.2024.00020", "event": "2024 28th International Conference Information Visualisation (IV)", "isbn": "979-8-3503-8016-3", "lecturer": [ 5438 ], "location": "Coimbra", "pages": "6", "pages_from": "58", "pages_to": "63", "publisher": "IEEE", "research_areas": [], "keywords": [ "Geographic Information", "Meteorological Information", "River Water level", "Interpolation", "Precipitation", "Rain", "Image color analysis", "Data visualization", "Rivers", "Space stations", "Bars" ], "weblinks": [], "files": [], "projects_workgroups": [], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/tanaka-2024-vor/", "__class": "Publication" } ]