@article{ScheibelTrappLimbergeretal.2020,
  author    = {Scheibel, Willy and Trapp, Matthias and Limberger, Daniel and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {A taxonomy of treemap visualization techniques},
  series = {Science and Technology Publications},
  journal   = {Science and Technology Publications},
  publisher = {Springer},
  address   = {Berlin},
  pages     = {8},
  year      = {2020},
  abstract  = {A treemap is a visualization that has been specifically designed to facilitate the exploration of tree-structured data and, more general, hierarchically structured data. The family of visualization techniques that use a visual metaphor for parent-child relationships based "on the property of containment" (Johnson, 1993) is commonly referred to as treemaps. However, as the number of variations of treemaps grows, it becomes increasingly important to distinguish clearly between techniques and their specific characteristics. This paper proposes to discern between Space-filling Treemap TS, Containment Treemap TC, Implicit Edge Representation Tree TIE, and Mapped Tree TMT for classification of hierarchy visualization techniques and highlights their respective properties. This taxonomy is created as a hyponymy, i.e., its classes have an is-a relationship to one another: TS TC TIE TMT. With this proposal, we intend to stimulate a discussion on a more unambiguous classification of treemaps and, furthermore, broaden what is understood by the concept of treemap itself.},
  language  = {en}
}
@article{Doellner2020,
  author    = {D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Geospatial artificial intelligence},
  series = {Journal of photogrammetry, remote sensing and geoinformation science : PFG : Photogrammetrie, Fernerkundung, Geoinformation},
  volume    = {88},
  journal   = {Journal of photogrammetry, remote sensing and geoinformation science : PFG : Photogrammetrie, Fernerkundung, Geoinformation},
  number    = {1},
  publisher = {Springer International Publishing},
  address   = {Cham},
  issn      = {2512-2789},
  doi       = {10.1007/s41064-020-00102-3},
  pages     = {15 -- 24},
  year      = {2020},
  abstract  = {Artificial intelligence (AI) is changing fundamentally the way how IT solutions are implemented and operated across all application domains, including the geospatial domain. This contribution outlines AI-based techniques for 3D point clouds and geospatial digital twins as generic components of geospatial AI. First, we briefly reflect on the term "AI" and outline technology developments needed to apply AI to IT solutions, seen from a software engineering perspective. Next, we characterize 3D point clouds as key category of geodata and their role for creating the basis for geospatial digital twins; we explain the feasibility of machine learning (ML) and deep learning (DL) approaches for 3D point clouds. In particular, we argue that 3D point clouds can be seen as a corpus with similar properties as natural language corpora and formulate a "Naturalness Hypothesis" for 3D point clouds. In the main part, we introduce a workflow for interpreting 3D point clouds based on ML/DL approaches that derive domain-specific and application-specific semantics for 3D point clouds without having to create explicit spatial 3D models or explicit rule sets. Finally, examples are shown how ML/DL enables us to efficiently build and maintain base data for geospatial digital twins such as virtual 3D city models, indoor models, or building information models.},
  language  = {en}
}
@article{IsailovićStojanovicTrappetal.2020,
  author    = {Isailović, Dušan and Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and Hajdin, Rade and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Bridge damage},
  series = {Automation in construction : an international research journal},
  volume    = {112},
  journal   = {Automation in construction : an international research journal},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0926-5805},
  doi       = {10.1016/j.autcon.2020.103088},
  pages     = {22},
  year      = {2020},
  abstract  = {Building Information Modeling (BIM) representations of bridges enriched by inspection data will add tremendous value to future Bridge Management Systems (BMSs). This paper presents an approach for point cloud-based detection of spalling damage, as well as integrating damage components into a BIM via semantic enrichment of an as-built Industry Foundation Classes (IFC) model. An approach for generating the as-built BIM, geometric reconstruction of detected damage point clusters and semantic-enrichment of the corresponding IFC model is presented. Multiview-classification is used and evaluated for the detection of spalling damage features. The semantic enrichment of as-built IFC models is based on injecting classified and reconstructed damage clusters back into the as-built IFC, thus generating an accurate as-is IFC model compliant to the BMS inspection requirements.},
  language  = {en}
}
@article{StojanovicTrappRichteretal.2019,
  author    = {Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Service-oriented semantic enrichment of indoor point clouds using octree-based multiview classification},
  series = {Graphical Models},
  volume    = {105},
  journal   = {Graphical Models},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {1524-0703},
  doi       = {10.1016/j.gmod.2019.101039},
  pages     = {18},
  year      = {2019},
  abstract  = {The use of Building Information Modeling (BIM) for Facility Management (FM) in the Operation and Maintenance (O\&M) stages of the building life-cycle is intended to bridge the gap between operations and digital data, but lacks the functionality of assessing the state of the built environment due to non-automated generation of associated semantics. 3D point clouds can be used to capture the physical state of the built environment, but also lack these associated semantics. A prototypical implementation of a service-oriented architecture for classification of indoor point cloud scenes of office environments is presented, using multiview classification. The multiview classification approach is tested using a retrained Convolutional Neural Network (CNN) model - Inception V3. The presented approach for classifying common office furniture objects (chairs, sofas and desks), contained in 3D point cloud scans, is tested and evaluated. The results show that the presented approach can classify common office furniture up to an acceptable degree of accuracy, and is suitable for quick and robust semantics approximation - based on RGB (red, green and blue color channel) cubemap images of the octree partitioned areas of the 3D point cloud scan. Additional methods for web-based 3D visualization, editing and annotation of point clouds are also discussed. Using the described approach, captured scans of indoor environments can be semantically enriched using object annotations derived from multiview classification results. Furthermore, the presented approach is suited for semantic enrichment of lower resolution indoor point clouds acquired using commodity mobile devices.},
  language  = {en}
}
@misc{TrappDoellner2019,
  author    = {Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Interactive Close-Up Rendering for Detail plus Overview Visualization of 3D Digital Terrain Models},
  series = {2019 23rd International Conference Information Visualisation (IV)},
  journal   = {2019 23rd International Conference Information Visualisation (IV)},
  editor    = {Banissi, E Ursyn},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  isbn      = {978-1-7281-2838-2},
  issn      = {2375-0138},
  doi       = {10.1109/IV.2019.00053},
  pages     = {275 -- 280},
  year      = {2019},
  abstract  = {This paper presents an interactive rendering technique for detail+overview visualization of 3D digital terrain models using interactive close-ups. A close-up is an alternative presentation of input data varying with respect to geometrical scale, mapping, appearance, as well as Level-of-Detail (LOD) and Level-of-Abstraction (LOA) used. The presented 3D close-up approach enables in-situ comparison of multiple Regionof-Interests (ROIs) simultaneously. We describe a GPU-based rendering technique for the image-synthesis of multiple close-ups in real-time.},
  language  = {en}
}
@article{ReimannKlingbeilPasewaldtetal.2019,
  author    = {Reimann, Max and Klingbeil, Mandy and Pasewaldt, Sebastian and Semmo, Amir and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Locally controllable neural style transfer on mobile devices},
  series = {The Visual Computer},
  volume    = {35},
  journal   = {The Visual Computer},
  number    = {11},
  publisher = {Springer},
  address   = {New York},
  issn      = {0178-2789},
  doi       = {10.1007/s00371-019-01654-1},
  pages     = {1531 -- 1547},
  year      = {2019},
  abstract  = {Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. In this work, we first propose a problem characterization of interactive style transfer representing a trade-off between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, we enhance state-of-the-art neural style transfer techniques by mask-based loss terms that can be interactively parameterized by a generalized user interface to facilitate a creative and localized editing process. We report on a usability study and an online survey that demonstrate the ability of our app to transfer styles at improved semantic plausibility.},
  language  = {en}
}
@misc{FlorioTrappDoellner2019,
  author    = {Florio, Alessandro and Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Semantic-driven Visualization Techniques for Interactive Exploration of 3D Indoor Models},
  series = {2019 23rd International Conference Information Visualisation (IV)},
  journal   = {2019 23rd International Conference Information Visualisation (IV)},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  isbn      = {978-1-7281-2838-2},
  issn      = {2375-0138},
  doi       = {10.1109/IV.2019.00014},
  pages     = {25 -- 30},
  year      = {2019},
  abstract  = {The availability of detailed virtual 3D building models including representations of indoor elements, allows for a wide number of applications requiring effective exploration and navigation functionality. Depending on the application context, users should be enabled to focus on specific Objects-of-Interests (OOIs) or important building elements. This requires approaches to filtering building parts as well as techniques to visualize important building objects and their relations. For it, this paper explores the application and combination of interactive rendering techniques as well as their semanticallydriven configuration in the context of 3D indoor models.},
  language  = {en}
}
@misc{TrappDoellner2019,
  author    = {Trapp, Matthias and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Real-time Screen-space Geometry Draping for 3D Digital Terrain Models},
  series = {2019 23rd International Conference Information Visualisation (IV)},
  journal   = {2019 23rd International Conference Information Visualisation (IV)},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Los Alamitos},
  isbn      = {978-1-7281-2838-2},
  issn      = {2375-0138},
  doi       = {10.1109/IV.2019.00054},
  pages     = {281 -- 286},
  year      = {2019},
  abstract  = {A fundamental task in 3D geovisualization and GIS applications is the visualization of vector data that can represent features such as transportation networks or land use coverage. Mapping or draping vector data represented by geometric primitives (e.g., polylines or polygons) to 3D digital elevation or 3D digital terrain models is a challenging task. We present an interactive GPU-based approach that performs geometry-based draping of vector data on per-frame basis using an image-based representation of a 3D digital elevation or terrain model only.},
  language  = {en}
}
@article{DischerRichterDoellner2019,
  author    = {Discher, S{\"o}ren and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Concepts and techniques for web-based visualization and processing of massive 3D point clouds with semantics},
  series = {Graphical Models},
  volume    = {104},
  journal   = {Graphical Models},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {1524-0703},
  doi       = {10.1016/j.gmod.2019.101036},
  pages     = {11},
  year      = {2019},
  abstract  = {3D point cloud technology facilitates the automated and highly detailed acquisition of real-world environments such as assets, sites, and countries. We present a web-based system for the interactive exploration and inspection of arbitrary large 3D point clouds. Our approach is able to render 3D point clouds with billions of points using spatial data structures and level-of-detail representations. Point-based rendering techniques and post-processing effects are provided to enable task-specific and data-specific filtering, e.g., based on semantics. A set of interaction techniques allows users to collaboratively work with the data (e.g., measuring distances and annotating). Additional value is provided by the system's ability to display additional, context-providing geodata alongside 3D point clouds and to integrate processing and analysis operations. We have evaluated the presented techniques and in case studies and with different data sets from aerial, mobile, and terrestrial acquisition with up to 120 billion points to show their practicality and feasibility.},
  language  = {en}
}
@misc{StojanovicTrappRichteretal.2018,
  author    = {Stojanovic, Vladeta and Trapp, Matthias and Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {A service-oriented approach for classifying 3D points clouds by example of office furniture classification},
  series = {Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology},
  journal   = {Web3D 2018: Proceedings of the 23rd International ACM Conference on 3D Web Technology},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  isbn      = {978-1-4503-5800-2},
  doi       = {10.1145/3208806.3208810},
  pages     = {1 -- 9},
  year      = {2018},
  abstract  = {The rapid digitalization of the Facility Management (FM) sector has increased the demand for mobile, interactive analytics approaches concerning the operational state of a building. These approaches provide the key to increasing stakeholder engagement associated with Operation and Maintenance (O\&M) procedures of living and working areas, buildings, and other built environment spaces. We present a generic and fast approach to process and analyze given 3D point clouds of typical indoor office spaces to create corresponding up-to-date approximations of classified segments and object-based 3D models that can be used to analyze, record and highlight changes of spatial configurations. The approach is based on machine-learning methods used to classify the scanned 3D point cloud data using 2D images. This approach can be used to primarily track changes of objects over time for comparison, allowing for routine classification, and presentation of results used for decision making. We specifically focus on classification, segmentation, and reconstruction of multiple different object types in a 3D point-cloud scene. We present our current research and describe the implementation of these technologies as a web-based application using a services-oriented methodology.},
  language  = {en}
}