TY - JOUR A1 - Discher, Sören A1 - Richter, Rico A1 - Döllner, Jürgen Roland Friedrich T1 - Interactive and View-Dependent See-Through Lenses for Massive 3D Point Clouds JF - Advances in 3D Geoinformation N2 - 3D point clouds are a digital representation of our world and used in a variety of applications. They are captured with LiDAR or derived by image-matching approaches to get surface information of objects, e.g., indoor scenes, buildings, infrastructures, cities, and landscapes. We present novel interaction and visualization techniques for heterogeneous, time variant, and semantically rich 3D point clouds. Interactive and view-dependent see-through lenses are introduced as exploration tools to enhance recognition of objects, semantics, and temporal changes within 3D point cloud depictions. We also develop filtering and highlighting techniques that are used to dissolve occlusion to give context-specific insights. All techniques can be combined with an out-of-core real-time rendering system for massive 3D point clouds. We have evaluated the presented approach with 3D point clouds from different application domains. The results show the usability and how different visualization and exploration tasks can be improved for a variety of domain-specific applications. KW - 3D point clouds KW - LIDAR KW - Visualization KW - Point-based rendering Y1 - 2016 SN - 978-3-319-25691-7 SN - 978-3-319-25689-4 U6 - https://doi.org/10.1007/978-3-319-25691-7_3 SN - 1863-2246 SP - 49 EP - 62 PB - Springer CY - Cham ER - TY - JOUR A1 - Gonschorek, Julia A1 - Langer, Anja A1 - Bernhardt, Benjamin A1 - Raebiger, Caroline T1 - Big Data in the Field of Civil Security Research: Approaches for the Visual Preprocessing of Fire Brigade Operations JF - Science N2 - This article gives insight in a running dissertation at the University in Potsdam. Point of discussion is the spatial and temporal distribution of emergencies of German fire brigades that have not sufficiently been scientifically examined. The challenge is seen in Big Data: enormous amounts of data that exist now (or can be collected in the future) and whose variables are linked to one another. These analyses and visualizations can form a basis for strategic, operational and tactical planning, as well as prevention measures. The user-centered (geo-) visualization of fire brigade data accessible to the general public is a scientific contribution to the research topic 'geovisual analytics and geographical profiling'. It may supplement antiquated methods such as the so-called pinmaps as well as the areas of engagement that are freehand constructions in GIS. Considering police work, there are already numerous scientific projects, publications, and software solutions designed to meet the specific requirements of Crime Analysis and Crime Mapping. By adapting and extending these methods and techniques, civil security research can be tailored to the needs of fire departments. In this paper, a selection of appropriate visualization methods will be presented and discussed. KW - Big Data KW - Civil Security KW - Explorative (Data-) Analysis KW - Geovisual Analytics KW - Visualization Y1 - 2016 U6 - https://doi.org/10.4018/IJAEIS.2016010104 SN - 1947-3192 SN - 1947-3206 VL - 7 SP - 54 EP - 64 PB - IGI Global CY - Hershey ER - TY - JOUR A1 - Lischeid, Gunnar A1 - Kalettka, Thomas A1 - Merz, Christoph A1 - Steidl, Jörg T1 - Monitoring the phase space of ecosystems: Concept and examples from the Quillow catchment, Uckermark JF - Ecological indicators : integrating monitoring, assessment and management N2 - Ecosystem research benefits enormously from the fact that comprehensive data sets of high quality, and covering long time periods are now increasingly more available. However, facing apparently complex interdependencies between numerous ecosystem components, there is urgent need rethinking our approaches in ecosystem research and applying new tools of data analysis. The concept presented in this paper is based on two pillars. Firstly, it postulates that ecosystems are multiple feedback systems and thus are highly constrained. Consequently, the effective dimensionality of multivariate ecosystem data sets is expected to be rather low compared to the number of observables. Secondly, it assumes that ecosystems are characterized by continuity in time and space as well as between entities which are often treated as distinct units. Implementing this concept in ecosystem research requires new tools for analysing large multivariate data sets. This study presents some of them, which were applied to a comprehensive water quality data set from a long-term monitoring program in Northeast Germany in the Uckermark region, one of the LTER-D (Long Term Ecological Research network, Germany) sites. Short-term variability of the kettle hole water samples differed substantially from that of the stream water samples, suggesting different processes generating the dynamics in these two types of water bodies. However, again, this seemed to be due to differing intensities of single processes rather than to completely different processes. We feel that research aiming at elucidating apparently complex interactions in ecosystems could make much more efficient use from now available large monitoring data sets by implementing the suggested concept and using corresponding innovative tools of system analysis. (C) 2015 Elsevier Ltd. All rights reserved. KW - Ecosystem research KW - Monitoring KW - Concept KW - Effective dimensionality KW - Continuity KW - Visualization Y1 - 2016 U6 - https://doi.org/10.1016/j.ecolind.2015.10.067 SN - 1470-160X SN - 1872-7034 VL - 65 SP - 55 EP - 65 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Semmo, Amir A1 - Döllner, Jürgen Roland Friedrich T1 - Interactive image filtering for level-of-abstraction texturing of virtual 3D scenes JF - Computers & graphics : CAG ; an international journal of applications in computer graphics N2 - Texture mapping is a key technology in computer graphics. For the visual design of 3D scenes, in particular, effective texturing depends significantly on how important contents are expressed, e.g., by preserving global salient structures, and how their depiction is cognitively processed by the user in an application context. Edge-preserving image filtering is one key approach to address these concerns. Much research has focused on applying image filters in a post-process stage to generate artistically stylized depictions. However, these approaches generally do not preserve depth cues, which are important for the perception of 3D visualization (e.g., texture gradient). To this end, filtering is required that processes texture data coherently with respect to linear perspective and spatial relationships. In this work, we present an approach for texturing 3D scenes with perspective coherence by arbitrary image filters. We propose decoupled deferred texturing with (1) caching strategies to interactively perform image filtering prior to texture mapping and (2) for each mipmap level separately to enable a progressive level of abstraction, using (3) direct interaction interfaces to parameterize the visualization according to spatial, semantic, and thematic data. We demonstrate the potentials of our method by several applications using touch or natural language inputs to serve the different interests of users in specific information, including illustrative visualization, focus+context visualization, geometric detail removal, and semantic depth of field. The approach supports frame-to-frame coherence, order-independent transparency, multitexturing, and content-based filtering. In addition, it seamlessly integrates into real-time rendering pipelines and is extensible for custom interaction techniques. (C) 2015 Elsevier Ltd. All rights reserved. KW - Image filtering KW - Level of abstraction KW - Texturing KW - Virtual 3D scenes KW - Visualization KW - Interaction Y1 - 2015 U6 - https://doi.org/10.1016/j.cag.2015.02.001 SN - 0097-8493 SN - 1873-7684 VL - 52 SP - 181 EP - 198 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Richter, Rico A1 - Döllner, Jürgen Roland Friedrich T1 - Concepts and techniques for integration, analysis and visualization of massive 3D point clouds JF - Computers, environment and urban systems N2 - Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges. KW - 3D point clouds KW - System architecture KW - Classification KW - Out-of-core KW - Visualization Y1 - 2014 U6 - https://doi.org/10.1016/j.compenvurbsys.2013.07.004 SN - 0198-9715 SN - 1873-7587 VL - 45 SP - 114 EP - 124 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Laue, Ralf A1 - Awad, Ahmed Mahmoud Hany Aly T1 - Visual suggestions for improvements in business process diagrams JF - Journal of visual languages and computing N2 - Business processes are commonly modeled using a graphical modeling language. The most widespread notation for this purpose is business process diagrams in the Business Process Modeling Notation (BPMN). In this article, we use the visual query language BPMN-Q for expressing patterns that are related to possible problems in such business process diagrams. We discuss two classes of problems that can be found frequently in real-world models: sequence flow errors and model fragments that can make the model difficult to understand. By using a query processor, a business process modeler is able to identify possible errors in business process diagrams. Moreover, the erroneous parts of the business process diagram can be highlighted when an instance of an error pattern is found. This way, the modeler gets an easy-to-understand feedback in the visual modeling language he or she is familiar with. This is an advantage over current validation methods, which usually lack this kind of intuitive feedback. KW - Business process model KW - Business process diagram KW - BPMN-Q KW - Visualization Y1 - 2011 U6 - https://doi.org/10.1016/j.jvlc.2011.04.003 SN - 1045-926X VL - 22 IS - 5 SP - 385 EP - 399 PB - Elsevier CY - London ER -