Developing strategies to mitigate or to adapt to the threats of floods is an important topic in the context of climate changes. Many of the world’s cities are endangered due to rising ocean levels and changing precipitation patterns. It is therefore crucial to develop analytical tools that allow us to evaluate the threats of floods and to investigate the influence of mitigation and adaptation measures, such as stronger dikes, adaptive spatial planning, and flood disaster plans. Up until the present, analytical tools have only been accessible to domain experts, as the involved simulation processes are complex and rely on computational and data-intensive models. Outputs of these analytical tools are presented to practitioners (i.e., policy analysts and political decision-makers) on maps or in graphical user interfaces. In practice, this output is only used in limited measure because practitioners often have different information requirements or do not trust the direct outcome. Nonetheless, literature indicates that a closer collaboration between domain experts and practitioners can ensure that the information requirements of practitioners are better aligned with the opportunities and limitations of analytical tools. The objective of our work is to present a step forward in the effort to make analytical tools in flood management accessible for practitioners to support this collaboration between domain experts and practitioners. Our system allows the user to interactively control the simulation process (addition of water sources or influence of rainfall), while a realistic visualization allows the user to mentally map the results onto the real world. We have developed several novel algorithms to present and interact with flood data. We explain the technologies, discuss their necessity alongside test cases, and introduce a user study to analyze the reactions of practitioners to our system. We conclude that, despite the complexity of flood simulation models and the size of the involved data sets, our system is accessible for practitioners of flood management so that they can carry out flood simulations together with domain experts in interactive work sessions. Therefore, this work has the potential to significantly change the decision-making process and may become an important asset in choosing sustainable flood mitigations and adaptation strategies.@en

Recent advances in crowd simulation techniques have led to realistic agent and group behavior through elaborate behavioral models, complex motion planning algorithms and impressive physics systems. As many crowd simulation solutions typically target only specific types of environment and scenario, a variety of special-purpose methods and systems has emerged that are hard to re-configure and re-use in other contexts. Solving this situation demands a higher-level approach that takes re-use and configuration of crowds as a priority, for adequate application in a broad variety of scenarios, virtual environments and inter- action with the entities present in that environment. In this article we propose semantic crowds, a novel approach that allows one to re-use the same crowds for virtually any environment, and have them use the objects available in it in a meaningful manner, without any modification. To have the agents autonomously interact within any virtual world, we minimize in them the information relative to what objects do and how to use them. Instead, that information is stored in the objects themselves, which the agents can then query, based on what they plausibly want to achieve. To facilitate creating such crowds, we devel- oped an interactive crowd editor that provides high-level editing parameters for defining crowd templates. We illustrate the flexibility of semantic crowds by means of two cases, in which we let the same crowd populate quite differently configured airport terminal environments. These examples also highlight that this modular approach easily combines with your custom implementations of agent behavior model and/or motion planner.@en

In projection-based Virtual Reality (VR) systems, typically only one headtracked user views stereo images rendered from the correct view position. For other users, who are presented a distorted image, moving with the first user's head motion, it is difficult to correctly view and interact with 3D objects in the virtual environment. In close-range VR systems, such as the Virtual Workbench, distortion effects are especially large because objects are within close range and users are relatively far apart. On these systems, multi-user collaboration proves to be difficult. In this paper, we analyze the problem and describe a novel, easy to implement method to prevent and reduce image distortion and its negative effects on close-range interaction task performance. First, our method combines a shared camera model and view distortion compensation. It minimizes the overall distortion for each user, while important user-personal objects such as interaction cursors, rays and controls remain distortion-free. Second, our method retains co-location for interaction techniques to make interaction more consistent. We performed a user experiment on our Virtual Workbench to analyze user performance under distorted view conditions with and without the use of our method. Our findings demonstrate the negative impact of view distortion on task performance and the positive effect our method introduces. This indicates that our method can enhance the multi-user collaboration experience on close-range, projection-based VR systems.@en