Geo-Visualization in 4D environment - Simulation of floods over an Urban Area

Abstract

Visualization of geographical data is one of the important aspects of a geographical information system. Whilst 2D visualization techniques have been employed for decades, capturing dynamic phenomenon like floods over static urban areas within a space-time framework is gaining importance in GIS systems. This is because viewing geospatial data in higher (3 or 4) dimensions enables visualization of insightful information concerning events that may have been either limited or missing before. In certain phenomenon, the dynamics of it is better visualized and understood when the 3-dimensions of space and the time dimension (both forward and backward) can be employed to highlight its trajectory and object states over the region of interest. For instance, the phenomenon like ocean currents, atmospherics systems, airplane tracking, GPS tracks on terrain, etc. In the recent past, with the availability of three-dimensional geospatial data and the advancements in GPU processing power, various computer graphics applications have emerged including its adoption for Geospatial applications like virtual globes, city visualization, etc. While efforts at the visualization of space-time dependent phenomena like floods over natural surfaces have been attempted, but doing so in the presence of 3D non-natural objects and developing it from a GIS perspective is still a challenge. From a geospatial perspective, depicting a space-time process requires not only the time state information of the phenomenon but also its integration with the surface model. This throws up the challenge of capturing the phenomenon in the right geospatial context, i.e., the projection system. Other challenges include the ability to transform the data model to visualize the static or dynamic objects over the terrain and creating a computational framework that can integrate the process models with such geospatial visualization approaches. We make use of the publicly available 3D Berlin CityGML dataset for creating static urban models. As CityGML is an information model rather than visualization model, using them for rendering is non-trivial, along with their integration with virtual globes. We handle such issues by making use of 3DCityDB along with a tiling based approach which helps in rendering large CityGML datasets. Further, to make building models more realistic, we try to map the building textures with real world textures. We try to achieve this by automated draping of building textures from geo-tagged images which are captured by a cell phone camera with a built-in GPS. We use the properties of the images to tag them to the corresponding footprint by using the camera pose, and the position of the camera to automate the process. The challenge of integrating the dynamic phenomenon and the static urban model is handled by creating digital surface models of the region of interest. And using these surface models as the base for hydrological simulation. Some attempts have been made using methods of animation to depict the dynamic phenomenon like floods, snow avalanches, etc. While these approaches do provide a good visualization of the effect, they are based on simulated scenarios with an effort towards a smooth visual appearance of the depicted phenomenon. In this process, they overlook the locational interactions, which a near-real process simulation of the phenomenon capture. In this work, we present a 4D GIS system to visualize space-time dependent phenomena - simulated hydrological water flow model over an urban area. This work attempts to use the calculated water depth information to present a nearreal visual rendering of the same, with the emphasis on the visual interaction of the 3D objects (buildings) with such phenomenon captured in 4D (space and time). The developed system is built using the NASA’s WorldWind Globe and uses a depth filling algorithm as its input for time-step generated water depth maps, the dynamic layer. The urban scene is derived from a static CityGML LOD2 buildings layer overlaid on the digital elevation map. The dynamic flow visualization is enabled through an appropriate color mapping scheme so that the user can have a fair sense of water depth at various areas of the city over the time period. While visualization helps to understand the phenomenon and its progress, it is also important to provide an appropriate mechanism to derive or extract the relevant technical information from such a system. Towards this, in the developed system, analytical tools like querying for water depth at any given time, displaying of hydrographs showing the variation of height over time at a given location and a slider to control the time parameter of the system, have been incorporated. As the system uses 3DcityDB as the storage model, it is highly expendable for answering complex queries like ”which buildings of a specific area of the city will get flooded and when ?”. Such queries are not yet built in and is left for further work. The static model represents only the building models, and doesn’t take into account other city features like city furniture and vegetation. Further the system doesn’t capture the effect of the dynamic surface on the static model. Such interactions can be considered for future work.