11,047 research outputs found
Fireground location understanding by semantic linking of visual objects and building information models
This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding
Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment
We study the question of how visual analysis can support the comparison of
spatio-temporal ensemble data of liquid and gas flow in porous media. To this
end, we focus on a case study, in which nine different research groups
concurrently simulated the process of injecting CO2 into the subsurface. We
explore different data aggregation and interactive visualization approaches to
compare and analyze these nine simulations. In terms of data aggregation, one
key component is the choice of similarity metrics that define the relation
between the different simulations. We test different metrics and find that a
fine-tuned machine-learning based metric provides the best visualization
results. Based on that, we propose different visualization methods. For
overviewing the data, we use dimensionality reduction methods that allow us to
plot and compare the different simulations in a scatterplot. To show details
about the spatio-temporal data of each individual simulation, we employ a
space-time cube volume rendering. We use the resulting interactive, multi-view
visual analysis tool to explore the nine simulations and also to compare them
to data from experimental setups. Our main findings include new insights into
ranking of simulation results with respect to experimental data, and the
development of gravity fingers in simulations.Comment: arXiv preprin
Scale-Space Splatting: Reforming Spacetime for the Cross-Scale Exploration of Integral Measures in Molecular Dynamics
Understanding large amounts of spatiotemporal data from particle-based
simulations, such as molecular dynamics, often relies on the computation and
analysis of aggregate measures. These, however, by virtue of aggregation, hide
structural information about the space/time localization of the studied
phenomena. This leads to degenerate cases where the measures fail to capture
distinct behaviour. In order to drill into these aggregate values, we propose a
multi-scale visual exploration technique. Our novel representation, based on
partial domain aggregation, enables the construction of a continuous
scale-space for discrete datasets and the simultaneous exploration of scales in
both space and time. We link these two scale-spaces in a scale-space space-time
cube and model linked views as orthogonal slices through this cube, thus
enabling the rapid identification of spatio-temporal patterns at multiple
scales. To demonstrate the effectiveness of our approach, we showcase an
advanced exploration of a protein-ligand simulation.Comment: 11 pages, 9 figures, IEEE SciVis 201
Geo-Spatial Analysis in Hydrology
Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale
Estimation of Sounding Uncertainty from Measurements of Water Mass Variability
Analysis techniques are introduced that allow for estimation of potential sounding uncertainty due to water mass variability from reconnaissance campaigns in which oceanographic parameters are measured at a high temporal and spatial resolution. The analysis techniques do not require sounding data, thus analyses can be tailored to match any survey system; this allows for pre-analysis campaigns to optimize survey instrumentation and sound speed profiling rates such that a desired survey specification can be maintained. Additionally, the output of the analysis methods can potentially provide a higher fidelity estimation of sounding uncertainty due to water mass variability than uncertainty models in common use
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