loader graphic

Loading content ...

SeaVizKit: Interactive Maps for Ocean Visualization

Ali, W.H., M. Merhi, A. Gupta, C.S. Kulkarni, C. Foucart, D.N. Subramani, and P.F.J. Lermusiaux, 2019. SeaVizKit: Interactive Maps for Ocean Visualization. In: OCEANS '19 MTS/IEEE Seattle, 27-31 October 2019, in press.

With the increasing availability of high-resolution comprehensive spatiotemporal ocean models and observation systems, ocean data visualization has become ubiquitous. This is due to the major impact of ocean products on disaster management, shipping, fisheries, coastal operations and scientific studies. Yet, there are several challenges for effective communication of data through visualization techniques. Specifically, ocean data is multivariate (e.g. temperature, salinity, velocity, etc.), and is available for multiple depths and multiple time instants which leads to large, multi-dimensional datasets. Thus, it is necessary to have an interactive multiscale visualization tool that can assist scientists, policy makers, and the public in getting insights from big data produced by ocean predictions and observations.

In this work, we present a 3D (spatial) + 1 (temporal) multivariate visualization tool that produces interactive, dynamic, fast and portable ocean maps. The tool is based on Leaflet and D3.js JavaScript libraries, and is built for the multidisciplinary simulation, estimation, and assimilation systems (MSEAS) ocean products, along with it has been used for multiple real-time sea exercises.


Clustering of Massive Ensemble of Vehicle Trajectories in Strong, Dynamic and Uncertain Ocean Flows

Dutt, A., D.N. Subramani, C.S. Kulkarni, and P.F.J. Lermusiaux, 2018. Clustering of Massive Ensemble of Vehicle Trajectories in Strong, Dynamic and Uncertain Ocean Flows. In: Oceans '18 MTS/IEEE Charleston, 22-25 October 2018. doi:10.1109/oceans.2018.8604634

Recent advances in probabilistic forecasting of regional ocean dynamics, and stochastic optimal path planning with massive ensembles motivate principled analysis of their large datasets. Specifically, stochastic time-optimal path planning in strong, dynamic and uncertain ocean flows produces a massive dataset of the stochastic distribution of exact timeoptimal trajectories. To synthesize such big data and draw insights, we apply machine learning and data mining algorithms. Particularly, clustering of the time-optimal trajectories is important to describe their PDFs, identify representative paths, and compute and optimize risk of following these paths. In the present paper, we explore the use of hierarchical clustering algorithms along with a dissimilarity matrix computed from the pairwise discrete Frechet distance between all the optimal trajectories. We apply the algorithms to two datasets of massive ensembles of vehicle trajectories in a stochastic flow past a circular island and stochastic wind driven double gyre flow. These paths are computed by solving our dynamically orthogonal level set equations. Hierarchical clustering is applied to the two datasets, and results are qualitatively and quantitatively analyzed.


Volume rendering data with uncertainty information

Djurcilov, S., K. Kim, P.F.J. Lermusiaux and A. Pang, 2001. Volume rendering data with uncertainty information. In "Data visualization", Joint Eurographics - IEEE TCVG Symposium on Visualization, D. Ebert, J. M. Favre and R. Peikert (Eds.), Springer-Verlag. pp. 243-252, 355-356.

This paper explores two general methods for incorporating volumetric uncertainty information in direct volume rendering. The goal is to produce volume rendered images that depict regions of high (or low) uncertainty in the data. The first method involves incorporating the uncertainty information directly into the volume rendering equation. The second method involves post-processing information of volume rendered images to composite uncertainty information. We present some initial findings on what mappings provide qualitatively satisfactory results and what mappings do not. Results are considered satisfactory if the user can identify regions of high or low uncertainty in the rendered image. We also discuss the advantages and disadvantages of both approaches.