A view-dependent spatiotemporal saliency-driven approach for time varying volumetric data in geovisualization. (September 2016) - Li, Jing; Zhang, Tong; Wong, David W.S.; Mooney, Meghan
Abstract: Geospatial datasets from satellite observations and model simulations are becoming more accessible. These spatiotemporal datasets are relatively massive for visualization to support advanced analysis and decision making. A challenge to visualizing massive geospatial datasets is identifying critical spatial and temporal changes reflected in the data while maintaining high interactive rendering speed, even when data are accessed remotely. We propose a view-dependent spatiotemporal saliency-driven approach that facilitates the discovery of regions showing high levels of spatiotemporal variability and reduces the rendering intensity of interactive visualization. Our method is based on a novel definition of data saliency, a spatiotemporal tree structure to store visual saliency values, as well as a saliency-driven view-dependent level-of-detail (LOD) control. To demonstrate its applicability, we have implemented the approach with an open-source remote visualization package and conducted experiments with spatiotemporal datasets produced by a regional dust storm simulation model. The results show that the proposed method may not be outstanding in some specific situations, but it consistently performs very well across different settings according to different criteria. Highlights: A novel measurement for spatiotemporal information saliency is proposed to quantify information diversity. A saliency-driven time–space partition tree (TSP) based visualization enhancement approach is designed. The saliency-driven approach is implemented in a cloud-based visualization framework.