DynoViz: Dynamic Visualization of Large Scale Satellite Data

被引:0
作者
Shang, Zhuocheng [1 ]
Shivakumar, Suryaa Charan [1 ]
Eldawy, Ahmed [1 ]
机构
[1] Univ Calif Riverside, Riverside, CA 92521 USA
来源
PROCEEDINGS OF THE 12TH ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ANALYTICS FOR BIG GEOSPATIAL DATA, BIGSPATIAL 2024 | 2024年
基金
美国国家科学基金会; 美国食品与农业研究所;
关键词
Raster data; Spatial data; Satellite imagery; Big data; Visualization; multilevel;
D O I
10.1145/3681763.3698475
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rapid increase in publicly available satellite data with high-resolution, so did the demand on interactive visualization of this data on a web map. This data is often high-resolution, with up-to daily three-meter resolution data, and multi-spectral with up-to 15 bands per datasets. Users in various fields need to interactively explore terabytes of this data via a web-based interface to choose the right data for their projects. Unfortunately, existing systems are either single-machine with limited scalability, or they do have limited support for dynamic visualization. Moreover, most systems pre-render visible bands only, i.e., RGB, and ignore other bands even though many scientific domains are more interested in other bands, e.g., infrared. This work introduces DynoViz, a novel system for dynamic web-based scalable visualization of satellite data. It visualize big satellite data on a regular web-based interface through multilevel dynamic-resolution visualization. The design consists of three main parts. First, a pre-generation process produces a limited set of select static tiles stored on disk. This process is controlled with a parameter to balance interactivity and disk usage depending on the application needs. Second, a dynamic on-the-fly generation technique uses a raster index to provide real-time visualization of high-resolution regions of the map. Third, a web-based interface provides client-side rendering of tiles according to the user requirements and can handle multi-spectral data with no additional overhead on the server. Experiments with terabyte-scale datasets show that DynoViz is up-to an order of magnitude faster than other distributed systems in the pre-generation phase and uses 60 times less disk storage without sacrificing the interactivity.
引用
收藏
页码:20 / 29
页数:10
相关论文
共 50 条
  • [31] Visualization of large-scale correlations in gene expressions
    Eriksen K.A.
    Hörnquist M.
    Sneppen K.
    Functional & Integrative Genomics, 2004, 4 (4) : 241 - 245
  • [32] Visualization of large-scale correlations in gene expressions
    Eriksen, K. A.
    Hornquist, M.
    Sneppen, K.
    FUNCTIONAL & INTEGRATIVE GENOMICS, 2004, 4 (04) : 241 - 245
  • [33] Large Scale Spatial Temporal Data Visualization based on Spark and 3D Volume Rendering
    Mao, Bo
    Yu, Zhengchun
    Cao, Jie
    2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 1879 - 1882
  • [34] Visualization for HPC data - Large terrain model
    Li, BY
    Liao, HS
    Chang, CH
    Chu, SL
    SEVENTH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND GRID IN ASIA PACIFIC REGION, PROCEEDINGS, 2004, : 280 - 284
  • [35] Visualization of high dynamic range data in geosciences
    Yuan, Xiaoru
    Liu, Yingchun
    Chen, Baoquan
    Yuen, David A.
    Perglerd, Tomas
    PHYSICS OF THE EARTH AND PLANETARY INTERIORS, 2007, 163 (1-4) : 312 - 320
  • [36] Dynamic smooth subdivision surfaces for data visualization
    Mandal, C
    Qin, H
    Vemuri, BC
    VISUALIZATION '97 - PROCEEDINGS, 1997, : 371 - +
  • [37] Efficient Large Scale Clustering based on Data Partitioning
    Bendechache, Malika
    Le-Khac, Nhien-An
    Kechadi, M-Tahar
    PROCEEDINGS OF 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS, (DSAA 2016), 2016, : 612 - 621
  • [38] A distributed execution environment for large data visualization
    Huang, Jian
    Liu, Huadong
    Beck, Micah
    Gao, Jinzhu
    Moore, Terry
    SCIDAC 2006: SCIENTIFIC DISCOVERY THROUGH ADVANCED COMPUTING, 2006, 46 : 570 - 576
  • [39] Accelerating BIRCH for Clustering Large Scale Streaming Data Using CUDA Dynamic Parallelism
    Dong, Jianqiang
    Wang, Fei
    Yuan, Bo
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2013, 2013, 8206 : 409 - 416
  • [40] Big Data Visualization Tools: A Survey The New Paradigms, Methodologies and Tools for Large Data Sets Visualization
    Caldarola, Enrico G.
    Rinaldi, Antonio M.
    PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON DATA SCIENCE, TECHNOLOGY AND APPLICATIONS (DATA), 2017, : 296 - 305