DynoViz: Dynamic Visualization of Large Scale Satellite Data

被引:1
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
美国食品与农业研究所; 美国国家科学基金会;
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 27 条
[1]  
[Anonymous], Mapbox
[2]  
ArcGIS, 2022, ArcGIS
[3]   Web-based visualization of very large scientific astronomy imagery [J].
Bertin, E. ;
Pillay, R. ;
Marmo, C. .
ASTRONOMY AND COMPUTING, 2015, 10 :43-53
[4]  
Brown Daniel., 2015, Monitoring and evaluating post-disaster recovery using high-resolution satellite imagery-towards standardised indicators for post-disaster recovery
[5]  
Cruz Isabel F, 2013, P 21 ACM SIGSPATIAL
[6]  
Eldawy A, 2015, PROC INT CONF DATA, P1585, DOI 10.1109/ICDE.2015.7113427
[7]  
Eldawy Ahmed, 2016, 2016 IEEE 32nd ICDE
[8]  
GDAL/OGR contributors, 2022, GDAL/ OGR Geospatial Data Abstraction software Library
[9]  
GeoTrellis, 2019, GeoTrellis on Spark
[10]   AID*: A Spatial Index for Visual Exploration of Geo-Spatial Data [J].
Ghosh, Saheli ;
Eldawy, Ahmed .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 34 (08) :3569-3582