Full text: Proceedings, XXth congress (Part 4)

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windows size and transferred data amount based on the three 
different operations. 
Table 5.1. Windows Size .vs. Data volume 
  
  
  
  
  
  
  
  
  
  
  
  
Zoom toa 
pyramid 
level 
Zoom toa beyond pans 
Windows neighboring | neighborin | across 
size pyramid g level screen 
level (KB) (KB) (KB) 
640X480 337.84 422.30 370.50 
512X380 220.79 283.49 257.07 
400X300 150.99 188.73 179.31 
320X240 105.46 131.83 130.81 
250X190 73.19 91.48 95.38 
200X150 52.88 66.11 72.35 
  
The data volume in Table5.1 is the data which will be 
transferred to client side to show the highest quality of 
images according to users’ requests. The data volume 
transferred to client was reduced while the windows size 
became small. It shows that the windows size should be 
smaller on low network bandwidth than that on high network 
bandwidth. The data volume arranges from 52.88KB to 
422KB on different windows size and different operations. If 
the mean connection speed of Internet users is IMbit/s, 
[25KB/s, the sharpening (get all highest resolution images in 
that request) time will be from 0.4 to 3.4 seconds. Due to the 
data is been streamed to the users, even for the modem users, 
they will feel they are getting serves any time after clicking 
on the viewer. 
6. Conclusions 
Based on this improved structure, GSN client becomes much 
more efficient and smart than the old one. Firstly, the 
response time is equal to a desk top system. User will not feel 
any delay while operating map. Secondly, the network will 
not be the decisive factor to the end users. Thirdly, many 
fancy functions are able to be integrated to GSN client. They 
are Fly In/Out, 2D Image Fly (any direction) of 2D Images. 
The last feature will be the most important core component 
in this project. The module can be easily migrated to a true 
3D model if the digital terrain data is available. Finally, it 
becomes more reliable and extensible. Further works should 
be to use this technique on 3D Presentation and the storage 
technologies to handle huge imagery. 
By applying streaming and pyramid technologies, the 
Sstem's performance has been improved efficiently and 
looks like a desktop GIS application. Additionally, enable to 
Support GSN Globe has promoted it to be a huge image data 
Set server. GSN Image System technology offers these key 
features and benefits: 
Easy Implementation 
GSN Image requires no special server-side software or 
complicated authoring tools, allowing for quick and 
easy content creation and implementation. 
g and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
1143 
* . Bandwidth Friendly 
GSN Image only downloads the files being used and 
streams required files on demand allowing for high- 
resolution images over all bandwidth environments. 
* High Resolution Images 
GSN Image allows web designers the ability to post 
high-resolution JPEG and vector graphics files that 
users can view, regardless of connection speed. 
* ‘unlimited’ amount of images support 
If hardware is available, unlimited images can be put to 
its storage. 
7. References 
Harder, C. (1998). Serving maps on the Internet. 
Redlands, CA: ESRI Press. 
A primer on Internet-based mapping technologies, for 
novices. Thomas Baker, Ph.D. PathFinder Science 
Federal Communications Commission, 2001. 
“FEDERAL COMMUNICATIONS COMMISSION 
RELEASES DATA ON HIGH-SPEED SERVICES 
FOR INTERNET ACCESS”, US 
http://ftp.fec.gov/Bureaus/Common_ Carrier/News Rele 
ases/2001/nrcc0133.html 
ERMapper White Paper, 2003. "Using ECW 
Connector™ and Image Web Server™ with ArcIMS®”, 
US. 
http://Awww.ermapper.com/document/doc.aspx?doc id= 
34 
William B. Pennebaker and Joan L. Mitchell, 1992. 
JPEG: Still Image Data Compression Standard, Van 
Nostrand Reinhold, New York. 
J.M. Ogden, E.H. Adelson, J R. Bergen, P.J. Burt. 1985. 
Pyramid-based computer graphics, RCA Engineer 30-5 
Computer Graphics Group, Remote 3D Visualization 
using — Image-Streaming Techniques, Universit at 
Erlangen-N"urnberg, Germany http://www.vis.uni- 
stuttgart. de/ger/research/pub/pub1999/1$1M A DE99.pdf 
  
  
  
  
8. Acknowledgements 
We would like to thank the Geospatial Information and 
Communication Technology (GeoICT) Lab, Department of 
Earth and Space Science and Engineering, York University 
for their support and for providing the GSN source code and 
some high resolution images to support the image-streaming 
algorithm. 
 
	        
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