Full text: ISPRS 4 Symposium

RAPID DISPLAY OF DIGITAL ELEVATION MODELS 
IN A MINI-COMPUTER ENVIRONMENT 
Lawrence Kikuchi 
Ph.D. Candidate 
State University of New York at Buffalo 
Geographic Information Systems Laboratory 
415 Fronczak Hall - Amherst NY 14260 
Jose Armando Guevara 
Ph.D. Candidate - State University of New York at Buffalo 
Assistant Professor of Computer Science 
Universidad de Los Andes 
Escuela Ingeniería de Sistemas - Dpto. Computación 
Merida - Venezuela 
David Mark 
Assistant Professor of Geography 
State University of New York at Buffalo 
Geographic Information Systems Laboratory 
415 Fronczak Hall - Amherst NY 14260 
Duane F. Marble 
Professor of Geography 
Adjunct Professor of Computer Science 
State University of New York at Buffalo 
Geographic Information Systems Laboratory 
415 Fronczak Hall - Amherst NY 14260 
ABSTRACT 
Digital Elevation Models (DEM) are digital representations 
of the earth's surface. As with all systems which man 
ipulate large volumes of data, the performance of the 
algoriturns uepenus strongly on the efficient storage and 
structuring of the data. DEM aata are often obtained or 
generated as a uniform rectangular grid (URG) organized 
either in profiles (scan lines) or patches. 
This paper describes a DEM processing system for the quick 
inspection and display of these digital elevation models. 
Utilizing system specific binary blocked I/O, and a run 
time, run-length algoritm, the system is able to produce 
contour band maps, contours, anagrapliic contours, slope 
gradient and aspect maps, and analytical hill-shading for 
a variety of applications. The system is interactive, 
being able to process a digital grid of 200,000 cells in 
about two minutes on a Data General Eclipse S/130 mini 
computer running under RDOS. Other features being incorp 
orated into the system include the detection of pits, 
peaks, passes, ridges, and ravines. The system can poten 
tially handle any regular grid DEM's, but is currently 
keyed to the U.S. Geological Survey's "7-1/2 minute Quad 
rangle" series of digital data sets.
	        
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