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Computing Pfafstetter Labelings I/O-Efficiently (abstract) Lars Arge∗ Andrew Danner†
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Document Date: 2006-01-17 14:17:20


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City

Durham / /

Country

United States / Canada / Denmark / /

/

Facility

Duke University / University of Aarhus Aabogade / Dalhousie University / University Ave / /

IndustryTerm

drainage networks / river network / internal-memory algorithm / river networks / local parallel processing / internal-memory algorithms / software memory limit / recursive labeling algorithm / computing / above algorithm / geographic information systems / terrain simplification algorithms / software distribution / /

NaturalFeature

river RTID / river RRID / river Ri / /

OperatingSystem

Linux / /

Organization

Duke University / Faculty of Computer Science / US Army Research Office / Department of Computer Science / University of Aarhus Aabogade / US National Science Foundation / Canadian Foundation for Innovation / Lars Arge∗ Andrew Danner† Department of Computer Science / Natural Sciences and Engineering Research Council of Canada / Danish National Science Research Council / Dalhousie University / /

Person

Rj / Herman Haverkort‡ Norbert Zeh / /

Position

Governor / author was / /

ProgrammingLanguage

C++ / /

ProvinceOrState

Tennessee / Manitoba / North Carolina / /

PublishedMedium

Computer Graphics / Communications of the ACM / Theory of Computing / /

Technology

terrain simplification algorithms / GIS / 4 3.40 GHz processor / internal-memory algorithms / above algorithm / Remote Sensing / Linux / Existing algorithms / internal-memory algorithm / Image Processing / parallel processing / recursive labeling algorithm / /

URL

http /