Lsevlet: web-based processing tool for LiDAR data

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Lsevlet web-based processing tool for LiDAR data

• J Ramon Arrowsmith
• December 14, 2004

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PART 1 LiDAR data handling and processing - the
challenges
Hector Mine Earthquake ALSM coverage (Mojave
Desert)

• Huge datasets
• 8.79 million pts
• Files getting larger with higher pulse rate
  instruments

• How do we grid and distribute these data?
• ArcGIS cant handle it easily
• Expensive commercial software not an option for
  most data consumers

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ALSM data schematic workflow with partial
implementation -possible scenario for NCALM data
handling system? -incorporation of greater
flexibility into system as user community grows
watershed analysis (?)
-Crosby and Arrowsmith, 2004
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GRASS as a processing tool for LiDAR

• GRASS Open source GIS
• Interpolation commands designed for large data
  sets
• Splines use local pt density to segment data into
  rectangular areas for interpolation
• Can control spline tension and smoothness
• Modular configuration could be easily implemented
  with in the GEON work flow
• EX User uploads point data to remote site where
  GRASS interpolation module runs on super computer
  and returns user a raster file.
• Ultimately a gridding utility for all large,
  computationally intensive data - gravity

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Whats next?

• Build projection library
• Add functionality - TIN, Kriging, Oskin-type
  local plane fits, etc.
• More performance testing
• Comparison of interpolation algorithms for
  different landscapes
• Migrate to SDSC HP cluster for improved
  performance (?)

• Ties to NCALM and Earthscope
• Web-based front end for data distribution

http//calm.geo.berkeley.edu/ncalm/ims/NapaDwnld/v
iewer.jsp
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A quick primer on
From geongrid.org Our mission is to prototype
cyberinfrastructure for the Earth
Sciences, based on close collaboration among IT
and Earth Science
researchers. We have deployed a distributed
system consisting of nodes that host datasets
and/or tools and applications programs, called
the GEONgrid . Such an environment enables
new modes of science and can transform the
day-to-day conduct of science .
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A quick primer on
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GEON computing resources

• Data node
• CPU Xeon 2.8 Ghz MHz Hyperthreaded. 2GB Ram
• 2TB of Storage
• Agassiz (Point of Presence Node)
• CPU Xeon 2.8 Ghz MHz Hyperthreaded. 2GB Ram
  roughly
• 550GB of Storage
• Compute-0- ( 1-4)
• CPU Xeon 2.8 Ghz) MHz Hyperthreaded. 2GB Ram
• roughly 35GB of Storage
• Linux platform