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Title: Image processing, LiDAR and high resolution 2D interpolation and 3D visualization for data integrati


1
Image processing, LiDAR and high resolution 2D
interpolation and 3D visualization for data
integration
  • J Ramón Arrowsmith Department of Geological
    Sciences, Arizona State University,
    ramon.arrowsmith_at_asu.edu http//activetectonics.la
    .asu.edu/GEONatASU/index.htm
  • http//www.geoinformaticsnetwork.org/swgeonet/
  • With Jeff Conner, Chris Crosby, and Gilead Wurman

2
Java servlets on ASU GEON NODE
3
Very useful data, but difficult to locate
appropriate scenes, acquire, process and
manipulate data for many users. So, we built a
system to do on-the-fly processing and delivery
in a variety of useful formats
http//asterweb.jpl.nasa.gov/
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Connection Method
  • ArcIMS, Java Servlet, Manager Program, Java
    Implemented Server

6
Java Servlet Welcome Screen
Display information to user and communicates with
the Manager program, relaying relevant information
7
Java Servlet - Results Screen
Display results of IDL processing to user and
offer for download processed information
(thumbnail and larger .jpg .tar.gz contains
full and 8x reduced GEOTIFF .tif and .tfw)
8
CIR 3, 2, 1 as Red-Green-Blue (RGB) at 15
m/pixel. Actively photosynthesizing vegetation is
red (near-infrared band). Undisturbed bedrock
and soils appear as browns, greens, and greys.
Built materials and regions typically exhibit
blue-green, reddish - purple, and white
colors. Downloaded GEOTIFF in ArcScene with DEM
base heights.
9
Users besides ASU University of New Mexico,
University of Nevada Las Vegas, Mesa State
University, Texas AM University, US Forest
Service, NASA, University of Arizona, Los Alamos
National Laboratory, US Geological Survey,
Wisconsin Charter School, University of Nevada
Reno Desert Research Institute, Southwest
Research Institute
40 in May
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http//agassiz.la.asu.edu8080/gservlet
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LIght Detection And Ranging
  • Airborne scanning laser rangefinder
  • Differential GPS
  • Inertial Navigation System
  • 30,000 points per second at 15 cm accuracy
  • 4001000/mi2, 106 points/mi2, or
    0.040.1 cents/point
  • Extensive filtering to remove tree canopy
    (virtual defor-estation)

Figure from R. Haugerud, U.S.G.S -
http//duff.geology.washington.edu/data/raster/lid
ar/About_LIDAR.html
14
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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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
  • Database import and query
  • Ties to NCALM and Earthscope
  • Web-based front end for data distribution

San Andreas Laser Scan (Bevis, Hudnut)
22
Current Architecture
Web Services Architecture
  • JSPs
  • Socket Connections
  • Messages passed are a custom Java container class.
  • Portlets
  • SOAP
  • Messages passed are XML

Web services become workflow building blocks
23
Acknowledgements
  • Colleagues and organizations who have shared data
    with us.
  • This work was supported by the US National
    Science Foundation grants ITR/IMAP (GEO)
    Collaborative research Creation of a geospatial
    data system for the transition between the
    Colorado Plateau and the Basin and Range
    Provinces (Geoinformatics in Action)--EAR-0112960
    and ITR Collaborative research GEON a research
    project to create cyberinfrastructure for the
    geosciences--EAR-0225543.
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