Community Services for Serving LiDAR Data

Christopher J. Crosby, J Ramón Arrowsmith, Jeffrey Connor, Gilead Wurman

Efrat Frank, Ashraf Memon, Vishu Nandigam, Chaitan Baru

International Workshop of Cyberinfrastructure for Geosciences (IWCG2006)

LiDAR (LIght Detection And Ranging) a.k.a ALSM (Airborne Laser Swath Mapping)

• Airborne pulsed laser scanning system + differential GPS + inertial measurement unit (IMU)

• > 30,000 points/second

• Ground sampled multiple points/sq. meter

• ~ 15 cm vertical accuracy

• ~$300 - $500 per sq. km acquisition cost

http://coastal.er.usgs.gov/hurricanes/mappingchange/

Canopy Height (ft)

D. Harding, NASA

Point Cloudx, y, z, …

Complete LiDAR Workflow

Survey

Analyze / “Do Science”

Interpolate / Grid

Triangular Interpolation Network (TIN) Inverse Distance Weighted (IDW)

Kriging Regularized Spline with Tension and smoothing (RST)

Figure from Helena Mitasova (NCSU): http://skagit.meas.ncsu.edu/~helena/gmslab/index.html

Project goal:Democratize access to LiDAR point cloud data using cyberinfrastructure available through the GEON project.

~1.2 billion data points

Example Data Set:

• Northern San Andreas fault and associated marine terraces.

• Flown February 2003

• Funded by NASA in collaboration w/ USGS.

• ~418 Square Miles

The Computational Challenge:

• LiDAR/ALSM generates massive data volumes - billions of returns are common.

• Distribution of these volumes of point cloud data to users via the internet represents a significant challenge.

• Processing and analysis of these data requires significant computing resources not available to most geoscientists.

• Interpolation of these data challenges typical GIS / interpolation software.– our tests indicate that ArcGIS, Matlab and similar software

packages struggle to interpolate even a small portion of these data.

GEON / ASU node “Agassiz”:

• Multi-institution collaboration between IT and Earth Science researchers• Funded by NSF “large” ITR program

• GEON Cyberinfrastructure provides:– Authenticated access to data and Web services– Registration of data sets and tools, with metadata– Search for data, tools, and services, using ontologies

• “GEON was designed as an equal collaboration between Information Technology (IT) and Geoscience researchers, with the goal of developing an enabling IT platform to facilitate the next generation of Geoscience research.”

Distributed Network

- Scientific workflow environment- Data and map integration capability- Visualization and GIS mapping

The Vision:• Utilize the

cyberinfrastructure developed by GEON to offer online data distribution, interpolation to grid, and analysis of large LiDAR datasets.

• Completely internet-based workflow:– Point cloud to

visualization

• Utilize modular web services to complete a variety of processing and analysis tasks.

• Offer users control of interpolation and analysis parameters.

Conceptual GEON LiDAR Workflow

• Goal = interactive processing environment for iteration and exploration of various interpolation and processing options.

– Optimize landscape representation based upon application of the data.

• By using distributed computing resources, user is able to quickly run multiple jobs and compare results.

– Similar iteration may take days or weeks on a single local machine.

• Leave computationally intensive data processing to resources available through GEON and offer user downloadable products in common file formats.

Products:Hillshade of DEM

Slope Map

Aspect Map

Profile Curvature Map

Implementation Overview:The GLW utilizes advanced spatial databases (IBM DB2), GRASS Open Source GIS, Kepler Workflow manager and web service technology to distribute, interpolate, and analyze LiDAR data.

GRASS GIS

Current GLW Status

Datasets online:

1. Northern San Andreas Fault

2. West Rainier Seismic Zone

3. Eastern California Shear Zone

4. Dragon’s Back portion of the San Andreas Fault (B4 Project)

Total of ~2.6 billion LiDAR returns available via GLW.

Mike Oskin PI

Future WorkDevelopment of GLW is an

ongoing project

• Additional datasets

• Point cloud reprojection

• Additional interpolation algorithms:– Moving window:

Block min / mean / max

• Improve DEM generation performance

• Additional tools and functionality

Future Work• GLW as a tool for data

integration

• Co-hosted raster imagery:– Aerial photography

– Hyperspectral

• Incorporation of terrestrial LiDAR data (TLS)

• Dynamically generated 3D objects for web-based visualization:– VRML

– Fledermaus scene (.sd) files

LiDAR data integration: Dynamically generated 3D model from LiDAR DEM and aerial photography

GLW as a community resource

• Currently, LiDAR datasets are hosted and maintained by a variety of organizations/agencies.

• Goal is to provide a unified “one stop shop” portal for users to access and process these datasets.

• GLW architecture is modular and scaleable so it is well suited to managing access to geographically distributed datasets and processing tools.

• Discussions underway with a number of organizations/agencies to expose their data via the GLW:– U.S. Geological Survey EROS Data Center

– Forthcoming UNAVCO/GeoEarthscope datasets

GEON LiDAR Workflow Access Instructions:

http://www.geongrid.org/science/lidar.html

More information on the GLW:

Efrat Jaeger-Frank, Christopher J. Crosby, Ashraf Memon, Viswanath Nandigam, J. Ramon Arrowsmith, Jeffery Conner, Ilkay Altintas, Chaitan Baru, A Three Tier Architecture for LiDAR Interpolation and Analysis, Lecture Notes in Computer Science, Volume 3993, Apr 2006, Pages 920-927, DOI: 10.1007/11758532_123.

Crosby, C.J. et al., A geoinformatics approach to LiDAR data distribution and processing for the earth sciences, in preparation.

Abstracts, posters & powerpoints:

http://lidar.asu.edu/