A Three Tier Architecture for LiDAR Interpolation and Analysis

Abstract

Abstract. Emerging Grid technologies enable solving scientific problems that involve large datasets and complex analyses. Coordinating distributed Grid resources and computational processes requires adaptable interfaces and tools that provide a modularized and configurable environment for accessing Grid clusters and executing high performance computational tasks. In addition, it is beneficial to make these tools available to the community in a unified framework through a shared cyberinfrastructure, or a portal, so scientists can focus on their scientific work and not be concerned with the implementation of the underlying infrastructure. In this paper we describe a scientific workflow approach to coordinate various resources as data analysis pipelines. We present a three tier architecture for LiDAR interpolation and analysis, a high performance processing of point intensive datasets, utilizing a portal, a scientific workflow engine and Grid technologies. Our proposed solution is available through the GEON portal and, though focused on LiDAR processing, is applicable to other domains as well.

Tectonic geomorphology of the San Andreas Fault zone from high resolution topography: An example from the Cholame segment

“…The data have been made available publicly. In particular, the GEON project (http://www.geongrid.org or http://www.opentopography.org), through the GEON LiDAR Workflow Jaeger-Frank et al, 2006), enables the scientific community access the raw point cloud and to produce Digital Elevation Models (DEMs) with user-defined resolution and other processing parameters (see below).…”

LiDAR data management pipeline; from spatial database population to web-application visualization

“…In [15], a comprehensive Urban data management system is detailed that leverages many aspects of Geographical Information Systems (GISs), including access to LiDAR stored in a database component. In [10], a grid-computing solution is detailed for processing large volumes of LiDAR which is stored in a database. In [19], this work highlights approaches from the acquisition and processing perspective where optimized triangular meshes are generated from LiDAR point clouds stored in a database.…”

A Provenance-Based Fault Tolerance Mechanism for Scientific Workflows

“…We demonstrate this approach in a scientific workflow example using a part of the GEON LiDAR Workflow (GLW) [3].…”

“…These data tokens are sent again to the sub-workflow along with any data tokens received after it in their original order. Figure 3 shows an example workflow containing a Checkpoint actor, based on the GLW [3], which allows geoscientists to analyze and interpolate Light Distance and Ranging (LiDAR) datasets. A geoscientist first selects a region to be analyzed from a web portal.…”