Product Review: LIDAR Analyst 5.1

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LIDAR Analyst has been around since 2005, when it was first released as an extension to ArcGIS. The initial release focused primarily on DEM generation and select automated feature extraction tasks.

A lot has changed since 2005: Visual Learning Systems (VLS), the company that makes LIDAR Analyst, was acquired by Overwatch (part of Textron Systems) and Esri has increasingly enhanced LIDAR support with each release of ArcGIS. The former begs the question as to whether or not the acquisition by a large defense-oriented company has stifled innovation, and the latter begs the question whether or not one would even need a LIDAR extension to ArcGIS given the current capabilities?

The answers to those questions are "no" and "yes" respectively. The acquisition by Overwatch hasn’t slowed down the folks at VLS one bit and the 5.1 release of LIDAR Analyst makes complex LIDAR workflows accessible to the GIS community, going far beyond the capabilities offered by ArcGIS.

Given that LIDAR Analyst is an ArcGIS extension it should come as no surprise that the software is designed with the GIS user in mind. Rather than a collection of tools, LIDAR Analyst is more of a collection of workflows that enable the end user to explore, prep, and extract information from LIDAR point clouds. LIDAR Analyst is extremely easy to use. An experienced GIS professional can be up and running with the software within a few hours, performing fairly complex tasks such as DEM generation and automated building extraction.

One of the biggest aids to getting started with LIDAR Analyst is the 215-page tutorial guide and sample projects that accompany the software. The tutorials are presented as logical workflows, guiding the end user through a complete set of tasks from start to finish. While there are detailed help files, I found the project-driven approach to the tutorial guide to be a big aid when I first started using the software.

The main interface for LIDAR Analyst consists of a toolbar within ArcMap (Figure 1). The tools are arranged in a logical order and the interface is clean and uncluttered. One of the strengths of LIDAR Analyst is that is does not expose endless parameters to the end user, but rather has a limited number of key parameters with default values that in my extensive testing hardly ever needed to be adjusted. As I mentioned earlier, LIDAR Analyst was chiefly designed with step-wise workflows in mind (e.g. derive a bare earth surface model from a point cloud, extract buildings, and then manually correct errors). As such, you will have the greatest success if you use LIDAR analyst for all of the points along a given workflow as one of the few drawbacks of LIDAR Analyst is that it can be a bit particular with the inputs.

LIDAR Analyst has always offered a robust set of tools that enable the end user to carry out key tasks such as generating bare earth terrain models or extracting tree point locations. The interface makes it easy to test workflows, but even better is LIDAR Analyst’s macro-esqe feature of automatically saving any process that is run to a .afe file. This .afe file contains all the settings you need to then run the process in batch.

For example, you can test out various approaches to DEM generation and building extraction on a few LAS files, then after you have settled on a set of parameters, use the .afe file to batch process all your LIDAR point cloud data to get out DEMs and building polygons.

If you want even greater control you can assemble processes using the Feature Modeler (Figure 2). Given that LIDAR Analyst is built on top of ArcGIS it’s not the fastest solution for batch processing tasks, but 5.1 adds the functionality to distribute batch processing to multiple CPUs using the open source HTCondor highthroughput computing platform.

In the 5.1 release Feature Analyst moves beyond being a tool one would use solely for generating raster surfaces or automatically extracting buildings or trees. The major improvements have come in the realm of point cloud visualization and LIDAR pre-processing.

LIDAR Analyst now includes a standalone point cloud viewer (Figure 3). It is certainly not the best point cloud viewer on the market, but it has numerous features that put it head and shoulders above the standard point cloud viewer available in ArcGIS.

It has the added ability to sync with the active data frame ArcMap, greatly facilitating the simultaneous analysis of multiple datasets. The point cloud viewer includes all the tools the GIS end user will need; from profile views (Figure 4) to TIN display (Figure 5). There are also tools that will help GIS users make better sense of LIDAR data and improve their workflow such as LAS header viewing and LAS coordinate conversion.

LIDAR Analyst does not offer a highly set of customizable feature extraction tools (although one can do this by combining it with Overwatch’s Feature Analyst product), rather it focuses on the most common feature extraction tasks and offers highly functional, tailored workflows that make it easy to not only automatically extract features, but to correct errors and make improvements.

I tested LIDAR Analyst’s building extraction capabilities on the publically available Pennsylvania LIDAR data and was pleased with the results, particularly as the LIDAR point density was relatively low. LIDAR Analyst produced cartographically-pleasing, attribute-rich output in a GIS-ready format (Figure 6). Although there were some errors of commission, I observed very few errors of omission, even on small buildings adjacent to tree canopy. These errors are easy to correct using the built-in building editing tools.

LIDAR Analyst is not a fully functional LIDAR package, but it’s not intended to be. It is a robust, yet easy to use solution that opens up LIDAR visualization and exploitation to the GIS community. The latest version is a worthy upgrade and the package should be on the short list of any GIS user who works with LIDAR data.

Jarlath O’Neil-Dunne is on the faculty of the University of Vermont where he also serves as the director of the Spatial Analysis Laboratory. He and his team specialize in extracting meaningful information from high-resolution remotely sensed data.

A 1.455Mb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE