Identifying significant environmental features using feature recognition.

White, Megan; Zhu, Junfeng; Blandford, Benjamin; Grossardt, Ted; University of Kentucky Transportation Center

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Identifying significant environmental features using feature recognition.

2015-10-01
By White, Megan ; Zhu, Junfeng ; Blandford, Benjamin ; ...

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Details:

Creators:

White, Megan ; Zhu, Junfeng ; Blandford, Benjamin ; Grossardt, Ted

White, Megan ; Zhu, Junfeng ; Blandford, Benjamin ; Grossardt, Ted Less -
Corporate Creators:

University of Kentucky Transportation Center
Corporate Contributors:

Kentucky. Transportation Cabinet ; United States. Federal Highway Administration

Kentucky. Transportation Cabinet ; United States. Federal Highway Administration Less -
Subject/TRT Terms:

[+]

Accuracy Automatic Data Collection Systems Data Processing Geographic Information Systems Geological Surveying Laser Radar Sinkholes Software Topography
Publication/ Report Number:

KTC-15-18/SPR15-502-1F
Resource Type:

Tech Report
Geographical Coverage:

Kentucky
Corporate Publisher:

University of Kentucky Transportation Center
Abstract:

The Department of Environmental Analysis at the Kentucky Transportation Cabinet has expressed an interest in feature-recognition capability because it may help analysts identify environmentally sensitive features in the landscape,

including those relating to historic preservation, archaeology, endangered species habitat, and geology. LIDAR Analyst

and Feature Analyst are a pair of geoprocessing software packages that have been developed by Textron Systems.

Using this software, users can use LIDAR data to identify finely-scaled user-specified features. The software’s

automated feature extraction saves time that might otherwise be spent manually analyzing images and digitizing

features.

This report explores the capabilities and accuracy of this software by using LIDAR data to identify sinkholes

throughout a small area in Kentucky. This report also discusses an alternative LIDAR-based geoprocessing methodology

developed by the Kentucky Geological Society. The method relies on ArcGIS and Python scripting to identify sinkholes.

The feasibility and applicability of these methodologies are compared, the workflow for each method is outlined, and the

capabilities and limitations of each are noted. Sample results—the identification of sinkholes—from each methodology

are presented. The research team found the batch processing capability built into LIDAR and Feature Analyst adequate

and beneficial for smaller projects, such as projects that prioritize the extraction of buildings, trees, and forest regions.
Format:

PDF
Funding:

SPR15-502
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:bfda9eda2b58614470464c28f6bc238130f3f2183dd6c65fe7897241042c1c25
Download URL:

https://rosap.ntl.bts.gov/view/dot/29542/dot_29542_DS1.pdf
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Identifying significant environmental features using feature recognition.

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