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Identifying Significant Environmental Features Using Feature Recognition

Authors: White, Megan; Zhu, Junfeng; Blandford, Benjamin L.; Grossardt, Ted H.;

Identifying Significant Environmental Features Using Feature Recognition

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.

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Keywords

Transportation Engineering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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