Using LiDAR Data within Smallworld

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Using LiDAR Data within Smallworld Mark Field – FCSI

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Using LiDAR Data within Smallworld. Mark Field – FCSI. Overview. LiDAR – We have heard about it, but what is it? How’s it collected What data is in it Variations of LiDAR data How is it stored in Smallworld Example set of tools - PowerPoint PPT Presentation

Transcript of Using LiDAR Data within Smallworld

Page 1: Using  LiDAR  Data within Smallworld

Using LiDAR Data within Smallworld

Mark Field – FCSI

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Overview

• LiDAR – We have heard about it, but what is it?– How’s it collected– What data is in it– Variations of LiDAR data

• How is it stored in Smallworld• Example set of tools• How this implementation can be used to

develop useful data

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What is LiDAR?

• Light Detection And Ranging– aka Laser Detection And Ranging (LADAR)

• Can be used in side and downward-looking– Laser speed gun, weather “radar”, airborne

• Data is gathered by detecting the reflection of the light– Distance, intensity, return number

• Raw results are “point clouds”

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LiDAR Collection Illustration

Source: https://www.e-education.psu.edu/lidar/node/1405

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Variations of LiDAR• Raw, Unfiltered Data(LAS format)– Contains noise, artifacts

• Filtered, Classified (LAS format)– Contains point cloud data– Multiple returns– Classified into ground, vegetation, building, etc.

• Ground Points (LAS format)– Contains single “layer” of ground

• Digital Elevation Models (DEM, DTM, TIN)– Rasterized version of ground points

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Variations of LiDAR (cont.)

• LAZ– Lossless Compressed LAS

• Categorized XYZ – ASCII– Files separated into single category

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Implementation by FCSI

• Expanded FCSI’s N-Dimensional Geometry– 5+ Dimensional TIN Structure (XYZTV)

• TIN Node now contains all LiDAR Data

– X, Y, Z– Intensity– Return– Number of Returns– Scan Direction– GPS Time

– Edge of Flight?– Classification– Scan Angle– User Data– Point Source– Color (RGB)

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Example Tools by FCSI

• Point Viewer– Points colored by return, intensity, elevation,

classification– Filter points by return, intensity, classification

• DEM Updater– Update DEM values based off LiDAR

• Manual Feature Extraction

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Point Viewer

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DEM Update

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Elevation Extraction

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Issues with LiDAR

• Number of Points is extremely large– 1000’ x 100 ‘ of Missouri contains 560,000 points!– 5000’ x 5000’ 5.3 to 8.2 Million points!– 5.3 Million = 463MB gdb.ds (1MB rwo.ds)

• What will it be used for?– DEM/TIN – Rasterize externally– Ensure data is what you want to use

• Is it classified?• Does it contain non-ground data (multiple returns)?

• External tools do exist that may be more effective

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How Can LiDAR Be Used?

• Very accurate terrain models– TINs or DEMs

• Identify features for corridor management– Buildings– Vegetation– Towers, Poles, Lines

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Questions?/Contact

• Mark Field• [email protected]• FCSI Booth 19• Cell: 407-376-6105• @mbfbsae90• mbfbsae90