Automatic 3D
Object Extraction
NV5 Geospatial

Automatic 3D
Object Extraction

Abstract
Improvements to LiDAR (Light Detection and Ranging) technology have
revolutionized 3D geospatial data acquisition and usage. One example thathas made it easier to benefit from LiDAR data is GeoCassini. GeoCassini isa cloud-based platform, which enables users to share, update, and manage
geospatial data. NV5 Geospatial, the maker of the industry standard ENVI®
geospatial analysis software recently deployed its 3D extraction analytics onGeoCassini. The combination of GeoCassini’s cloud processing and ENVI
analytics enables users to harness the power of LiDAR data without theneed for extensive hardware or infrastructure investments. In addition, theplatform facilitates seamless data sharing, collaboration, and accessibility.
This whitepaper explores how users can access data on GeoCassini andapply NV5 Geospatial analysis tools for advanced 3D object extraction. NV5
Geospatial’s Custom Solutions Team also offers 3D object extraction forcustomers who want tailored results to meet specific project requirements.

Introduction
Evolution of LiDAR Technology
From the mid-1990s to the present, LiDAR technology has evolved rapidly,boasting improved accuracy, speed, and affordability. In the mid-1990s, LiDAR
scanners could produce 2,000 to 25,000 pulses per second. During this time,
position accuracy was improved when LiDAR was linked with the GPS technology.
With the invention of 3D spinning LiDAR in 2005 by Velodyne’s founder Dave Hall,360-degree vision was possible.
Today, LiDAR sensors can acquire more than one million points per second, with 5mm accuracy, and datasets can include RGB color values. This has led to LiDARreplacing many topographic surveys and aerial photography campaigns. Alongwith LiDAR becoming more readily available, datasets have also become moreaffordable. This has led to a dramatic rise of business applications using LiDAR.
Automatic Digitizing of Objects
Advancements in LiDAR technology has enabled automatic object digitization,
replacing manual methods. ENVI® software, created by NV5 Geospatial, includes
a variety of tools for detecting objects in LiDAR point clouds, including automaticclassification and vectorization. NV5 Custom Solutions team has developedadditional tools using RANSAC-based detection, and 3D Deep Learningtechniques. These tools and techniques are specifically designed to deliver highquality results that minimize the need for post-processing QA/QC. Along withENVI software, NV5 Geospatial has expertise in 3D data analysis through its
Custom Solutions team which works with customers around the world to deliver
results for specific projects.
E-Cassini’s cloud marketplace (GeoCassini) offers a centralized platform for
LiDAR data and analytics. By leveraging NV5 Geospatial’s renowned analytics
on GeoCassini data, users can now perform 3D object extraction and apply theresults to solve industry-specific problems. The following sections will explore howdifferent LiDAR acquisitions are required for specific 3D object detection.

ENVI LiDAR classification on Strasbourg (IGN France acquisition) OpenData Airborne LiDAR.
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2Automatic 3D Object Extraction

LiDAR Acquisitions
E-Cassini data includes LiDAR data acquired from different platforms. The density
and accuracy of LiDAR data depends on the sensor-to-target distance and thesensor platform, which can be aerial or terrestrial.
Airborne LiDAR
Airborne LiDAR involves sensors mounted on airplanes, helicopter, or drones, andis suitable for large area mapping. It can detect large objects such as buildings,trees, roads, and power poles. Objects that are below others will not be visible.

Low density urban airplane Lidar (left). High-density RGB urban mobile LiDAR on thesame area (right).

Terrestrial Mobile LiDAR
Terrestrial mobile LiDAR systems are attached to vehicles like cars or trains andtravel along a corridor. These systems focus on road, street, and railway objects;
however, they may not capture objects outside the sensor’s field of view.

High-density highway mobile LiDAR displayed with intensity gradient colors.

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Automatic 3D Object Extraction

Object and Shape Detection in LiDAR Point Clouds
E-Cassini’s platform offers three categories of object extraction:
Safety Barriers
Safety barriers are linear objects located on the side or the middle of the highway
and require high-density terrestrial LiDAR data for detection. The relative height ofthe barrier from the ground is provided in the output.

Detected highway barriers in 2D over Google imagery (left). Detected highway barriers in 3D
over terrestrial LiDAR (middle). Detected highway road markings in 2D over Google imagery.

Highway Markings
Highway markings are flat objects of varying shapes that are detected by using
high-density terrestrial LiDAR data. If available, the process can take the trajectoryof the CSV file into account to increase the quality of the results.
Traffic Signs
Large and small highway traffic signs are detected using high-density terrestrial
LiDAR data. If available, the process can take trajectory CSV file into account toincrease the quality of the results.

Detected traffic signs in 3D over terrestrial LiDAR with intensity gradient colors.

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Automatic 3D Object Extraction

Highway Bridges
Detecting highway bridges requires a high-density terrestrial LiDAR acquisition.
If available, the process can take the trajectory of the CSV file into account toincrease the quality of the results.

Detected highway bridges in 2D over Google imagery (left). Detected highway bridges in 3D
over terrestrial LiDAR with intensity gradient colors (right).

Landscape Features
ENVI LiDAR classification analytics on E-Cassini’s platform offer two processes
for detecting trees and buildings. Both can be detected with either low-density
airborne LiDAR or high-density mobile terrestrial LiDAR. Building roofs will be only
partially detected since LiDAR sensors cannot detect hidden portions of roofs.
Trees
Trees can be detected using either airborne or terrestrial LiDAR data. The output
includes the tree height and radius.

Detected trees in 2D over Google imagery.

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Buildings
Buildings are detected using airborne or terrestrial LiDAR data. Outputs will
include roof sections and building perimeters.

Detected buildings roofs in 2D over Google imagery (left). Detected buildings roofs in 3D overairborne LiDAR (right).

Building perimeter polygons are located at the bottom of the roofs.

Detected building perimeters in 2D over Google imagery (left). Detected building’s perimeters in
3D over airborne LiDAR (right).

Urban Elements
Urban elements include any type of object clearly visible in the point cloud.
Detecting these types of objects requires high-density mobile terrestrial LiDAR.
Poles
Poles that can be detected include power poles, telephone poles, andstreetlamps. Outputs includes 2D and 3D representations and pole height, whichhelps distinguish the type of pole.

Detected poles in 2D over Google imagery (left). Detected poles in 3D over high-density
terrestrial LiDAR with RGB (right).
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Automatic 3D Object ExtractionDeployment
Cloud-based processing services are initiated from E-Cassini’s web platform,
with dedicated virtual machines that handle the processing using the describedanalytics. Users can view results in 2D and 3D on E-Cassini’s platform or
download them for use in GIS software.

Future
NV5 Geospatial is in the process of developing new object detection capabilities
including advanced pole classification using 3D Deep Learning.

Conclusion
The evolution of LiDAR technology has brought about significant advancements
in 3D geospatial data acquisition and usage. This whitepaper has highlighted
the transformative capabilities of GeoCassini, E-Cassini’s cloud-based solution
for sharing, updating, and managing data from various sources, including LiDAR
sensors, drones, and videos. By leveraging NV5 Geospatial’s renowned 3D
extraction analytics, users can now leverage LiDAR data and apply sophisticatedanalysis benefiting various industries such as infrastructure planning,transportation, and urban development.
Furthermore, NV5 Geospatial’s Custom Solutions Team possesses expertisein working with LiDAR and 3D data to deliver tailored results to customers withspecific project requirements. As LiDAR technology continues to evolve, we canexpect even more exciting applications and advancements in the future, further
enhancing our understanding and utilization of 3D geospatial data.

Automatic 3D Object Extraction© 2023 NV5 Geospatial | 6/2023 NO

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