Processing LiDAR Data to extract 3D Buildings, extract Roof Forms & to classify Power Lines

👉Case Details: Elaborate video explaining Airborne LiDAR Data Processing using Esri ArcGIS Pro.(Watch in 1080p). LiDAR (Light / Laser Imaging, Detection & Ranging) data can be generated from instruments attached to:
a) Airborne vehicles such as Aircraft & Drones,
b) Stationary Terrestrial Scanner installed at ground level or at a specific height, and
c) Mobile Terrestrial Scanners setup on a Vehicle
#lidar #mapping #gis
Three distinct LiDAR data processing workflows covered
- extracting 3D Buildings Footprint
- extracting Roof Forms (extension to Footprint workflow)
- classifying Power Lines using Deep Learning framework
Datasets & Processing Workflow Credit: Esri Learn ArcGIS
👉Video is part of Mapmyops Geo-blog's elaborate article - 'From Point to Plot : LiDAR & Processing its Data' which can be accessed from - www.mapmyops.c...
👉Intelloc Mapping Services | Mapmyops.com is engaged in providing mapping products & services to organizations which facilitate operations improvement, planning & monitoring workflows. These include, but are not limited to - Supply Chain Consulting, Drone Services, Subsurface Mapping, GIS Applications, Satellite Imagery Analytics & Polluted Water Remediation. Projects can be conducted pan-India. Connect with us - [e-mail]
👉 Video is narrated by Arpit Shah - Founder and Partner - Intelloc Mapping Services
👉Read our published content from Mapmyops' Geo-blog - www.mapmyops.c...
👉Watch Mapping Solutions Use Cases on my website's home page - www.mapmyops.com or from this RUclips channel.
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TIMESTAMPS:
00:00 - Headline
00:05 - Case Details
00:19 - Caselet 1 - Extracting 3D Building Footprint from LiDAR Imagery Dataset
00:23 - C1 - Workflow 1 : Setting up & exploring the dataset
03:43 - C1 - Workflow 2 : Classifying the LiDAR Imagery Dataset
10:44 - C1 - Workflow 3: Extracting Buildings Footprint
14:12 - C1 - Workflow 4: Cleaning up the Buildings Footprint
17:25 - C1 - Workflow 5: Extracting 'Realistic' 3D Building Footprint
20:47 - Caselet 2 - Extracting Roof Forms from LiDAR Imagery Dataset
20:51 - C2 - Workflow 1 : Setting up the Data & Creating Elevation Layers
30:16 - C2 - Workflow 2 : Creating 3D Buildings Footprint
33:54 - C2 - Workflow 3 : Checking Accuracy of Building Footprints & Fixing Errors
42:06 - Caselet 3 - Classifying Power Lines using Deep Learning (DL) on LiDAR Imagery Dataset
42:10 - C3 - Workflow 1 : Setting up and Exploring the Dataset
46:23 - C3 - Workflow 2 : Training the DL Classification Model using a Sample Dataset
51:31 - C3 - Workflow 3 : Examining the Output of the Sample-Trained DL Classification Model
53:27 - C3 - Workflow 4 : Training the DL Classification Model using a Large Dataset
58:12 - C3 - Workflow 5 : Extracting Power Lines from the LiDAR Point Cloud Output
59:46 - Summary Note & Contact Us

[@mapmyops]

TIMESTAMPS:
00:00 - Headline
00:05 - Case Details
00:19 - Caselet 1 - Extracting 3D Building Footprint from LiDAR Imagery Dataset
00:23 - C1 - Workflow 1 : Setting up & exploring the dataset
03:43 - C1 - Workflow 2 : Classifying the LiDAR Imagery Dataset
10:44 - C1 - Workflow 3: Extracting Buildings Footprint
14:12 - C1 - Workflow 4: Cleaning up the Buildings Footprint
17:25 - C1 - Workflow 5: Extracting 'Realistic' 3D Building Footprint
20:47 - Caselet 2 - Extracting Roof Forms from LiDAR Imagery Dataset
20:51 - C2 - Workflow 1 : Setting up the Data & Creating Elevation Layers
30:16 - C2 - Workflow 2 : Creating 3D Buildings Footprint
33:54 - C2 - Workflow 3 : Checking Accuracy of Building Footprints & Fixing Errors
42:06 - Caselet 3 - Classifying Power Lines using Deep Learning (DL) on LiDAR Imagery Dataset
42:10 - C3 - Workflow 1 : Setting up and Exploring the Dataset
46:23 - C3 - Workflow 2 : Training the DL Classification Model using a Sample Dataset
51:31 - C3 - Workflow 3 : Examining the Output of the Sample-Trained DL Classification Model
53:27 - C3 - Workflow 4 : Training the DL Classification Model using a Large Dataset
58:12 - C3 - Workflow 5 : Extracting Power Lines from the LiDAR Point Cloud Output
59:46 - Summary Note & Contact Us