Lidar is a powerful tool for understanding the world and can be wielded to provide accurate information for important and sensitive use cases – from capturing as-builts to performing inspections and more. While the process of data collection, as well as the sensors available are rapidly improving, processing and managing the data generated can remain a challenge. This session showcases advances in lidar processing in real world projects. Hear from the professionals responsible for going from the field to providing the finished deliverable – and how they got there. 

Session moderated by Martin Flood, GeoCue Group

Mobile Lidar Point Cloud Processing Calls for both Innovation and Classic Techniques.

There is a big difference between the Mobile Lidar Data Derivatives needed by the decision makers in the office, compared to the boots (with the glasses) on the ground. What is appropriate, what do we need to do to get there, and how is this shaping the market? 

With a shift in who is creating point cloud data and using Lidar systems, the fields in which this data and post processed derivatives is utilized, and no clear consensus on standardization… yet… there is a demand for creative software and tools…still! 

Many commercial solutions are designed for aerial lidar, or BIM with no real world coordinates… all of the best programs still have their shortcomings… and its quite probable that the teammate you need to explain that the most to has no idea what you are talking about. 

With some notes on data management, my never ending call for interoperability, and an arrow pointing to day 1 of GIS or RS 101, my presentation will direct your thoughts to the intro course that told you about spectral and spatial patterns, the first rule of geography, and why there is a 'red exclamation point' next to your broken file path. A walk along the Data>Information>Knowledge trail will also assist in processing mobile lidar point clouds down to their appropriate product… unless they are being built up to an immersive AR/VR/XR experience to be shared via… "wait how are we sharing this massive data?"

Taylor Handschuh, The Map Lady, LLC

Pros and Cons of Using Point Clouds or 3D Meshes to View or Consume Reality Capture Data

3D laser scanning was invented in the 1960s and started to be used for design and engineering in the 1990s. Since the beginning, the discrete nature of the laser scanning technology has driven the way the resulting 3D data would be exposed to the user. Therefore, viewing your as-built conditions as a point cloud has become the norm. But our world is made of surfaces, not 3D points! The scanning sensor should not dictate the way we view the captured information since viewing and consuming a point cloud is not an easy task. Seeing through the dots, this type of data is difficult to interpret by non-experts. But 3D meshes may not be the answer to all those issues either since many modeling apps have been designed to consume point clouds, not meshes.

Rob Rasnic, Cintoo

Successful Automation of the Entire Mobile Lidar/Imagery Data Workflow, From Capture to Online Viewing and Feature Extraction

Mobile Lidar Data processing is a long and complex process. Taking the raw data of the Lidar scanners, cameras, and IMU units and turning it into a georeferenced point cloud and matching imagery used to require multiple human interventions, long processing times and lots of available disk space. This might not be such an issue when dealing with specific projects that amount to a few days of scanning. Jakarto's business model is different, and because the scope of Jakarto's project is at the scale of a city, or a state, and ultimately a country, the need to entirely automate and have the ability to easily scale up the processing power and space quickly became crucial.

It took a few years of development but the team over at Jakarto can now pride itself on having successfully created a fully automated mobile Lidar Geoprocessing platform. This platform not only takes the raw data and turns it into georeferenced Lidar and Imagery, but also makes that data available online through a web based point cloud and imagery viewer. Point cloud classification, image recognition, and feature extraction are also incorporated into this platform.

This presentation will focus on the various hardware integration challenges of a mobile Lidar/Imagery solution, as well as sharing the different processes of the fully automated workflow that leads from the raw data to the processed data and ultimately its availability on the cloud for viewing and downloading.

Thierry Baulu, Jakarto HD Mapping

Centralized Lidar Programs: Capture, Processing, Storage & Analytics

As one of the nation's largest electric utilities, Southern California Edison (SCE) provides electricity service to more than 15 million people in a 50,000 square-mile area of central, coastal and Southern California. SCE manages 12,635 miles of Transmission lines and 91,375 miles of distribution lines. Each year a little over 5000 miles of T&D lines are scheduled to be captured by Lidar. Of these 58% of the coverage are contained in the high fire risk areas. This data is used by Vegetation Management, Engineering Transmission, Asset Strategy and inspections.

Critical asset & infrastructure bearing organizations commonly have disparate programs across their operating units that each desire Lidar data for varying use cases. By centralizing Lidar use cases and consolidating siloed project scopes to a common optimized set of collection, processing & analytics requirements, organizations stand to benefit substantially through:

1. Elimination of redundant capture of the same assets

2. Streamlined procurement for best-in-class service providers & pricing

3. Creation of organizational 3D digital asset data to help project teams visualize & analyze their assets for their respective purposes

Daniel Bellissemo, GIS Surveyors, Inc and Jon Leist, Deloitte