When it comes to big data, LIDAR is right up there with the biggest generators. But outside of a few niche use cases, LIDAR–which uses lasers to build a three-dimensional model of physical objects–has not been widely adopted. Now, a San Francisco startup called Enview is hoping to change that with their Web-based AI service for analyzing LIDAR data.
From an article in Datanami by Alex Woodie.
San Gunawardana is imminently familiar with LIDAR (which stands for Light Detection and Ranging) and other types of geospatial data. He started his career in the U.S. Air Force, where he built satellites, then moved to Stanford University, where he earned his PhD in aerospace engineering in 2012. He received funding from NASA to develop computer vision and NLP technology for UAVs, then went “downrange” to Afghanistan as a civilian with the Army before returning to the United States to co-found Enview.
Along the way, Gunawardana developed an appreciation for not only the massive volume of LIDAR data (particularly when it’s collected at its highest fidelity), but also the huge potential the LIDAR has to benefit companies, people, and society.
“LIDAR technology has been around for decades,” Gunawardana tells Datanami, “but there are some big changes happening that are really revolutionizing what we can do with this.”
The costs associated with building and using LIDAR systems are coming down, and LIDAR is being used in more applications. For example, the latest generation iPhones and iPads feature LIDAR sensors, which are intended to be used with augmented reality applications.
“It’s becoming easier and cheaper to collect more and more data,” Gunawardana says. “But we don’t, and the reason we don’t is because the data is extraordinarily complex. It’s large in scale. It’s a true big data problem.”
Enview works with companies in the electricity and natural gas distribution industry to analyze LIDAR images of power lines and pipelines. LIDAR can identify anywhere from 50 million to 250 million objects per mile, and the objects in these “point clouds” historically have been manually labeled.
“There have been incredible advances in 2D computer vision and image processing,” Gunawardana says. “The challenge is, the entire body of literature for image processing is based on the assumption that you have structured data. Unfortunately, LIDAR data is unstructured, so you can’t just throw a pile of this into your typical AI framework and have it work.”
What Enview has done is develop an AI system that automates the processing of LIDAR data. Running atop a collection of CPUs and GPUs, its convolutional neural network can identify real-world objects embedded in “3D point clouds,” such as power poles, pipelines, buildings, bridges, trees, and vehicles.
Up to this point, Enview has utilized this AI system as part of consulting engagements. With today’s launch of Enview Explore, the company is now exposing the AI to a wider audience via the Web. Anybody with a Web browser can now load their LIDAR data into Enview’s AI system and receive the output in a faster and automated fashion.
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