This blog focuses on the data integration part, which is done with FME. Working with point clouds involves a few layers of technology: a LiDAR scanner, a place to store the point cloud data it collects, and a data integration platform to process and analyze it. 5 Ways to Improve Your LiDAR Workflows But first: LiDAR technology layers Analyzing a point cloud through calculations and expressions.Extracting or filtering out certain points via clipping, splitting, and more.Preprocessing a point cloud by updating values/components, reducing the size, or changing the structure.to make a colourized 3D model or generate an easily shared map Integrating point clouds with other data, e.g.Let’s look at how companies all over the world are using LiDAR and how you can use data integration workflows to transform, map, and process your point cloud data. You’ll find LiDAR scanners across many industries, from aerospace to telecom to utilities. Point clouds today are denser, higher quality, and ubiquitous. Today, point clouds typically represent landscapes, buildings, objects, and more with millimetre precision. In the original post, I noted how in 2007, our users were working with point clouds representing one point per square meter, while at the time of writing, our users had data representing eight points per square meter. Over 120 posts later, it’s time to revisit this awesome data type. Fun fact: that was my first blog at Safe. With LiDAR sensors showing up in smart cities, UAVs, film production, cars, phones, and much more, point cloud data has evolved significantly since the last time I blogged about it in 2013.
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