Customer Stories

Investments in fifth generation (5G) wireless infrastructure are estimated to exceed $325 billion by 2025; operator revenues are forecasted to reach US $1.3 trillion. To tap into that revenue stream telecom companies need to have accurate, detailed and up-to-date mapping of the markets they serve. One Colorado-based company is combining geospatial imagery and Trimble's eCognition technology to provide that essential business intelligence in 3D. The approach is giving companies telecom-tailored information that will help them compete in the race for a future that is predicted to be lightning fast. overview Location COLORADO TRANSFORMING THE WAY THE WORLD WORKS TRANSFORMING THE WAY THE WORLD WORKS One of the keys to unlocking 5G's fast future is the network's use of millimeter wave technology, which uses shorter, higher frequency wavelengths, which can mean faster data transmissions. To achieve the faster wireless speeds, telecom companies will need a notably denser array of small antennas, which require line-of-sight for transmission to work. Companies have to figure out how to ensure the signal gets from the next generation of 5G base stations to wireless devices. To help resolve this critical challenge, multiple US telecom companies have tasked Land Info Worldwide Mapping, an aerial and satellite data provider, with creating customized 3D models of select areas of interest (AOIs). They specifically required layered views of 3D building footprints, classified land cover and classified tree contours and their heights—the critical information to analyze line-of-sight potential for 5G sensors and determine the optimum locations for their infrastructure and accelerate their rollout. Guided by the telecom companies' lists of AOIs nationwide and their mapping requirements, Chris Lowe, Land Info's director of imagery analysis, created the city models. He obtained 1-m multispectral aerial imagery from the USDA's National Agriculture Imagery Program (NAIP) and aerial-derived DTM and DSM data, sourced from either lidar or SGM (Semi-Global Matching.) And for the vector datasets, Lowe sourced building footprints, water polygons and roads. He also used the DTM and DSM to create a normalized DSM (nDSM), which would provide key elevation data for classifying trees and buildings and calculating their heights.

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