Bluesky funds study into use of LiDAR for detailed tree analysis

by | Oct 14, 2020 | Featured Slider, Latest, News

Bluesky International is partnering with the University of Leicester to co-fund an advanced postgraduate degree study into the use of LiDAR, machine learning and artificial intelligence to create citywide maps of trees enriched with new levels of information.

A specialist in aerial mapping, Bluesky uses aircraft mounted lasers (LiDAR) to capture millions of highly accurate measurements called point clouds that detail the earth’s surface and features. Bluesky has already pioneered nationwide maps detailing the location, height and canopy cover of trees and this R&D project is expected to result in an automated workflow for the extraction of tree structural features and the classification of tree species.

“We have recently upgraded the sensor used to capture LiDAR point clouds so we have more detailed and accurate measurements than ever before. We are therefore exploring how artificial intelligence and machine learning can be applied to extract additional intelligence from this data in the most efficient, accurate and cost effective way,” comments James Eddy, technical director at Leicestershire based Bluesky.

“By partnering with the University of Leicester we hope to exploit its expertise across natural science and data science to develop automated algorithms for processing this data to identify specific tree structures and classify species to augment our National Tree Map datasets.”

The Bluesky proposed study, titled ‘High density point cloud processing for tree structural features extraction and species classification’, will result in a Master of Philosophy (MPhil) qualification. Offered by Leicester Innovation Hub, part-funded by the European Regional Development Fund, and delivered by the School of Mathematics and Actuarial Science and the School of Geography, Geology and the Environment at the University of Leicester, it is hoped the outputs of the project will include an annotated software code for tree identification, feature extraction and species classification, and a synthetic technical documentation on its algorithmic.

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