LIDAR urban scene infrastructure asset feature extraction

Canadian Communities are facing a tidal wave of physical infrastructure debt as their physical assets deteriorate due to age. This project aims to use urban LiDAR data (“streetscapes”) and computer vision to identify key physical assets such as (signs, curbs, centerline roads, streetlights, and other features) by there location (latitude / longitude) and key physical characteristics (size (height, width, length, thickness) and other characteristics. The objective is to use advanced technology to speed up the data collection process to aid in the identification of what assets are managed and their location.

Faculty Supervisor:

Irene Cheng

Student:

Gabriel Lugo Bustillo

Partner:

McElhanney Consulting Services Ltd

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Alberta