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Infrastructure Computer Vision

Authors: Ioannis Brilakis, Carl Thomas Michael Haas Publisher: Elsevier Science Publication date: 2019 Publication language: Angielski Number of pages: 408 Publication formats: EAN: 9780128172582 ISBN: 9780128172582 Category: Technology, engineering, agriculture Publisher's index: 9780128172582 Bibliographic note: Dr Haas’ research, teaching and consulting are in the areas of construction technology and the circular economy in the built environment. He has received numerous research and teaching awards. He serves on a number of editorial boards and on professional committees for organizations such as the American Society of Civil Engineers (ASCE), the Natural Sciences and Engineering Research Council (NSERC) of Canada and the International Association for Automation and Robotics in Construction (IAARC). His research has been supported by numerous companies as well as agencies such as TxDOT, MTO, NSERC, NSF, and the CRC. He is a member of the Canadian Academy of Engineering and a Fellow of the ASCE. He was elected to the US National Academy of Construction in 2013. In 2014 received the CSCE (Canadian Society of Civil Engineers) Walter Shanly Award for outstanding contributions to the development and practice of construction engineering in Canada. In 2015 he received the ASCE Peurifoy Construction Research Award, the premier international career award in construction research. In 2017, he received the University of Waterloo Award of Excellence in Graduate Supervision. In 2019, he received the ASCE Computing in Civil Engineering Award, as well as the CSCE Alan Russell Award.


Infrastructure Computer Vision delves into this field of computer science that works on enabling computers to see, identify, process images and provide appropriate output in the same way that human vision does. However, implementing these advanced information and sensing technologies is difficult for many engineers. This book provides civil engineers with the technical detail of this advanced technology and how to apply it to their individual projects.

  • Explains how to best capture raw geometrical and visual data from infrastructure scenes and assess their quality
  • Offers valuable insights on how to convert the raw data into actionable information and knowledge stored in Digital Twins
  • Bridges the gap between the theoretical aspects and real-life applications of computer vision

Author's affiliation

Ioannis Brilakis: Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Carl Thomas Michael Haas: Department of Civil and Environmental Engineering, University of Waterloo, Ontario, Canada