Research Article
Towards Construction Progress Estimation Based on Images Captured on Site
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@INPROCEEDINGS{10.1007/978-3-030-71061-3_9, author={Peter Hevesi and Ramprasad Chinnaswamy Devaraj and Matthias Tsch\o{}pe and Oliver Petter and Janis Nikolaus Elfert and Vitor Fortes Rey and Marco Hirsch and Paul Lukowicz}, title={Towards Construction Progress Estimation Based on Images Captured on Site}, proceedings={Industrial IoT Technologies and Applications. 4th EAI International Conference, Industrial IoT 2020, Virtual Event, December 11, 2020, Proceedings}, proceedings_a={INDUSTRIALIOT}, year={2021}, month={7}, keywords={Construction progress estimation Neural networks Computer vision}, doi={10.1007/978-3-030-71061-3_9} }- Peter Hevesi
Ramprasad Chinnaswamy Devaraj
Matthias Tschöpe
Oliver Petter
Janis Nikolaus Elfert
Vitor Fortes Rey
Marco Hirsch
Paul Lukowicz
Year: 2021
Towards Construction Progress Estimation Based on Images Captured on Site
INDUSTRIALIOT
Springer
DOI: 10.1007/978-3-030-71061-3_9
Abstract
State of the art internet of things (IoT) and mobile monitoring systems promise to help gathering real time progress information from construction sites. However, on remote sites the adaptation of those technologies is frequently difficult due to a lack of infrastructure and often harsh and dynamic environments. On the other hand, visual inspection by experts usually allows a quick assessment of a project’s state. In some fields, drones are already commonly used to capture aerial footage for the purpose of state estimation by domain experts.
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