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Authors: Mazharul Hossain 1 ; Tianxing Ma 1 ; Thomas Watson 2 ; Brandon Simmers 2 ; Junaid Ahmed Khan 3 ; Eddie Jacobs 2 and Lan Wang 1

Affiliations: 1 Department of Computer Science, University of Memphis, TN, U.S.A. ; 2 Department of Electrical and Computer Engineering, University of Memphis, TN, U.S.A. ; 3 Department of Electrical and Computer Engineering, Western Washington University, WA, U.S.A.

Keyword(s): LiDAR Point Cloud, Indoor Object Detection, Image Dataset, 3D Indoor Map, Public Safety Objects.

Abstract: An accurate model of building interiors with detailed annotations is critical to protecting the first responders’ safety and building occupants during emergency operations. In collaboration with the City of Memphis, we collected extensive LiDAR and image data for the city’s buildings. We apply machine learning techniques to detect and classify objects of interest for first responders and create a comprehensive 3D indoor space database with annotated safety-related objects. This paper documents the challenges we encountered in data collection and processing, and it presents a complete 3D mapping and labeling system for the environments inside and adjacent to buildings. Moreover, we use a case study to illustrate our process and show preliminary evaluation results.

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Paper citation in several formats:
Hossain, M.; Ma, T.; Watson, T.; Simmers, B.; Khan, J.; Jacobs, E. and Wang, L. (2021). Building Indoor Point Cloud Datasets with Object Annotation for Public Safety. In Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS; ISBN 978-989-758-512-8; ISSN 2184-4968, SciTePress, pages 45-56. DOI: 10.5220/0010454400450056

@conference{smartgreens21,
author={Mazharul Hossain. and Tianxing Ma. and Thomas Watson. and Brandon Simmers. and Junaid Ahmed Khan. and Eddie Jacobs. and Lan Wang.},
title={Building Indoor Point Cloud Datasets with Object Annotation for Public Safety},
booktitle={Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS},
year={2021},
pages={45-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010454400450056},
isbn={978-989-758-512-8},
issn={2184-4968},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Smart Cities and Green ICT Systems - SMARTGREENS
TI - Building Indoor Point Cloud Datasets with Object Annotation for Public Safety
SN - 978-989-758-512-8
IS - 2184-4968
AU - Hossain, M.
AU - Ma, T.
AU - Watson, T.
AU - Simmers, B.
AU - Khan, J.
AU - Jacobs, E.
AU - Wang, L.
PY - 2021
SP - 45
EP - 56
DO - 10.5220/0010454400450056
PB - SciTePress

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