TY - JOUR
T1 - An application of metadata-based image retrieval system for facility management
AU - Ma, Jong Won
AU - Czerniawski, Thomas
AU - Leite, Fernanda
N1 - Funding Information:
This research was supported by the National Science Foundation Civil Infrastructure Systems Grant 1562438. Their support is gratefully acknowledged. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/10
Y1 - 2021/10
N2 - For facility management, photography is an efficient and accurate method of recording the physical state of infrastructure. However, without an effective organizational scheme, the difficulty of retrieving relevant photos from historical databases can become overly burdensome for highly complex or long-lived assets. To make strategic decisions, it is crucial to retrieve the right information from a plurality of sources in a timely manner. The main objective of this paper is to present a method for organizing and retrieving photos from massive facility management photo databases using photo-metadata: photographed location, camera perspective, and image semantic content information. Indoor localization experiments were performed using Bluetooth technology to infer the location information. Perspective is inferred from the device's on-board inertial measurement unit (IMU). Image semantic content is inferred using a Convolutional Neural Network (CNN)-based deep learning algorithm. Fusing these three features, seven query options were provided for the user when retrieving images. Leveraging Building Information Modeling (BIM) as a process and Geographic Information Systems (GIS) as a framework, this paper also envisions a federated information management by connecting 2D and 3D facility assets with our real-world map which can be smoothly bridged with our image retrieval system. The realization of the integrated application with BIM and GIS is significantly beneficial for the facility management domain by advancing the understanding of projects in a broader view with a federated data platform. In this research, the framework is illustrated with 21 institutional buildings within the University of Texas at Austin's main campus, and the authors conclude that the proposed metadata-based image retrieval system can ultimately enhance the better-informed decision-making process through rapid information retrieval.
AB - For facility management, photography is an efficient and accurate method of recording the physical state of infrastructure. However, without an effective organizational scheme, the difficulty of retrieving relevant photos from historical databases can become overly burdensome for highly complex or long-lived assets. To make strategic decisions, it is crucial to retrieve the right information from a plurality of sources in a timely manner. The main objective of this paper is to present a method for organizing and retrieving photos from massive facility management photo databases using photo-metadata: photographed location, camera perspective, and image semantic content information. Indoor localization experiments were performed using Bluetooth technology to infer the location information. Perspective is inferred from the device's on-board inertial measurement unit (IMU). Image semantic content is inferred using a Convolutional Neural Network (CNN)-based deep learning algorithm. Fusing these three features, seven query options were provided for the user when retrieving images. Leveraging Building Information Modeling (BIM) as a process and Geographic Information Systems (GIS) as a framework, this paper also envisions a federated information management by connecting 2D and 3D facility assets with our real-world map which can be smoothly bridged with our image retrieval system. The realization of the integrated application with BIM and GIS is significantly beneficial for the facility management domain by advancing the understanding of projects in a broader view with a federated data platform. In this research, the framework is illustrated with 21 institutional buildings within the University of Texas at Austin's main campus, and the authors conclude that the proposed metadata-based image retrieval system can ultimately enhance the better-informed decision-making process through rapid information retrieval.
KW - Bluetooth
KW - Building Information Model
KW - Facility management
KW - Geographical Information System
KW - Image retrieval system
KW - Indoor localization
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U2 - 10.1016/j.aei.2021.101417
DO - 10.1016/j.aei.2021.101417
M3 - Article
AN - SCOPUS:85115015143
SN - 1474-0346
VL - 50
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
M1 - 101417
ER -