Abstract:

This study provides a potential solution to improve productivity in the facility management industry. Accurate and updated building asset information is crucial in ensuring facilities management’s effectiveness and efficiency. Maintenance and operation require accurate building asset information to improve the maintenance process. An automated solution has earlier been developed to extract data from the equipment asset list in PDF files to the AIR template in excel. However, to ensure that the extracted information from the equipment asset list is accurate and updated, the AIR template must be verified against the onsite images. The current practice in the industry involves the manual entry of information from images and the manual verification process. This practice requires more staff and time needed to consolidate the image information. The error rate also increases with the increasing number of image information to consolidate. An automated solution is developed and tested to extract data from images and verify the AIR template for PDF files in this study. The solution aims to reduce staff and time needed to complete the task without compromising accuracy. The developed solution’s test results show that it can reduce the time taken significantly and improve accuracy during data collection and verification. The survey done after the test provides insights and possible improvements for the developed solution.

Keywords: Asset Information Requirements (AIR), Digital Facility Management, Productivity, Python, Automated Image Retrieval

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Cite as: “Kwok NQ, Fadeyi MO, and Low MYH (2021). Automated retrieval of photo data for asset information requirements (AIR) input using Python. Built Environment Applied Research Sharing #06. ISSUU Digital Publishing.”