Semantic BIM enrichment for firefighting assets: Fire-ART dataset and panoramic image-based 3D reconstruction

TL;DR

This paper introduces the Fire-ART dataset and a panoramic image-based reconstruction method to improve semantic enrichment and 3D modeling of firefighting assets within BIM, enhancing emergency response and asset management.

Contribution

It provides a new extensive dataset and a novel panoramic reconstruction approach that improves recognition and localization accuracy of firefighting assets in BIM models.

Findings

Fire-ART dataset contains 2,626 images and 6,627 instances of firefighting assets.

Reconstruction approach achieves F1-scores of 73% and 88% in validation.

Localization errors are reduced to approximately 0.4-0.6 meters.

Abstract

Inventory management of firefighting assets is crucial for emergency preparedness, risk assessment, and on-site fire response. However, conventional methods are inefficient due to limited capabilities in automated asset recognition and reconstruction. To address the challenge, this research introduces the Fire-ART dataset and develops a panoramic image-based reconstruction approach for semantic enrichment of firefighting assets into BIM models. The Fire-ART dataset covers 15 fundamental assets, comprising 2,626 images and 6,627 instances, making it an extensive and publicly accessible dataset for asset recognition. In addition, the reconstruction approach integrates modified cube-map conversion and radius-based spherical camera projection to enhance recognition and localization accuracy. Through validations with two real-world case studies, the proposed approach achieves F1-scores of 73% and 88% and localization errors of 0.620 and 0.428 meters, respectively. The Fire-ART dataset and the reconstruction approach offer valuable resources and robust technical solutions to enhance the accurate digital management of fire safety equipment.