A Study of Applying BIM Technique into Fire Disaster Investigation System

Lin, Lian-Shin; Hsu, Yu-Huai; Hsiao, Yu-Chen

doi:10.1109/icmtma.2016.17

Cited by 2 publications

(1 citation statement)

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“…By incorporating the actual, real-time sensing data, as we propose to do in our approach, the system could be used for situational aware evacuation guiding and localization during a real fire. Lin et al [10] on the other hand, used a 3D BIM-model for fire cause investigation and visualization. Their work only focuses on presenting different kinds of data for fire disaster analysis and no combination/interaction is made between the sensor data and the building model.…”

Section: Building Models For Fire Investigationmentioning

confidence: 99%

Fireground location understanding by semantic linking of visual objects and building information models

Vandecasteele

Merci

Verstockt

2017

Fire Safety Journal

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This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and lowcost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi-)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding.

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Section: Building Models For Fire Investigationmentioning

confidence: 99%