## 1 Introduction
In the field of disaster safety systems for the prevention and prediction of large disasters such as natural disasters, fires, collapses, and environmental pollution accidents, simulation in the actual environment is often difficult. Therefore, a digital twin system that can simulate various scenarios by utilizing twins in the real and virtual worlds is necessary. A DT-based disaster safety management integrated platform study was co-planned at the Electronics and Telecommunications Research Institute (ETRI) in 2019, and research on the DT-based underground common area fire disaster support integrated platform commenced in 2020 [9]. This research used an integrated fire detection multi-sensor in the cavity and a fixed high-resolution LiDAR on the platform to detect irregularities and disaster locations in the cavity pre-space information, and then performed an intelligent escape induction method based on these details. [PERSON] [10; 11] suggested a simulated aerial firefighting activity DT framework after modeling the aircraft dropping of fire retardants in a fire scenario. A machine learning algorithm (MLA) model was optimized using DT simulation in the proposed method to quickly identify the optimal aircraft dynamics for maximizing the fire retardant release effectiveness while tracking the trajectory of airborne substances emitted from controlled aircraft. DT system studies on fire and catastrophe safety management are still in the early stages. It is especially important to investigate DT systems in early detection and reaction situations for massive fire prevention. Using a convolutional neural network (CNN), [PERSON] et al. [12] created a flame and smoke detection model intended for indoor use. The majority of early vision-based smoke detectors employed inference algorithms based on simple feature representations [13; 14]. It is important to note that the presented technique has various shortcomings, including the misclassification of clouds as smoke from flames. Researchers raised the issue of insufficient high-quality data for detecting fires and smoke in study archives in [14]. Owing to the unique and challenging nature of collecting learning data in multiple situations for fires, it is cited as one of the difficult tasks in increasing accuracy using deep learning models. There are two types of fire detection model studies. The initial step is to transform the fire status of the entire image using the classification model, followed by finding fire objects in the image using the object detection model and rebinarizing and reclassifying objects using the Backbone classification model. [PERSON] et al. [15] proposed a model that combines GoogleNet's Inception [16] block and ResNet's Skip connection [17] to detect fires in underground communities in dark environments. They enhanced the biased learning effect by categorizing the learning data as the target and supporting data, allowing the model to recognize the light source and geometric properties of the fire. Furthermore, both low-light scenarios, such as underground settlements and typical situations, were collected, and the model was learned using 10,200 target data points and 14,850 supporting data points. [PERSON] et al. [18] developed a FireSSD model based on a single shot detector (SSD) appropriate for edge devices. They added Residual connection and group convolution to the SqueezeNet-based SSD model and used the Wide Fire module, Dynamic Mbox detection layer, normalization, and Dropout modules to improve the accuracy while maintaining real-time performance in the CPU. In these tests, the FireSSD model achieved an approximately 70.6 mAP performance on the Pascal VOC 2007 dataset, identifying processing speeds that were almost six times faster than the SSD300 model. [PERSON] et al. [19] presented two Convolutional Neural Network (CNN) designs, NasNet-A-OnFire and ShuffleNetV2-OnFire, with low complexity for detecting the boundaries of fire pixel areas in image space. They employed [PERSON] et al.