## 1 Introduction  
Among the disasters that might occur in a city, fire accidents can gradually spread their harm, causing property loss as well as damage to human life. A National Fire Agency study [1] states that there were 38,659 fires in Korea in 2020. Buildings and structures accounted for 64.5 % of all fire events, of which 82.2, 82.6, and 88.7% resulted in fatalities, injuries, and property damage, respectively. Therefore, fire prevention requires considerable effort, and it is very important to realize this as soon as possible if a fire has not been prevented. Many studies on smart cities are being conducted to address these fire risks by fusing technologies such as artificial intelligence and the Internet of Things (IoT). The term \"smart city\" refers to an urban plan developed by the Ministry of Land, Infrastructure and Transport that uses modern technologies such as ICT big data to address urban issues and enhance quality of life [2]. According to urban population density, this urban plan is supposed to address a variety of social issues such as disasters, traffic jams, energy, and environmental pollution. One of the technologies used in smart cities that is gaining interest is digital twin technology, which reproduces the world using twins that reflect the real world in a digital virtual space by sending data on the physical space gathered via IoT [3]. A digital twin is not the name for a specific piece of IT hardware. This concept spans a wide range of technologies, including sensors that gather information from actual physical environments; augmented reality (AR), which enhances the real world; and virtual reality (VR), which creates a virtual world [4]. [PERSON] et al. [5] created a distributed feedback carbon monoxide (CO) sensor based on lasers utilized for early fire detection and tested its reliability with various trials. [PERSON] et al. [6] investigated and proposed an early fire detection approach based on a gas turbulent diffusion (GTD) model and particle swarm optimization (PSO). The test results showed that the sensor system performed well in terms of fire detection and location. [PERSON] et al. [7] suggested a quick and low-cost indoor fire alarm system that could detect fires, carbon monoxide, smoke, temperature, and humidity in real time and effectively perform data processing and classification. These sensor-based detection networks have limitations in the coverage of the sensors involved, making them difficult to apply in early fire detection. Meanwhile, with the recent development of data-driven artificial intelligence technologies such as deep learning, image-based initial fire detection methods have the advantage of being able to quickly recognize various types of fire-situation-related information, such as fire scale, compared to previously introduced fire monitoring sensors. Furthermore, by leveraging existing CCTVs, installation costs can be reduced, and the rate of misleading dispatch can be minimized by detecting the fire condition of the site in advance before deploying the alarm. However, using image-based deep learning models like CNNs requires a significant amount of data to train the model, but relatively few data exist, and they cost a lot of money to develop. Furthermore, it is safe to presume that no fire data exist for the early detection of fires--the goal pursued in this work. The development of synthetic learning data via digital twins is one way to overcome the problem of this learning data shortage and create high-quality learning data that can be deployed in the field. In this paper, we propose an early fire detection system using a digital-twin-based autonomous learning data generation model that can detect fires via image recognition sensors such as CCTVs. In 2020, [PERSON] et al. [8] defined the concept of a digital twin (DT) as a two-way mutually influential relationship between a physical object and a digital object and presented DT technologies and tasks in fields such as manufacturing, healthcare, and smart cities.