In the maintenance domain of the construction industry, digital twins have been actively introduced based on the technologies of the Fourth Industrial Revolution with growing interest in three-dimensional spatial information facility management and disaster response service using digital twin technology. In particular, disasters or abnormal situations in an underground utility tunnel (UTU) can cause serious casualties and property damage since key elements of a city such as power, communications, water supply, and heating facilities are collectively accommodated in a certain underground space. This study established the methodology for implementing an underground utility tunnel through a digital twin. The novelty of this study is demonstrating a sequential procedure of implementing digital twin technology by configuring major layers such as data acquisition, modeling, and service. This methodology can serve as reference material or a training aid for implementing a digital twin of underground facilities or underground utility tunnels in the future; the proposed methodology will be verified when an actual digital twin service is provided.

The increasing interest in digital twin technology, the digitalization of worn-out social overhead capital (SOC), and disaster management services has augmented the usage of 3D spatial models and information to manage infrastructure. In this study, a digital twin of a subterranean utility tunnel was created, and spatial objects were identified using inbuilt image sensors. The novelty lies in the development of a unique algorithm that breaks down the structure of the utility tunnel into points, lines, and planes, identifying objects using a multimodal image sensor that incorporates light detection and ranging (LiDAR) technology. The three main conclusions of this study are the following: First, a digital twin of the utility tunnel was constructed using building information modeling integrated with a geographic information system (BIM-GIS). Second, a method for extracting spatial objects was defined. Third, image-sensor-based segmentation and a random sample consensus (RANSAC) were applied. In this process, the supplementary algorithm for extracting and updating 3D spatial objects was analyzed and improved. The developed algorithm was tested using point cloud data, showing easier object classification with more precise LiDAR data.

Digital twin technology can be used for disaster safety management of underground utility tunnels, which are social infrastructures. To verify its efficiency in an underground utility tunnel, it must be implemented and monitored in the real world. Therefore, the state of information about an object and the position of an object in the real world, which can change upon movement, should be correspondingly reflected in the digital model. Underground utility tunnels are monitored through closed circuit television (CCTV) cameras because the main infrastructure facilities are installed in a long and narrow form, which limits access to them. Therefore, synchronization of a digital twin model of an underground utility tunnel requires a method for identifying the location of an object through an installed CCTV camera. This study compares the DenseDepth transfer learning method and the coordinate system conversion method through floor plan projection to estimate depth using a single CCTV camera installed in an underground utility tunnel. The coordinate system conversion method through floor plane projection showed an error of 0.23 m to 1.5 m within a 50 m range. The DenseDepth transfer learning method showed an error of 1 m to 3 m; therefore, it was considered unsuitable for distance estimation. The coordinate system conversion method through floor plane projection showed a smaller error even at a longer distance than the DenseDepth transfer learning method. Therefore, the coordinate system conversion method through floor plane projection was considered more suitable than the DenseDepth transfer learning method for depth estimation using a single camera.

Low or medium spatial resolution satellite images are used for environmental monitoring in remote, extremely cold areas such as Antarctica. However, they cannot provide detailed spatial and spectral information over Antarctic areas. For obtaining this information, we propose an Antarctic land-cover classification method using IKONOS and Hyperion data over Terra Nova Bay, Antarctica. High spatial resolution IKONOS imagery enabled the detection of detailed, accurate boundaries between areas of snow, ice, vegetation, water and rock. Rock types were classified in detail using high spectral resolution Hyperion imagery. Because Antarctic land-cover types exhibit unusual spectral features, a step-by-step classification using various data sets and methods was applied. Five soil types and two rock types were identified by applying spectral linear unmixing to Hyperion data. Our land-cover classification map shows small mossy areas near emperor penguin or skua habitat. Lakes, which are an important factor in environmental monitoring, are also clearly shown. The detailed rock and soil classification map revealed that biotite gneiss and clay soils are dominant. This Antarctic land-cover map is useful for determining a suitable site for an environment-friendly Antarctic station. Among the various land-cover types, the lakes and the vegetation, rock and soil areas are considered to be important environmental factors related to Antarctic living organisms.

The growing complexity of urban environments and high-rise facilities presents new challenges for disaster preparedness and response, particularly when managing multiple hazards. Traditional systems that focus on single hazards are insufficient for complex facilities that are prone to cascading disasters. This study develops an open disaster information platform that integrates Building Information Modeling (BIM), Geographic Information Systems (GIS), and real-time monitoring tools to enhance situational awareness and multi-hazard response coordination. The platform combines data from the internet of things’ sensors, structural models, and environmental systems to provide responders and facility managers with real-time access to critical information. Simulation tests and real-world deployments have confirmed the platform’s ability to optimize evacuation routes, improve response times, and minimize risks during emergencies. Integration with GIS further supports risk mapping and post-disaster recovery efforts. This study proposes a scalable disaster management framework that promotes real-time data sharing and collaboration across stakeholders. Aligned with the trend toward smart, resilient cities, the platform provides practical solutions for improving disaster preparedness and response in high-rise and complex urban environments.