Augmented Reality for Navigation, Team Coordination, and Real-Time Data Visualization in Firefighting Operations

© 2025 IEEE. This is the author’s pre-print version of the article that has been published in the proceedings of 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). The final version of this record is available at: 10.1109/ISMAR-Adjunct68609.2025.00041 Augmented Reality for Navigation, Team Coordination, and Real-Time Data Visualization in Firefighting Operations Konstantinos Charalampakis* School of Electrical and Computer Engineering, Technical University of Crete Ioannis Safranoglou† School of Electrical and Computer Engineering, Technical University of Crete Alexis Stavroulakis‡ School of Electrical and Computer Engineering, Technical University of Crete Marcel Ebel§ Paderborn University - Heinz Nixdorf Institute Iris Graessler¶ Paderborn University - Heinz Nixdorf Institute Despina Dimelli|| School of Architecture, Technical University of Crete Katerina Mania** School of Electrical and Computer Engineering, Technical University of Crete ABSTRACT A key challenge in wildfire management is the reliance on fragmented data, which complicates real-time decision-making for commanders in the field. While Augmented Reality (AR) shows promise, existing solutions often depend on handheld devices that occupy the user’s hands or focus on singular tasks rather than providing a comprehensive operational view. This paper presents an innovative head-worn AR system, designed to enhance situational awareness for firefighting team leaders. Our system intelligently fuses multiple live data streams, including team member GPS locations, satellite-derived fire perimeters, predictive fire spread simulations, and real-time weather data, into a single, interactive holographic interface. The system’s usability and effectiveness were evaluated at the Emergency Service Academy Finland involving 15 professional stakeholders. Feedback from these trials was positive, highlighting the system’s potential to support critical decisionmaking and improve the safety and efficiency of firefighting operations. Index Terms: Augmented Reality, Wildfire Management, Firefighting, Situational Awareness, HoloLens 2, Real-Time Data Visualization. 1 INTRODUCTION Wildfires present a dynamic and unpredictable challenge, yet field commanders often rely on fragmented data from paper maps and radio, which hinders real-time situational awareness [9]. Although Augmented Reality (AR) has emerged as a promising technology [22,6], many solutions rely on handheld devices that occupy the user’s hands [19,23,7]. Existing head-worn systems, meanwhile, often address isolated tasks such as drone control [21] or high-level coordination [25], rather than delivering a holistic operational picture to the field commander. This paper introduces a head-worn AR system for HoloLens 2 designed to provide team leaders with a cohesive, real-time operational view. Our primary contribution is the fusion and visualization *e-mail: [email protected] †e-mail: [email protected] ‡e-mail: [email protected] §e-mail: [email protected].de ¶e-mail: [email protected].de ||e-mail: [email protected] **e-mail: [email protected] of multiple live data sources, such as team GPS positions, active fire locations, fire spread predictions, and weather, into a unified, hands-free interface. Delivered via an interactive 3D map with ARguided routing, our system enhances team coordination, safety, and tactical decision-making during wildfire incidents. 2 RELATED WORK 2.1 AR for data Visualization in Emergency Response Augmented Reality (AR) has been explored in emergency response scenarios to enhance situational awareness and operational efficiency [22,6]. Several solutions leverage mobile devices; systems like THEMIS-AR [19] and others [23,7] use handheld AR to display geo-referenced information to responders. However, mobile AR systems occupy the user’s hands [15], impeding critical tasks and forcing a distracting shift of focus between the device’s screen and the physical environment. To address this, head-worn AR systems were developed [21,25], but concentrate on specific auxiliary tasks such as UAV control [21] or high-level collaboration [25]. These approaches do not focus on providing a comprehensive, datafused operational picture to the commander on the ground for realtime decision-making. In contrast, our system deploys head-worn AR for hands-free, gesture-driven interaction for wildfire commanders. It moves beyond singular tasks by fusing multiple, live data streams including team GPS locations, fire perimeters, and realtime weather into an interface to enhance on-the-ground strategic awareness. 2.2 Training and In-Field Operational Support Past research using Virtual and Augmented Reality for emergency management has focused on the pre-emergency phase, emphasizing safety training and preparedness [27,18,4]. This includes immersive fire drills combining AR and VR [13], and using VR to safely prototype and test new tools for firefighters, avoiding the complexity of real-world deployment [1]. Although crucial for improving responder skills, this work primarily addresses challenges in controlled settings, not live incidents. Past research explores in-field operational support [26], but these efforts often focus on other aspects such as high-level agency collaboration [25] or other disasters, such as real-time flood visualization [20,24]. In contrast, our work targets real-time, in-field operational support for wildfire incidents. Rather than training, our system provides a data-centric command tool for the field commander during an active operation. It enhances tactical decision-making on-the-ground by fusing live data about team positions, fire perimeters, and environmental conditions into an actionable interface. 1 © 2025 IEEE. This is the author’s pre-print version of the article that has been published in the proceedings of 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). The final version of this record is available at: 10.1109/ISMAR-Adjunct68609.2025.00041 2.3 Multi-Source Data for Critical Decision Support Effective decision-making in emergency management requires integrating and visualizing data from multiple sources to form a coherent operational picture [9,8]. Past research has focused on integrating singular data streams. For instance, certain systems monitor firefighters, using wearable sensors for real-time location and physiological data [2,16]. Other specialized work has focused on reliable indoor localization [17] or visualizing sensor data to the user [3,14]. While crucial, these approaches often provide a monocular view, focusing on either personnel or the environment. Furthermore, this information is typically displayed on 2D screens, separating the commander from the incident’s physical context [2,10]. In contrast, our system is for holistic, in-field data fusion and visualization in AR. It moves beyond single-data streams by integrating live team locations, fire spread simulation, and real-time weather data. Crucially, this system presents this information not on a remote screen, but as an interactive, three-dimensional overlay in the commander’s head-worn field of view. This approach unifies critical data into a single interface, reducing cognitive load and empowering on-the-ground tactical decision-making during wildfires. 3 SYSTEM OVERVIEW The system architecture uses a head-worn AR device to provide real-time data visualization and decision support for wildfire management. It overlays wildfire activity, team member locations, and environmental data directly in the user’s field of view to support navigation and coordination. The main components are the HoloLens 2 headset, a central server, and a companion GPS app. The HoloLens enables hands-free operation and immersive visualization, while the server manages data exchange and integrates external APIs and simulation outputs. This setup ensures unified, context-aware information is available to user in the field. The system features a dynamic 3D map interface powered by Mapbox, enabling users to navigate and interact with geospatial data. AR navigation guides users to team members or Points Of Interest (POI) using real-time routing and navigation arrows. Realtime team member tracking displays GPS positions on the map and as holographic markers in the AR scene. Active fire visualization integrates data from NASA FIRMS to show current fire locations and visualizes fire spread using the SPARK model. Weather monitoring provides real-time data, including temperature, wind speed, and direction, with AR visualization of wind vectors. 4 IMPLEMENTATION The system was developed using the Unity Engine and the Mixed Reality Toolkit 3 (MRTK3) for core user interactions and interfaces. The implementation architecture integrates varied data sources and functionalities into a cohesive AR experience, enabling the user to access and interact with vital information seamlessly. 4.1 User Interface and Interaction Design The user interface is designed for clarity and minimal cognitive load, using custom panels and MRTK3 interactions to provide quick access to essential information. It is structured around a series of panels, each serving a distinct function: a main hand-following menu, a primary map panel, a weather information panel, and two initial setup panels. To ensure persistent visibility without obstructing the user’s view of the real world, each panel follows the user’s gaze. This keeps the panel in the user’s field of view, remaining accessible without requiring additional head movements. The HandFollowing Menu is the central control panel is constrained to the user’s palm, appearing when they look at their open hand. This provides immediate, ”on-demand” access to the system’s main features without requiring the user to interact with world-space menus. Figure 1: Hand-following menu The main map panel is the system’s primary geospatial interface. It displays a 3D map with markers for the user, team members, POIs, and active fires, along with calculated navigational routes. To accommodate varying operational needs, the panel’s position and scale can be adjusted. Users can ”unlock” a map, reposition and resize it within their environment, and ”lock” it in the desired position on their field of view, depending on their preferences and current operational context. Figure 2: Main map panel The weather monitoring panel presents current and predicted meteorological data, including temperature, wind speed, and wind direction. It features a toggle to visualize 3D wind vectors in AR and a slider to navigate through a 24-hour forecast, allowing the user to anticipate changes in environmental conditions. Figure 3: Weather monitoring panel To ensure flexibility and accessibility, the system includes three interaction methods: Manual Interaction: Users directly touch and manipulate virtual objects and buttons using their hands; Distance Interaction: A ray cast from the user’s hand allows for the selection and manipulation of distant objects; Gaze Interaction: Users select an element by looking at it highlighting the object, and then confirm the selection with a hand ”pinch” gesture. 2 © 2025 IEEE. This is the author’s pre-print version of the article that has been published in the proceedings of 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). The final version of this record is available at: 10.1109/ISMAR-Adjunct68609.2025.00041 4.2 Location Setup and Calibration Process A challenge for geo-referenced AR on the HoloLens 2 is its lack of an integrated Global Positioning System (GPS) or a native compass for determining true north. A custom solution is needed to accurately anchor the AR content to the real world. Our system addresses this through a two-stage setup and calibration process. Because the HoloLens 2 does not have a built-in GPS sensor, the system relies on a companion smartphone app to provide the user’s real-world GPS location. This ”GPS Provider App” was developed in Unity for Android devices and serves as an external GPS sensor for the system. With the GPS position established, the orientation of the AR scene must be aligned with the real world. The system must know which direction is true north. This is achieved through a manual calibration process guided by the companion app. This is crucial to ensure that AR overlays for distant POIs, team members, and navigational cues appear in the correct real-world locations. 4.3 3D Map Implementation with Mapbox The core of the system’s geospatial visualization is a 3D map provided by the Mapbox API. When the user opens the map panel, a 3D version of the map appears, centered on their current location, with a marker indicating their position. The map is rendered using the Mapbox API, which provides high-resolution terrain data and satellite imagery. To enhance usability, several custom features were implemented. The map can be zoomed in and out. The zoom functionality is controlled by two simple buttons on the map panel, allowing the user to focus on specific areas of interest. In addition, a toggle button allows the user to switch between street view and satellite mode. The street view mode displays a standard road map, while the satellite mode shows high-resolution satellite imagery, which can be particularly useful for identifying terrain features and vegetation patterns relevant to wildfire management. 4.4 Navigation and Routing The system provides robust navigation capabilities, allowing the user to generate and follow optimal routes to selected destinations. This functionality is also powered by the Mapbox Directions API. When a user selects a destination (e.g., by clicking on a POI or team member marker), the system triggers a route calculation. A request is sent to the Mapbox Directions API, which then returns a response containing a list of GPS coordinates that form the checkpoints of the optimal path to the destination. This route is then visualized in two distinct ways: 3D Map Display: The list of route checkpoints is converted from GPS coordinates to the map’s local world space. Then the system draws a continuous line connecting these points on the 3D map, providing a clear visual representation of the path. AR Route Guidance: To provide real-time, heads-up navigational aid, the system displays a 3D arrow marker in the AR environment that constantly guides the user to the targer location. To place this checkpoint in the AR scene (relative to the user), its GPS coordinates must be translated into Unity world space, which is achieved by implementing a geospatial conversion based on the WGS84 ellipsoid model.ible marker in the Unity scene. Figure 4: 3D navigation arrow The 3D arrow marker is designed to always point towards the target location. It is positioned at a fixed distance in front of the user. This creates an intuitive ”follow-me” directional cue that guides the user towards their destination. 4.5 Visualization of Team Members and POIs Enhancing team coordination and awareness of environmental factors requires the clear visualization of team members and POIs. The system displays this information both on the 2D map and as 3D overlays in the user’s environment. To facilitate real-time tracking of team members, the system relies on the companion app that runs on each team member’s smartphone. This app transmits each team member’s GPS coordinates to the central server, which then forwards this information to the system. A similar process is used for POIs as POI data is provided by the server, which can include locations of water sources, fire hydrants, or other critical infrastructure. The server process such data and sends them to the system for visualization. The locations of team members and POIs are displayed on the 3D map using the same method previously described. In the AR environment, their positions are determined by converting their GPS coordinates into world-space positions. Figure 5: Point Of Interest (POI) types and icons The team members are represented by a 3D human figure, while POIs are represented by their unique 2D icons rendered on a 3D quad. To enhance visibility, each POI includes a distance indicator text label which is updated in real-time to reflect the distance from the user. In early system development, distant POIs appeared too small to be legible. Dynamically adjusting the object’s size based on its distance from the user made distant objects larger and closer ones smaller, ensuring consistent visibility. Each POI marker always faces the user, so that the icon and distance text remain readable from any angle. Figure 6: Team members visualization 4.6 Fire Monitoring and Spread Visualization A core function is providing accurate fire data by integrating active fire locations with a predictive fire spread model. The system ingests satellite-detected hotspot data from NASA’s Fire Information for Resource Management System (FIRMS) API, which has up to three hours latency. Data is filtered to display only relevant fires based on the user’s GPS position and a set radius. For predictive insights, the system visualizes output from the SPARK fire spread model. SPARK provides its data as a CSV file representing 3 © 2025 IEEE. This is the author’s pre-print version of the article that has been published in the proceedings of 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). The final version of this record is available at: 10.1109/ISMAR-Adjunct68609.2025.00041 a 30×30 m grid, where each row contains the cell’s Web Mercator coordinates (X,Y), fire arrival time, flame height, and intensity. Figure 7: Fire spread visualization The server sends this simulation data to a HoloLens client for visualization, which is controlled by a simple user interface with forward, backward, and play/pause buttons. When navigating the simulation, cell colors update based on the fire’s arrival time relative to the simulation clock: Red indicates a currently active fire, Yellow means the fire is predicted to arrive within 5 minutes, and Black shows a burned area where the fire has passed. This interactive timeline allows a user to scrub through the fire’s predicted progression, providing a powerful tool for strategic planning. 4.7 Weather Data Monitoring and Visualization Environmental conditions, particularly wind, are a critical factor in wildfire behavior. The system integrates real-time and predicted weather data from the OpenMeteo API. To get the weather data, the system sends a request to the API, based the user’s location, and receives the current weather conditions and a 24-hour forecast of temperature, wind speed, and wind direction. These data are parsed and displayed in the weather panel. Using a slider, the user selects a specific hour within the 24-hour forecast, to access the corresponding data. The system retrieves the relevant forecast data from the stored list and updates the display accordingly. The system also implements a feature where a user places a pointer on the map. The system then translates the local coordinates of that pointer into GPS coordinates. This allows the user to select a specific target location on the map. The system retrieves the weather forecast for that location. To make wind conditions more intuitive, the system visualizes wind direction directly in AR. This occurs by spawning a grid of 3D arrows in the user’s FOV, which is updated whenever new weather data is received to reflect the current direction along with an animated movement that simulates the wind’s flow. Figure 8: AR wind direction visualization 5 EVALUATION Results from simulation models are visualized in head-worn AR for firefighters. For testing with Finnish stakeholders, the developed AR system was subjected to a set of test cases ([11]) and integrated into a realistic test scenario ([12]). The tests with the AR system were carried out at an Emergency Service Academy in Finland as part of an annual stakeholder meeting in Kuopio. In an exhibitionlike setup, 15 Finnish firefighters at different command levels with varied levels of experience, tested the AR system. Both quantitative and qualitative feedback was collected during the evaluation. The stakeholders were each given an individual introduction to AR and the developed system. Participants who had no previous experience with AR were offered individual guidance and were guided through the system functionalities before executing the actual test cases. As part of the testing, features such as the visualization of fire, interactive maps, POIs and the visualization of team members were used by stakeholders. In order to obtain quantitative feedback, firefighters were provided with questions in a questionnaire and asked to rate the questions using a 5-point Likert scale based on the System Usability Scale [5]. The users rated the visualization of relevant POIs (1) with a slightly above-average rating of 3.93 out of 5.00 points. Trust in the system for the visualization of simulation results (2) of a forest fire simulation model with AR was rated with 3.60 out of 5.00 points, illustrating basic technological acceptance. This cautious evaluation is due to the general acceptance of simulation models in the response phase, as it became clear in the qualitative feedback. This means that the users were satisfied with the visualization of the results, but since simulation models are difficult to use for the response phase due to their long computing time, this question was cautiously rated. The support provided by a map for better orientation at the scene (3) was rated particularly positively, achieving the best result with 4.20 out of 5.00 points. Qualitative feedback included comments such as “[.. . ] useful for building fires if the relevant information can be called up directly via the AR glasses” or “The usability was good!”. The intuitiveness of the system and the fast learning curve that the user experiences when using the system for the first time were also emphasized. Requests for additional actions included AR visualization training, obtaining official approvals, conducting further practical tests and provision of detailed information on use and reliability. Overall, the results showed user acceptance of the visualization functions. Firefighters provided valuable feedback on further improvements to the system. 6 CONCLUSION This paper introduced a head-worn AR system designed to address critical gaps in wildfire command by improving commanders’ situational awareness. By fusing disparate, real-time data streams, including team member locations, satellite-derived fire perimeters, and predictive simulations into a unified, interactive holographic interface, the system provides a comprehensive operational picture directly in the user’s field of view. An evaluation conducted with 15 professional firefighters not only confirmed the system’s high usability and intuitive design but also emphasized its significant potential to support complex tactical decision-making. This work demonstrates a crucial step towards equipping field commanders with technology that can enhance both the safety and efficiency of firefighting operations. Future work could involve refining the system and conducting further field trials. Key areas for enhancement include integrating new data sources such as real-time drone feeds, thermal imaging, and health monitoring for team vital signs. Furthermore, the system could be improved with the addition of local sensors for more accurate wind data and an attached GPS module to eliminate reliance on a companion app. The goal of these enhancements is to develop a more robust, field-ready tool that improves the safety and efficiency of firefighting operations. 7 ACKNOWLEDGMENTS This work was supported by the EU project CREXDATA under Horizon Europe agreement No. 101092749. 4 © 2025 IEEE. This is the author’s pre-print version of the article that has been published in the proceedings of 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). The final version of this record is available at: 10.1109/ISMAR-Adjunct68609.2025.00041 REFERENCES [1] T. Bailie, J. Martin, Z. Aman, R. Brill, and A. Herman. Implementing user-centered methods and virtual reality to rapidly prototype augmented reality tools for firefighters. In International Conference on Augmented Cognition, pp. 135–144. Springer, 2016. 1 [2] P. Battistoni, M. Di Gregorio, D. Giordano, M. Sebillo, G. Tortora, and G. Vitiello. Wearable interfaces and advanced sensors to enhance firefighters safety in forest fires. In Proceedings of the 2020 International Conference on Advanced Visual Interfaces, AVI ’20. Association for Computing Machinery, New York, NY, USA, 2020. doi: 10. 1145/3399715.3399961 2 [3] P. A. Beata, A. E. Jeffers, and V. R. Kamat. Real-time fire monitoring and visualization for the post-ignition fire state in a building. Fire technology, 54(4):995–1027, 2018. 2 [4] G. E. Beroggi, L. Waisel, and W. A. Wallace. Employing virtual reality to support decision making in emergency management. Safety Science, 20(1):79–88, 1995. The International Emergency Management and Engineering Society. doi: 10.1016/0925-7535(94)00068-E 1 [5] J. Brooke et al. Sus-a quick and dirty usability scale. Usability evaluation in industry, 189(194):4–7, 1996. 4 [6] B. D. Campbell, H. O. Mete, T. Furness, S. Weghorst, and Z. Zabinsky. Emergency response planning and training through interactive simulation and visualization with decision support. In 2008 IEEE Conference on Technologies for Homeland Security, pp. 176–180, 2008. doi: 10. 1109/THS.2008.4534445 1 [7] A. Campos, N. Correia, T. Rom˜ ao, I. Nunes, and M. Sim˜ oes Marques. Mobile augmented reality techniques for emergency response. In Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous ’19, pp. 31–39. Association for Computing Machinery, New York, NY, USA, 2020. doi: 10.1145/3360774.3360825 1 [8] T. Chalimas and K. Mania. Cross-device augmented reality for fire and rescue operations based on thermal imaging and live tracking. In Proceedings of the 1st Joint Workshop on Cross Reality (JWCR23) at ISMAR, 2023. 2 [9] F. Dusse, P. S. J´ unior, A. T. Alves, R. Novais, V. Vieira, and M. Mendonc¸a. Information visualization for emergency management: A systematic mapping study. Expert Systems with Applications, 45:424–437, 2016. doi: 10.1016/j.eswa.2015.10.007 1,2 [10] A. Ginige, L. Paolino, M. Romano, M. Sebillo, G. Tortora, and G. Vitiello. Information sharing among disaster responders-an interactive spreadsheet-based collaboration approach. Computer Supported Cooperative Work (CSCW), 23(4):547–583, 2014. 2 [11] I. Graessler, M. Ebel, and J. Pottebaum. Model-based planning of test cases and test scenarios to support engineering of cyber-physical systems. In 2024 IEEE International Symposium on Systems Engineering (ISSE), pp. 1–8. IEEE, 2024. 4 [12] I. Graessler, M. Ebel, and J. Pottebaum. Method for systematic generation of test scenarios for early customer involvement. In in CIRP DESIGN: 35th CIRP Design Conference, Patras, Greece. CIRP, 2025. 4 [13] H.-S. Kang, J.-W. Lee, and S.-M. Choi. Combining augmented and virtual reality experiences for immersive fire drills. In SIGGRAPH Asia 2022 Posters, SA ’22. Association for Computing Machinery, New York, NY, USA, 2022. doi: 10.1145/3550082.3564195 1 [14] M. Katsiokalis, E. Tsekeri, A. Lilli, K. Gobakis, D. Kolokotsa, and K. Mania. Gonature ar: Air quality & noise visualization through a multimodal and interactive augmented reality experience. In Proceedings of the 2023 ACM International Conference on Interactive Media Experiences, pp. 366–369, 2023. 2 [15] G. A. Koulieris, K. Aks¸it, M. Stengel, R. K. Mantiuk, K. Mania, and C. Richardt. Near-eye display and tracking technologies for virtual and augmented reality. Computer Graphics Forum, 38(2):493–519, 2019. doi: 10.1111/cgf.13654 1 [16] M. Kozlovszky and D. Z. Pavlini´ c. Environment and situation monitoring for firefighter teams. In 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 439–442, 2014. doi: 10.1109/CINTI.2014.7028715 2 [17] J. Li, Z. Xie, X. Sun, J. Tang, H. Liu, and J. A. Stankovic. An automatic and accurate localization system for firefighters. In 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI), pp. 13–24, 2018. doi: 10.1109/ IoTDI.2018.00012 2 [18] X. Li, W. Yi, H.-L. Chi, X. Wang, and A. P. Chan. A critical review of virtual and augmented reality (vr/ar) applications in construction safety. Automation in Construction, 86:150–162, 2018. doi: 10.1016/ j.autcon.2017.11.003 1 [19] I. L. Nunes, R. Lucas, M. Sim˜ oes-Marques, and N. Correia. An augmented reality application to support deployed emergency teams. In S. Bagnara, R. Tartaglia, S. Albolino, T. Alexander, and Y. Fujita, eds., Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), pp. 195–204. Springer International Publishing, Cham, 2019. 1 [20] I. Safranoglou, A. Stavroulakis, M. Ebel, J. Pottebaum, G. Lamprinakis, D. Dimelli, and K. Mania. Augmented reality for realtime decision-making in flood emergencies. In 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMARAdjunct), pp. 110–116, 2024. doi: 10.1109/ISMAR-Adjunct64951. 2024.00032 1 [21] D. Sainidis, D. Tsiakmakis, K. Konstantoudakis, G. Albanis, A. Dimou, and P. Daras. Single-handed gesture uav control and video feed ar visualization for first responders. In Proceedings of the International Conference on Information Systems for Crisis Response and Management (ISCRAM), Blacksburg, VA, USA, pp. 23–26, 2021. 1 [22] M. Sebillo, G. Tortora, G. Vitiello, L. Paolino, and A. Ginige. The use of augmented reality interfaces for on-site crisis preparedness. In P. Zaphiris and A. Ioannou, eds., Learning and Collaboration Technologies, pp. 136–147. Springer International Publishing, Cham, 2015. 1 [23] T. Siu and V. Herskovic. Sidebars: improving awareness of off-screen elements in mobile augmented reality. In Proceedings of the 2013 Chilean Conference on Human - Computer Interaction, ChileCHI ’13, p. 36–41. Association for Computing Machinery, New York, NY, USA, 2013. doi: 10.1145/2535597.2535608 1 [24] A. Stavroulakis, D. Dimelli, M. Roumeliotis, and A. Mania. An augmented reality system architecture for flood management. In GISTAM, pp. 211–218, 2024. 1 [25] A. R. Wani, S. Shabir, and R. Naaz. Augmented reality for fire and emergency services. In Int. Conf. on Recent Trends in Communication and Computer Networks, Byderabad India, 2013. 1 [26] K. Zhang, B. R. Cochran, R. Chen, L. Hartung, B. Sprecher, R. Tredinnick, K. Ponto, S. Banerjee, and Y. Zhao. Exploring the design space of optical see-through ar head-mounted displays to support first responders in the field. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, pp. 1–19, 2024. 1 [27] Y. Zhu and N. Li. Virtual and augmented reality technologies for emergency management in the built environments: A state-of-the-art review. Journal of Safety Science and Resilience, 2(1):1–10, 2021. doi: 10.1016/j.jnlssr.2020.11.004 1 5