## 5 Discussion
Even in the field, as suggested here, service model evaluation and economic efficiency analysis are required based on appropriate cross-reviews by developers and local government operators. Our findings have important implications. First, this study suggested a thermography camera-based abnormal situation detection service encompassing night situations, pre-fire phenomena, and status changes in objects caused by temperature changes. Combined with IoT sensors, this service model can help in overcoming the limitations of CCTVs. Studies have typically focused on Thermography/IR image analysis separately; by contrast, this study suggests a service model selection/evaluation process from a comprehensive perspective to implement control services in urban areas. Our insights can be utilized as a reference in decision-making situations for selecting relevant service models. Second, as a service model selection/evaluation technique, this study presents an extension of the classical AHP method: the integrated Fuzzy AHP/TOPSIS model. While we performed the Fuzzy AHP-based relative importance analysis for the evaluation criteria, which is often utilized in previous studies, we fuzzified the TOPSIS technique. This provides more precise criteria weights while considering questionnaire fatigue in a questionnaire with more than 20 alternative evaluations, and using best and worst alternatives. This study also has methodological significance because TFN, a fuzzified evaluation criteria weight, was applied to the fuzzy decision matrix while deriving FPIS and FNIS of Fuzzy TOPSIS. This allowed us to combine the strengths of Fuzzy AHP based on relative importance and Fuzzy TOPSIS based on absolute importance. Third, the implications of the major service model evaluation results are as follows. The night pedestrian and mobility detection services were ranked first and second overall, respectively, which is considered to indicate a greater importance attributed to the object recognition utilization and reliability of thermal cameras than to general RGB images that are vulnerable at night. When implementing this service, it is essential to consider not only object recognition but also the method of transmitting it to pedestrians and drivers (e.g., using LED electronic boards and pictograms). Precursor fire event detection services were also ranked at the top (ranking third to fifth). To operate the service, it is necessary for operators to select thermal cameras suitable for the service area range. For example, to detect a precursor fire inside a building, several entry-level types with short detection distances should be installed in dangerous facilities (e.g., electric charging stations and distribution pipes). To identify fires outside buildings and forest fires, products with long detection distances should be selected, even if they are expensive. It is also a good idea to use mobile equipment, such as drones. Fourth, the difference in perception between the developer and the service operator is as follows. We found that developers highly evaluated services that can identify early signs of dangerous situations by detecting temperature changes in heat, which is the core principle of thermography cameras (e.g., pre-fire phenomenon), while local governments highly evaluated control services related to citizen safety (e.g., pedestrian detection at night). Clearly, while selecting an effective service model, the opinions of experts with a high understanding of the technology itself and operators who actually manage services should be appropriately reflected. This study has some limitations. First, while a service model was evaluated from a comprehensive perspective to detect life safety abnormalities based on thermography cameras, actual regional and spatial analysis was not conducted. One must carefully explore the current status, such as regional features, before introducing thermography cameras and control infrastructure levels and performing spatial analysis.