## 1 Introduction  
In recent decades, the rapid advancement of technology has driven accelerated urbanization globally. This phenomenon has led to a significant increase in population density in urban areas, necessitating the development of essential infrastructure and services--such as electrical grids, water supply, transportation systems, and public safety mechanisms--to improve the quality of life in these regions [1, 2]. In this context, the concept of smart cities has emerged as a technological response designed to optimize urban resource management through the integration of interconnected and advanced systems [3, 4]. With the evolution of technologies such as Big Data and the Internet of Things (IoT), these systems can interact and operate autonomously, optimizing processes in areas including security, disaster management, and resource efficiency [5]. One of the critical factors in improving citizens' quality of life is crime prevention. Urban regions with high population density are often associated with elevated crime rates, making it increasingly challenging to ensure public safety. According to the Organizationfor Economic Cooperation and Development (OECD), public safety is a fundamental element of citizens' well-being, making it a priority for public administrations across the globe [6]. Furthermore, crime statistics reported by SIEDCO (Sistema de Informacion Estadistico, Delincuencial Contravencional y Operativo de la Policia Nacional--National Police Statistical, Criminal, Contravening, and Operational System) [7] indicate that the most common crimes in Colombia include the following:  
* Robbery;
* Domestic violence;
* Vehicle theft;
* Sexual crimes;
* Homicide;
* Extortion. Figure 1 presents crime statistics reported over the last three years in Colombia, emphasizing the prevalence of robbery, which averages approximately 310,000 incidents annually. Improving the deployment of police resources in each region is crucial to prevent such crimes. According to the above, public safety remains a top priority in the context of smart cities. The interconnection of various urban elements provides opportunities for optimizing resource management, preventing crimes, and ultimately improving citizens' quality of life. Given the prevalence of robbery as a major challenge for public safety in Medellin (Colombia), targeted efforts to prevent crime are essential. This study is part of the project titled \"Administracion inteligente de problemas de seguridad ciudadana a traves de modelos y herramientas generados a partir de plataformas para territorios inteligentes apoyadas por estrategias de participacion ciudadana en la ciudad de Medellin\" (Intelligent Administration of Citizen Security Problems through Platform-Generated Models and Tools for Smart Cities Supported by Citizen Participation Strategies in the City of Medellin). The project is implemented through a collaboration between the Mayor's Office of Medellin, the Universidad de Antioquia, and the Universidad Nacional de Colombia. Its main objective is to develop technological strategies aiming to address public security issues in the metropolitan area. In this context, several efforts have been made to improve crime prevention, including the use of person re-identification systems that employ facial recognition databases and convolutional neural networks, achieving performance rates of up to 98% [8]. Additionally,  
Figure 1: Crime statistics in Colombia; source: [7]. deep learning models have been employed to strengthen facial recognition processes in urban surveillance systems, demonstrating the feasibility of achieving real-time results with response times on the order of 20 ms [9]. These technologies provide critical support to security agencies and contribute to public safety. However, these approaches are often constrained by the quality and diversity of the training datasets, as well as their dependence on ideal environmental conditions, such as appropriate lighting and unobstructed views.