More precisely, since the initiation of the first Earth Observation Satellite Mission, starting with NASA's Landsat-1 in 1972, satellite systems have continuously collected data, allowing for long-term observations and analysis of changes in vegetation, land use, fire events, and environmental conditions. These historical data enable researchers to study patterns, trends, and potential fire risks. Thenceforth, the combination of RS data with other spatial data in a GIS environment can be found in various studies [7; 8; 9; 10; 11; 12] and has proved its value in fire risk assessment. Fire behavior models are essential tools used in wildfire management and research to understand and predict the behavior of wildfires under various conditions. Instances of such models include FLAMMAP and FARSITE, which provide valuable insights into fire spread, flame length, and fireline intensity, considering factors like fuel type, topography, weather conditions, and wind patterns [13; 14]. Additionally, BehavePlus is widely used for analyzing fire behavior in different fuel types and weather conditions [15], while WRF-SFIRE integrates weather and fire behavior modeling for high-resolution fire spread predictions [16]. Furthermore, Prometheus specializes in predicting wildfires in Mediterranean landscapes, and FireFamilyPlus offers a suite of models for simulating fire spread and behavior in different ecosystems [17]. FireLib, on the other hand, provides a library of fire behavior algorithms for various applications [18]. In parallel, AI/ML and data analytics are becoming increasingly valuable in assessing and managing fire risk, as they can analyze large volumes of data, extract patterns, and make predictions. These technologies have been widely adopted by the scientific community and have resulted in accurate and analysis-ready results [11; 19; 20]. Among the plethora of advances and developed methods, it is also worth mentioning the analytical network process (ANP) and the analytical hierarchical process (AHP). Both are under the umbrella of decision-making methods and are designed to handle complex and interconnected decision-making situations that involve interdependencies and feedback among criteria, alternatives, and stakeholders [8]. These methods are usually combined with GIS techniques and fuzzy logic and result in accurate field risk assessments [8; 10; 21]. Other studies investigated other types of more complex models, such as Kameleano FireEx (KFX) Fire and Heat Transfer Simulations, which require special care due to assumptions and uncertainties, aimed to establish a methodology that could be applied to the global scale [22; 23; 24]. Apart from advancements and scientific trends in fire risk research, the literature also identifies limitations and unaddressed issues that need further investigation. More precisely, most studies do not reach a high-detail analysis of fire risk in terms of scale (vital for safeguarding human life) but mainly refer to areas of small extent and average scale [25; 26; 27]. At the same time, several studies do not incorporate field knowledge [12], which, if coupled with scientific analysis, could offer very accurate and targeted results that could essentially support public actors when dealing with disasters. Last but not least, most of these research works, though very important, fail to produce hands-on results in terms of operational applicability, for example, able to be incorporated into a Decision Support System (DSS) that will directly be of help to civil protection services and first responders (e.g., fire department). Through this applied research work, we attempt to meet these challenges, but most of all needs.