# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 5 Architecture of the Framework  
This section describes the framework that is designed for the analysis and synthesis of the prospective data fusion alternatives. The intention of this section is not to explain the implementation of the system in detail, but rather provide an architectural view and abstract technical description so that the reader may gain an insight into its structure and the working of it. A symbolic representation of the architecture is given in Figure 2, using the 'UML component diagram' notation. Three stakeholders are shown in the figure: _Analyst_, _Data source modeler_, and _Geographical area digital-twin modeler_ in 'actor' notation. The stakeholder _Designer of the system_, who is in charge of designing and implementing the framework, is not shown here. With the help of the component _UI_, the _Analyst_ analyzes or synthesize the prospective sensor fusion alternatives. To this aim, _Data source modeler_ defines the models of the available data sources using the feature-modeling tool, as explained in the previous section. _Geographical area digital-twin modeler_ is responsible for creating a model of the geographical area that is considered. The _UI_ component retrieves data from the components _Feature-modeling tool_ and _OO database_. The component _OO database_, in turn, accesses the component _Digital-twin model of Geographical-area_. An external _GIS database_ provides the necessary data for this purpose. The component _UI_ utilizes the functions offered by the component _Model evaluator_ for the analysis and synthesis operations. The component _Algorithms_ offers the necessary algorithms for both analysis and synthesis. For dynamic menu generation, the components _Simulator_ and _OO database_ use the services of the component _UI_. The proposed architecture is'model-based'. Without modifying the system, data source and geographic information system models can be defined and introduced into the system with the help of model management tools. In addition, UI menus, analysis and synthesis algorithms, and most system functionalities are designed as plug-ins; they can be replaced after the installation of the system without re-compilations. The model of the geographic area is based on CityGML [55], which is an extensible Geographic Information System (GIS) specification. The models and meta-models of CityGML can be extended by subclassing the existing classes. However, it is claimed that the current models of CityGML are not expressive enough to represent the data structures required by disaster management systems, and to this aim, an extension of CityGML is proposed [56]. In this article, we adopt the proposed extension, where a rich set of element types are supported, such as Bridge, Factory, Financial Center, Firefighter Station, Historical Buildings, Hospital, Logistic Center, Residence, Restaurant, Road, School, and Shopping Center. In addition, disaster-specific data types are defined such as Emergency Control Center, Collapse, Fire, Flood, Landslide, and Tsunami. Figure 2: A symbolic representation of the architecture of the model-based framework. Figure 3 shows a snippet of class Residence. The attributes of class Residence are grouped in three categories marked with the stereotypes constant, dynamic, and derived. The attributes tagged with the stereotype <constant>> are those whose values are set during the instance creation phase, when a digital-twin model [57] of an urban area is defined. Regarding the attribute _robustnessFactor_, it is defined by experts depending on the earthquake resilience properties of the construction and the ground mechanics of the construction site [58]. The value of this attribute can be set as 'undefined', or be defined within the range of 0 to 5. The dynamic attributes are those whose values may change in time. For example, the attribute _nOfPersons_ can be estimated, set, or modified by a simulator, or it can be based on an estimated number, derived from the relevant data sources.