Supporting City Resilience Through Interoperable Platforms and Tools for Monitoring Natural Threats and Evaluating Their Impacts: A Case Study of Camerino

Abstract

Natural threats are becoming increasingly frequent and difficult to anticipate, urging public authorities and stakeholders to adopt sustainable methodologies and tools capable of continuously supplying historical and real-time data on hazards and their impacts. Such tools enable the prompt activation of recovery actions, enhance the resilience of citizens and the built environment, and contribute to the achievement of the Sustainable Development Goals (SDGs). This paper presents an interoperable and multipurpose framework developed within the MULTICLIMACT project (GA n. 101123538), designed to enhance urban smartness and sustainability, and to support and improve resilience in municipal decision-making. The framework integrates heterogeneous data sources into a unified environment, covering infrastructures, buildings, and social systems. It also includes physiological monitoring, which collects physiological parameters from wearable sensors in a privacy-preserving way, and microclimate monitoring, which records indoor air quality in inhabited environments. Simulation-based analyses are applied to capture cascading effects of disruptions, while multidimensional indicators (societal, economic, operational, and health-related) are used to quantify resilience. The approach was implemented in the Italian municipality of Camerino, where hazard monitoring systems, impact assessment tools, and indoor comfort data were integrated and validated in the SCP-MULTICLIMACT platform. The proposed approach offers a replicable model for integrating environmental and health data in support of climate resilience and sustainable urban development.

1. Introduction

In recent years, the increasing frequency and intensity of natural hazards have posed unprecedented challenges to cities and communities worldwide. According to the Center for Research on Disaster Epidemiology (CRED), in 2023, the EM-DAT (International Disaster Database) recorded 399 disasters related to natural events, which is 30 more than the annual average for the period 2003–2022 [1]. These phenomena, increasingly intensified by the effects of climate change, underscore the pressing need for advanced tools to strengthen urban system resilience and to support effective, sustainable emergency response strategies.

Several studies highlight that one of the key obstacle that municipalities face in managing natural extreme events is the lack of integration of data and tools used to monitor hazards, evaluate risks, and coordinate response actions. As noted by Mohammadi et al. [2], in multihazard multisector risk management “there is a lack of integrated frameworks”. According to Zuccaro et al. [3], while essential for reliable decision-support tools in disaster risk management, “data are scattered among multiple sources, thus hampering their effective use and translation into actionable information” for decision-makers. The study conducted by Šakić Trogrlić et al. [4] highlights that “there is a very limited implementation of approaches that address multi-hazard risks (e.g., multi-hazard early warning systems, land use management which reduces impacts of both floods and droughts), as they largely remain focused on tackling risks from single hazards”. Furthermore, the same study puts in evidence that insufficient prevention and preparedness for multi-hazard risks is related to the siloed approach that dominates the current disaster management strategies across Europe. Systems operate independently, isolated like farm silos, since they rely on multiple vertical and domain-specific platforms that adopt different data formats and protocols, making them unable to exchange information, like people speaking different languages.

In this context, interoperability plays a crucial role in improving service delivery to citizens and businesses; its absence leads to fragmentation across platforms, technologies, and institutional actors, ultimately resulting in suboptimal and uncoordinated services at the local level [5]. Situations where different city platforms are disconnected from each other can create significant barriers to timely information access, situational awareness, and coordinated decision-making across departments and institutions. This has been formally recognized by the United Nations in its special report on Technology for Disaster Risk Reduction, which explicitly defines as “vital” the development in interoperability models that enable communication and data exchange between heterogeneous systems [6]. Similarly, the European Commission has highlighted the importance of interoperability in the urban context and has published the European Interoperability Framework for Smart Cities and Communities (EIF4SCC) [5] to address such challenges.

Municipalities are often the first institutional actors responsible for responding to crises and ensuring the safety of communities. It is, therefore, essential to provide them with interoperable platforms that harmonize data flows and facilitate seamless communication among digital systems. These platforms should enable municipalities to swiftly retrieve critical information from heterogeneous sources during emergencies; conduct cross-sectoral analyses to identify cascading effects and interdependencies; coordinate response actions across emergency services, infrastructure operators, and community stakeholders; monitor the evolution of risk exposure and vulnerability over time; and support evidence-based decision-making throughout both immediate response and long-term recovery phases.

They represent a strategic asset not only during emergencies but also in the broader context of urban planning and sustainability enhancement, where understanding risk exposure and system interdependencies is key to strengthening long-term resilience. Moreover, the centralization of data into a common operational picture helps overcome institutional fragmentation, which is repeatedly cited in the literature as a critical barrier to effective risk governance [7].

In the specific domains of hazard risk reduction, emergency management, and urban resilience, several studies have focused on designing interoperable platforms and addressing interoperability challenges at different levels. Tomas et al. [8] focused on harmonizing and disseminating natural hazard data from heterogeneous sources, aligning their framework with the INSPIRE directive [9]. Carsí et al. [10] developed semantic interoperability mechanisms to enable resilience assessments across different frameworks, while Schröter et al. [11] highlighted interoperability gaps at three critical levels: data/models, communication, and governance. In addition, Jiang et al. [12] proposed a Disaster Model Service Chain (DMSC) to facilitate interoperable multi-hazard modeling and simulation. More recently, Zhang et al. [13] emphasized cross-sectorial interoperability as a key challenge for global disaster resilience strategies based on open data infrastructures.

From a broader smart city perspective, Bokolo [14] provided a systematic review of digital systems in urban environments, emphasizing that the lack of standardization, and consequently interoperability, poses growing limitations to the scalability and effectiveness of smart city applications. His work identifies the use of APIs (Application Programming Interfaces) as pivotal for achieving seamless communication across urban digital infrastructures. This is in line with what is stated by the IES City Framework, an international initiative led by NIST and involving stakeholders from across the globe, aimed to reduce interoperability barriers in smart city implementations [15]. To support this goal, the framework introduced the concept of Pivotal Points of Interoperability (PPIs), defined as common design choices independently adopted by multiple cities (e.g., the use of open APIs for data exchange). These shared patterns can serve as a foundation for the development of consensus-based interoperability standards in the smart city domain.

Despite this growing body of literature, the existing approaches tend to focus primarily on the interoperability of data and models directly related to natural events or damaged infrastructures. However, limited integration of additional domains such as health and comfort has been explored so far. To the best of the authors’ knowledge, holistic interoperable approaches that encompass such data sources remain largely unexplored.

In this perspective, the authors have developed an interoperability framework [16], based on API-based communication models (e.g. RESTful Web Service), to enable seamless integration across heterogeneous sensing and solutions for monitoring different types of natural hazards (earthquakes, floods, and heatwaves) at multiple geographic scales and indoor comfort and physiological parameters. The framework derives from the customization of the “Smart City Platform Specification” (SCPS) [17]. Unlike initiatives such as EIF4SCC or IES City Framework, which mainly define meta-frameworks to support the implementation of interoperable solutions for smart cities, the SCPS takes some further steps: it adopts their principles and concepts, where relevant; applies them to specific smart city contexts, such as the management and monitoring of extreme natural events; and offers technical specifications ready to be used (e.g. JSON specification to represent data [18]). Furthermore, it provides a reference software implementation, the “Smart City Platform” (SCP), that can be configured for specific context. Both SCPS and SCP have been already adopted in several smart city contexts, following different objectives, e.g., supporting the traffic management, monitoring the public energy consumption, etc. The framework presented in [16] is the first case of use in the multi-hazard domain and, importantly, it also supports the exchange of data from complementary domains such as comfort and well-being, which are essential for implementing person-centered resilience strategies and disaster recovery actions.

The approach adopts the definition of city resilience provided by [19], that is, “the capacity of urban systems—including physical, social, economic, and institutional dimensions—to absorb, adapt, and recover from disruptive events while maintaining essential functions”. This holistic perspective emphasizes that resilience is not limited to a single sector but emerges from the interplay of multiple components of the urban environment. Within this framework, buildings represent only one element of city resilience. While resilient buildings are critical for safeguarding residents, they must be understood in connection with critical infrastructures, social systems, and governance mechanisms that sustain urban functionality.

Recent contributions in the domain of smart buildings underscore the need for modeling approaches capable of capturing the complexity of indoor environments and the dynamic interactions between built spaces and their occupants. Conceptualizing buildings as intelligent entities composed of interdependent layers and subsystems enables a more comprehensive analysis of environmental and physiological variables relevant to comfort and health monitoring [20,21]. This paradigm supports the development of high-fidelity digital twins that integrate data from structural components, environmental sensors, and wearable devices, aimed at replicating the behavior of buildings as holistic systems.

Regularly monitoring human well-being and health status in the built environment can contribute to enhance the resilience of the entire urban ecosystem, whose reaction capability is directly impacted by the indoor conditions of the environment and of the occupants. Granular multi-domain data collected in the building ecosystem allows us to (i) have a real-time vision of indoor conditions, (ii) prioritize emergency interventions based on the general health status of dwellers, and (iii) generate early warning enhancing the resilience at urban levels. All these data-driven approaches support decision-making in a proactive manner instead of common post-event assessment-based decision.

Addressing indoor well-being requires more than static assessments; robust, quantitative models are required to simulate dynamic conditions and to support responsive control strategies. Traditional linear design workflows often disconnect architectural decisions from their energy and health impacts, resulting in inefficiencies in comfort optimization [21]. In contrast, integrated modeling frameworks allow for continuous monitoring of thermohydrometric variables and physiological signals, such as heart rate, blood oxygen saturation, and motion, thus offering insights into both objective environmental conditions and subjective occupant responses.

Within this framework, health and physiological monitoring systems integrated into smart environments have emerged as critical enablers for resilience-oriented building design and emergency preparedness. By leveraging Internet of Things (IoT) and Artificial Intelligence (AI) technologies, these systems improve the observability of indoor environmental conditions and support more effective management of health-related risks, particularly during extreme events such as heatwaves, seismic events, or deteriorating indoor air quality [22]. IoT technologies and AI algorithms undoubtedly play a pivotal role in this field, enabling the creation of monitoring platforms able to collect, store, and analyze big data that can support decision-making processes [23], thus supporting municipalities in decision-making processes. As urban development increasingly aligns with these paradigms, the real-time integration of comfort and health monitoring is becoming a foundational component of resilient and adaptive built environments [24].

With this systematic perspective, the framework developed by the authors sees city resilience as an overarching concept, narrowed down in the case study to its operational components: resilient buildings, critical infrastructures, and the well-being of residents monitored through comfort indicators.

To demonstrate how this multidimensional perspective can be translated into practice, the paper presents the implementation of an interoperable and replicable framework developed and tested within a real municipal context. Specifically, the Italian municipality of Camerino served as a pilot site, where various sensing and digital tools for monitoring natural hazards were integrated into a unified platform for purposes of validation in the framework of European project MULTICLIMACT (MULTI-faceted CLIMate adaptation ACTions to improve resilience, preparedness, and responsiveness of the built environment against multiple hazards at multiple scales, GA no. 101123538).

The goal here is not to detail the MULTICLIMACT project per se, but rather to demonstrate how the proposed approach enables interoperability between different applications related to natural hazards and the comfort and well-being domains, and to integrate cross-sector data and the related benefits. By showcasing the experience of Camerino, the authors take the first steps in this direction, as the study provides concrete insights into how interoperability can be translated from theory into practice, bringing tangible benefits in terms of situational awareness, coordinated action, and strategic planning.

The remainder of the paper is structured as follows: Section 2 introduces the interoperability model and the sensing tool and solutions exploited in the Camerino case; Section 3 presents the implementation and outcomes of the pilot case study; Section 4 discusses the findings, limitations, and next steps of the study; and then, Section 5 concludes the paper with final remarks and future perspectives.

2. Materials and Methods

2.1. The Interoperability Reference Model

In order to design a framework capable of addressing multiple natural hazards in urban environments, MULTICLIMACT has defined a reference scenario that combines representative case studies and a cross-cutting measurement procedure (a summary, highlighting the types of data flows generated by each one, is provided by

Table 1. Summary of the MULTICLIMACT Reference Scenario.

Type	Name	Description	Data Flow Type
Case studies	Earthquake monitoring and damage estimation	Monitoring seismic events and their impact on cities	Earthquake Events Impact Risk Indicators Earthquake Damaged Buildings Counter
	Heatwave prediction	Prediction of outdoor urban parameters during heatwaves	Urban Surface Condition Prediction
	Flood monitoring	Monitoring parameters for stability and leakage in flood conditions	Hydrological Extremes Time Series
Procedure	Indoor comfort monitoring	Monitoring indoor parameters for health and comfort assessment	Health (physiological) Monitoring Microclimate Monitoring

).

This scenario sets the stage for the development of an interoperable digital infrastructure capable of collecting, integrating, and exchanging data across heterogeneous systems. The MULTICLIMACT solutions aim to monitor, detect, and respond to critical conditions related to natural hazards such as earthquakes, heatwaves, and floods, and to monitor the health and well-being of individuals in affected areas. However, to fully exploit the data generated by these solutions, it is crucial that they are able to communicate and share information effectively.

To prevent solutions from operating in isolation, MULTICLIMACT has introduced an Interoperability Reference Model. This model, presented in [16], proposes the adoption of the SCPS and its reference implementation, the SCP, both designed and developed by ENEA.

The SCPS [17] is a set of public specifications defining a common language and enabling interoperable communication among heterogeneous solutions dedicated to a specific application domain; it is recommended in the Italian national standard UNI 11973:2025 [25].

The SCP is a software prototype designed to retrieve and harmonize data from various solutions operating at different urban scales. The SCP enables data exchange between heterogeneous solutions with a common and shared interoperability protocol, described in the SCPS; this protocol comes through the definition of two fundamental concepts [17]:

• UrbanDataset, a common format to represent the urban data of the city according to a common, shared semantic (based on OWL Ontology, as defined in the specification “SCPS Semantic 2.0”), and implemented in the JSON syntax (based on JSON Schema, as defined in the specification “SCPS Information 2.0”).
• UrbanDatasetGateway, a web service to enable the UrbanDataset exchange between solutions and SCP (as defined in the specification “SCPS Communication 2.0”). Furthermore, SCP implements a syntactic and semantic validation process (based on JSON Schema, OWL Ontology and Schematron) that ensures interoperability of the exchanged data.

SPCS/SCP adopt an APIs-based communication model: JSON to define the data structures and RESTful Web Service as transport protocol. APIs define a set of shared and open rules and methods that allow different applications to interoperate with each other, hiding the internal complexity of a system and exposing only what is necessary to interact.

Furthermore, the SCPS allows the UrbanDataset format to be extended and customized to meet the requirements of specific geographic or application contexts; this can be done at any time following a specific procedure defined by the SCPS [17], and the new or updated UrbanDatasets quickly become part of the public specification. In parallel, the UrbanDatasetGateway and the SCP have been conceived to support any of these potential extensions or customizations and to be configured for each scenario supported by UrbanDatasets. This enables the framework to be scalable (new scenarios can be supported and new systems can be integrated with minimal effort) and replicable across different urban contexts. Actually, the MULTICLIMACT Interoperability Reference Model has been achieved by defining and implementing the process required to customize the SCPS to meet the specific requirements of the project, as deeply argued in [16]. Its main components are as follows:

• SCP-MULTICLIMACT platform, an instance of the SCP prototype configured for the MULTICLIMACT Reference Scenario, featuring the following:

  –

  SCP-GUI: Interface for managing interoperability and system configurations.

  –

  SCP-DASH: Dashboard for visualizing harmonized data outputs.

• UrbanDataset subset, representing data on natural hazard and indoor comfort monitoring, exchanged via the UrbanDatasetGateway.

The model supports a set of vertical case studies and a shared measurement procedure, as outlined in Table 1, and includes the UrbanDatasets that implement all related types of data flow; potentially, by adopting the customization process described in [16], any other type of case study concerning natural hazard and the related UrbanDatasets could be added.

The Implementation in the Italian Pilot

The Italian pilot addresses the “earthquake monitoring and damage estimation” case study, including the “indoor comfort monitoring” measurement procedure supporting the assessment of the impacts of seismic events on well-being and health of people in a certain environment. The scenario supported by this pilot is shown in Figure 1 and involves five types of solutions that send or receive UrbanDatasets to/from the SCP-MULTICLIMACT platform; the UrbanDatasets adopted here are as follows:

• Earthquake Events (red arrows in Figure 1): It provides data concerning one or more seismic events (timestamp of the event detection, epicenter, magnitude, and depth), specifying whether the data relates to a real or simulated event.
• Impact Risk Indicators (light green arrow in Figure 1): It gives aggregated indicators of socioeconomic impact due to natural hazards, indicating the resilience evolution over time and set of the affected nodes (Points of Interest and critical infrastructure). The UrbanDataset is not constrained to a specific indicator calculation method neither to a specific natural hazard; it supports the aggregation of the indicators at different geographic scales (district, town, province, region or country) and it allows the indication of whether the data relates to a real or simulated event.
• Earthquake Damaged Buildings Counter (dark green arrow in Figure 1): It deals with data on the number of buildings damaged in the specified geographic area due to a seismic event; the level of damage has to be indicated according to the European Macroseismic Scale 1998 (EMS-98). This UrbanDataset can refer to different geographic scales (district, town, province, region or country) and it allows to indicate whether the data relates to a real or simulated event.
• Health Monitoring (yellow arrow in Figure 1): It provides various parameters regarding a person’s overall physiological status (e.g., blood oxygen saturation level, heart rate, heart rate variability, etc.). For privacy reasons, the UrbanDataset allow the identification of the device/sensor that collects the data but not the identification or georeferencing of the person.
• Microclimate Monitoring (orange arrow in Figure 1): It provides indoor air quality parameters relating to inhabited environments.

The five involved solutions are as follows:

• Earthquake Event Monitoring, able to collect real-time data for seismic events.
• Urban Resilience Assessment, able to evaluate urban resilience in response to seismic events.
• Earthquake Damage Risk Assessment, able to assess the risk of physical damage to buildings and infrastructures.
• Health monitoring, capable of evaluating human vulnerability to environmental stressors through physiological data analysis.
• Indoor Comfort Monitoring, able to measure the indoor environmental conditions relevant to health and well-being.

In the context of the Italian pilot, these types of solutions have been implemented as follows: (i) Earthquake Event Monitoring, designed to collect and process earthquake related data from external source; (ii) CIPCast Decision Support System [26], which supports risk assessment for buildings and infrastructures and evaluates urban resilience with respect to specific natural events; (iii) LIS platform, designed as a multi-domain monitoring system that integrates environmental and physiological data collection within buildings. More details are provided in the following sections.

2.2. The Earthquake Event Monitoring Solution

The Earthquake Event Monitoring solution is designed to collect and process real-time earthquake-related data from external sources, specifically the INGV (National Institute of Geophysics and Volcanology), Italy’s national institute for geophysics and volcanology. INGV is responsible for monitoring seismic activity, volcanic eruptions, and other geophysical phenomena, providing real-time data on natural hazards. Specifically, the INGV platform provides essential information on seismic events, including the magnitude, epicenter, depth, and a unique event ID assigned by INGV to each earthquake. To ensure timely and accurate data collection, the Earthquake Event Monitoring solution conducts periodic checks (e.g., every 15 min) to retrieve the most up-to-date information from the INGV system.

When a seismic event occurs, and its magnitude exceeds a threshold of $3.0$, indicating a potentially destructive event, the Earthquake Event Monitoring solution retrieves critical data from the INGV portal, including the earthquake’s magnitude, coordinates, timestamp, and the unique event ID, as illustrated in Figure 1. This data is then transmitted to the SCP-MULTICLIMACT platform, where it is made available to relevant stakeholders or other solutions. For example, the information can be required by other solutions to assess potential building damage and urban resilience, which are further discussed in the following sections.

At present, the seismic data available is at a broad territorial level. However, integrating sensors within buildings could enhance the accuracy of damage assessments and enable more targeted safety measures at the building level.

2.3. CIPCast: The Earthquake Damage Risk Assessment Module

The Earthquake Damage Risk Assessment module is designed to assess the impact of seismic events on buildings and infrastructures within a specified area by processing seismic data, including the event’s magnitude and location. The evaluation takes into account factors such as ground shaking intensity and the structural characteristics of each building.

Damage levels are assessed using the “European Macroseismic Scale 1998 (EMS-98)” [27], which categorizes earthquake damage from minor effects ($D0$) to total destruction ($D5$). This scale is applied at two geographic levels: the urban scale, which refers to specific districts or neighborhoods within a city, and the territorial scale, which covers broader regions such as provinces or entire countries.

The assessment process incorporates fragility curves, which are specific to the building’s structural characteristics, including material type (masonry or reinforced concrete), height (low-rise or mid/high-rise), and proximity to the earthquake’s epicenter [28,29]. These fragility curves model the probability of different levels of damage based on Peak Ground Acceleration (PGA), a key measure of seismic intensity.

To calculate the PGA, the epicenter’s location and the building’s position are used to assess the intensity of ground shaking at that specific site. Once the PGA is determined, it is input into the fragility curves to calculate the probability of exceeding each of the five damage states defined by EMS-98. This calculation allows the platform to estimate the likelihood of damage for individual buildings, from minor to catastrophic levels.

The results of the fragility analysis are critical for identifying the buildings most at risk and, hence, for generating warnings and prioritizing interventions [30]. The process then aggregates this data, counting the number of buildings that fall into each damage state category based on predefined probability thresholds. This comprehensive damage assessment, which includes geographical and temporal information, is subsequently provided to the SCP-MULTICLIMACT platform for further analysis and decision-making support, as shown in Figure 1.

2.4. CIPCast: The Urban Resilience Assessment Module

The CIPCast Urban Resilience Assessment module estimates the service levels delivered by infrastructures within a district or city, assessing how these may be degraded by natural or human-made disruptions. The tool is grounded in the MARIS (Modeling infrAstructuRe dependencIes at an urban Scale) methodology [26], originally developed to quantify the impacts of earthquakes on infrastructure services (Figure 2). More generally, MARIS provides a framework to measure urban resilience and to transfer this information into the SCP-MULTICLIMACT platform, thereby supporting both short-term emergency response and long-term adaptation planning.

2.4.1. The MARIS Methodology

MARIS investigates the interdependencies among critical infrastructure (CI) sectors—such as transportation, energy, water supply, health care, and communications—highlighting how disruptions in one sector can propagate to others through cascading failures. The methodology begins with the identification of relevant CI sectors and their interconnections, drawing from official technical reports, international and national directives, and, where available, local data supplied by municipalities and infrastructure operators.

Infrastructure facilities, or Points of Interest (POIs), are extracted from open sources such as OpenStreetMap and complemented with non-open local datasets to ensure the inclusion of facilities managed at the municipal or operator level. Each POI is classified by sector and represented as a node in a directed dependency graph, where the edges describe service provision or consumption relationships. Dependency links are determined by functional roles and geographical proximity, while failure propagation probabilities are derived from the scientific literature or expert knowledge.

The dependency graph constitutes the basis for dynamic simulations that estimate cascading effects across the network. Simulation outcomes include three complementary indicators of impact: (i) societal, expressed as the number of people affected by a service outage; (ii) economic, expressed as GDP losses due to service unavailability; and (iii) operational, reflecting reductions in service performance or functionality. These indicators can be tailored to local conditions, demographic structures, and environmental vulnerabilities.

A key step in MARIS is the identification of failure scenarios, i.e., sets of POIs that are disrupted and may trigger chains of cascading effects. Based on the findings of [31], the analysis restricts attention to subchains of limited order (typically up to seven), as higher-order dependencies contribute negligibly to the overall risk. Stochastic methods are applied to account for uncertainty in propagation probabilities and restoration actions, thereby enabling a robust comparison of risks across different dependency chains and pinpointing the most critical ones within the system.

2.4.2. Risk of a Dependency Chain

To define the risk of a specific dependency chain, let $v_1\to v_2\to \cdots \to v_n$ represent a dependency chain of n POI instances (or nodes) with $n-1$ dependencies, without loops. The cumulative dependency risk in the presence of resilience controls is given by the following:

$${\tilde{D}}_{v_1\to v_n}\left(t\right)=\sum _{j=1}^n\left[\left(\prod _{k=1}^j{L}_{k,k+1}\right)·I_{j,j+1}\left(t\right)-\left(\prod _{k=1}^j{\overline{L}}_{k,k+1}\right)·{\overline{I}}_{k,k+1}\left(t\right)\right]$$

(1)

Here, $L_{j,k}$ denotes the likelihood of failure propagation from node $v_j$ to $v_k$, and $I_{j,k}\left(t\right)$ the impact induced at $v_k$ at time t. Conversely, ${\overline{L}}_{j,k}$ and ${\overline{I}}_{j,k}\left(t\right)$ capture the effect of restoration and recovery actions, reducing propagation likelihoods and impacts.

The impact function $I\left(t\right)$ is parameterized by three ordinal variables: the maximum potential impact I (1–9), the expected duration of unavailability T (1–9), and the temporal growth rate G (1–3, distinguishing slow, linear, or fast evolutions). Restoration functions $\overline{I}\left(t\right)$ follow analogous rules but represent the effect of mitigation and recovery strategies. Together, these parameters allow for a flexible and dynamic representation of how cascading failures evolve and how resilience mechanisms operate to counteract them.

2.4.3. Resilience Evaluation and Decision Support

Building on the definition of dependency risk, a resilience index R can be derived along a dependency chain as the integral of the inverse dependency risk over the observation period. This index expresses the ability of the system to recover from disturbances and provides a comparative metric to rank and prioritize mitigation actions along the most vulnerable chains.

The Urban Resilience Assessment module of CIPCast delivers these outputs directly to the SCP-MULTICLIMACT platform. The results highlight the infrastructure facilities within critical dependency chains that exhibit the highest risk, together with their geospatial location (e.g., hospitals, substations, or water towers), resilience indicators, and resilience values. By integrating this information, stakeholders gain the ability to identify vulnerable components, evaluate the potential benefits of restoration actions, and design targeted resilience strategies. This approach supports not only emergency management during crises but also long-term planning for sustainable and resilient urban development.

2.5. LIS Platform

Recent advances in urban resilience emphasize the critical role of indoor well-being and safety, especially during and after natural disasters. The LIS platform introduces an integrated interoperable system architecture combining real-time physiological monitoring with indoor environmental sensing, designed to support multiple functions (e.g., early warning, emergency response, and long-term comfort optimization).

The underlying approach considers buildings as complex, dynamic systems, whose behaviour cannot be understood by analysing individual components but rather through a holistic evaluation as an integrated system [32]. Indeed, the ecosystem components interact each other, determining the overall behaviour of the system as a whole [33]. Thus, the monitoring architecture was structured based on discrete-event system modeling, treating real-time data streams (e.g., environmental metrics and physiological indicators) as sequences of discrete events [34], allowing the system to promptly detect and react to critical state transitions, including the onset of heatwaves or physiological distress, thereby improving responsiveness and operational efficiency.

To ensure interoperability with the existing digital infrastructures, the platform is fully compatible with Building Information Modeling (BIM) methodologies, enabling the spatial contextualization of real-time sensor data within digital representations of the built environment, supporting advanced decision-support functionalities and contributing to both immediate crisis management and longer-term resilience strategies, positioning indoor comfort and health as central components of adaptive and occupant-centric urban systems. Three modules are envisaged, as described in the following.

The indoor comfort monitoring module is used to track in real-time microclimatic parameters (e.g., temperature, humidity, and indoor air quality). Environmental data are collected through embedded sensors and used to assess indoor conditions under both standard and emergency scenarios, including post-earthquake occupancy and heatwave events. This information feeds into early warning mechanisms capable of identifying deviations from predefined comfort thresholds and initiating appropriate alerts or adaptive responses (e.g., cooling strategies prioritization in case of heatwaves). Furthermore, the collected data support long-term resilience planning by providing evidence for retrofitting interventions based on observed comfort trends. These data are transmitted to the SCP-MULTICLIMACT platform through the standardized “microclimate monitoring” UrbanDataset format (Figure 1), hence contextualizing environmental information spatially and temporally within the broader hazard monitoring framework. This supports decision-makers in identifying critical indoor environments requiring intervention, improving emergency preparedness and adaptive management strategies.

The health monitoring module employs wearable physiological sensors to acquire physiological indicators (e.g., heart rate, blood oxygen saturation (SpO2), and physical activity patterns), particularly valuable in emergency contexts, where rapid assessment of occupants’ health status is required. An SOS activation mechanism (manually or automatically triggered) is included for direct alerts to emergency responders or facility managers. Stream-processing architectures (e.g., Apache Kafka) are used to process these data (fully anonymized to comply with GDPR), ensuring low-latency responses and seamless integration with decision-support dashboards. Environmental and physiological signals are jointly analyzed to assess health-related risks, detect early signs of heat-related stress, or evaluate the impact of degraded indoor air quality, particularly in vulnerable populations. These data generated are conveyed to the SCP-MULTICLIMACT platform in the “Health Monitoring” UrbanDataset format (Figure 1), and correlated with environmental stressors to deliver real-time alerts, informing emergency actions, and supporting evidence-based planning for occupants’ well-being and safety, thus contributing to more effective resilience-oriented decision-making.

2.6. The SCP-MULTICLIMACT Platform

The SCP-MULTICLIMACT platform is an instance of SCP, configured for the MULTICLIMACT Reference Scenario, which acts as a central hub for data exchange and orchestration of tasks among the MULTICLIMACT solutions.

2.6.1. SCP Overview

The SCP is a prototype of an ICT platform for smart city management, designed to implement the SCPS. While it represents one possible instantiation of the SCPS, any other implementation adhering to the same specification would be functionally equivalent in terms of interoperable data exchange.

SCP/SCPS allow smart city monitoring on different scales: district, city, region, and country; there are already working SCP instances on these scales. More generically, SCP/SCPS can be used in every environment to enable interoperable communication between different heterogeneous solutions that need to communicate with each other requesting or providing urban data. It is important to note that the goal of the SCP/SCPS approach is not to replace the existing solutions but, instead, to become a horizontal central bridge that connects them, allowing data exchanging, monitoring, and harmonization, through a shared and public methodology aimed at interoperability among systems. In this way, the technologies of the city do not need to change their approach, each solution continues to manage its specific vertical domains (e.g., monitoring of natural threats, monitoring of people’s well-being, etc.), and they remain complete and autonomous, i.e., capable of collecting data, processing, and archiving it, without having constraints on internal implementation. However, each solution will be able also to provide SCP with interchangeable data, with different transmission frequencies depending on needs. The UrbanDataset format can represent data related to different geographical scales (e.g., building, neighborhood, city, etc.) and measured or grouped on different time intervals (e.g., 15 min, 1 h, 1 day, etc.)

The SCP uses the following concepts and components described in the SCPS:

• The UrbanDataset format and its JSON syntax, defined before.
• The Ontology, defining the semantic content of the UrbanDatasets, and classifying them into categories and sub-categories. In practice, it provides a shared vocabulary and terms and is expressed using the Web Ontology Language (OWL) defined by W3C. The ontology is a dynamic component that can evolve and grow over the time; in order to cover new application contexts, new terms can be added to the vocabulary and new types of UrbanDataset can be defined in the library, as happened for supporting the MULTICLIMACT scenario.
• The UrbanDatasetGateway Web Service, defined before.
• The Registry database, to take note of users, solutions, UrbanDatasets, and all the information about the defined collaborations enabled and configured in the SCP (in other words, the productions and accesses permits).
• The UrbanDataset database, to store and retrieve UrbanDatasets through a non-relational database.

The high-level architecture and main components of the SCP are shown in Figure 3: starting from the bottom, it depicts how each solution, managing a specific application context, exports from its local database the information in the “UrbanDataset” JSON format and sends them to the horizontal platform through an “UrbanDatasetGateway” web service client (WS CLIENT); the UrbanDatasets are received by the SCP through the “UrbanDatasetGateway” web service (WS SERVER).

The SCP provides two interfaces for the final users:

• SCP-GUI (Graphical User Interface), which allows one to register and enable solutions on the SCP, to configure collaborations, and to monitor the activities on SCP and the received data.
• SCP-DASH (DASHboard), where the received data are visualized after being reprocessed and through different types of diagrams, tables or maps.

2.6.2. The Configuration Fine-Tuned for the Italian Pilot

In order to support the implementation of the case study addressed by the Italian pilot, the SCP-MULTICLIMACT platform has been configured to exchange data with the solutions illustrated in the previous sections; in practice, as partially shown in Figure 4, the SCP-MULTICLIMACT platform can perform the following:

• Receive data from the “Earthquake Event Monitoring” solution according to the “Earthquake Events” UrbanDataset format;
• Receive data from “CIPCast” solution according to the “Impact Risk Indicators” or “Earthquake Damaged Buildings Counter” UrbanDataset formats;
• Receive data from “LIS Platform” solution according to the “Microclimate Monitoring” or “Health Monitoring” UrbanDataset format;
• Satisfy requests for “Earthquake Events” UrbanDataset submitted by “CIPCast” solution.

It is important to note that for every configured production, an universal resource identifier (resource_id) is generated to refer unambiguously to the UrbanDataset in every phase of the data exchange. The resource_id is created according to the syntax and criteria defined in the specification “SCPS Collaboration 2.0” [17].

Then, a specific dashboard (Figure 5) has been designed and developed for the SCP-MULTICLIMACT platform; it represents a single access point for the municipality to monitor the data collected by the different solutions that, in this way, can be easily compared, evaluated, and used for implementing evidence-based policies for disaster preparedness and urban resilience. The SCP-MULTICLIMACT Dashboard is organized in several reports, one for each case study depicted by the MULTICLIMACT Reference Scenario. The reports enabled for the Italian pilots are as follows:

• Earthquake, which provides information on the following: (1) the latest earthquake (including a map showing the epicenter), (2) its impact on the built environment and people (including a histogram showing the socioeconomic impacts), and (3) the estimated damage caused by it (including a pie chart showing the percentages of damaged buildings according to the EMS98 scale after the latest earthquake).
• Indoor monitoring, which provides information on the comfort level of indoor environments and the health status of their occupants. It includes line-basic diagrams showing the course of some air quality parameters and the course of some physiological parameters during a period.

It is important to note that the SCP-DASH component in the SCP provides to the administrator users an editor to map the UrbanDatasets retrieved from the SCP-GUI in the tables and charts, taking advantage of the harmonized structure of all the received and elaborated data.

3. Results

3.1. The Case Study of Camerino

The approach has been implemented and tested in the municipality of Camerino, where a case study has been developed focusing on the seismic risk associated with the Visso earthquake on October 2016, simulating its effects on buildings, infrastructure, and indoor comfort.

The city of Camerino (Marche region, Central Italy) is located along the Apennine fault system and is classified as a high seismic-hazard area under the national zoning scheme. The local building stock includes a large share of pre-code masonry buildings (≈70%), many of which are historical and unretrofitted, a condition that increases structural vulnerability and risk to cultural heritage. The presence of the University of Camerino (UNICAM) further raises urban exposure and criticality in emergency scenarios. The 2016 Central Italy seismic sequence highlighted these vulnerabilities in practice and produced an extensive local damage survey that informs our inventory and vulnerability description [35].

The sequence began on 24 August with the so-called “Amatrice earthquake” (Mw = 6.0), followed by numerous aftershocks and culminating in two major events: the “Visso earthquake” (Mw = 5.9) on 26 October, and the “Norcia earthquake” (Mw = 6.5) on 30 October. The Visso event, in particular, caused extensive damage in the Camerino–San Severino corridor, with major collapses including the bell tower of the Church of Santa Maria in Via in Camerino itself, symbolizing the fragility of the built environment and the cultural heritage (Figure 6).

Camerino was intentionally selected as a demonstrator site despite its divergence from dense “smart city” archetypes. Camerino is a heritage-rich, seismic-prone small city dominated by masonry construction and dispersed settlements; these characteristics (sparse sensor coverage, legacy building typologies, and fragmented ownership) pose distinct operational challenges for multi-source data harmonization and, therefore, provide a rigorous test-bed for the platform’s interoperability capabilities. The mapping from inventory classes to fragility families and the description of the local building stock were based on local post-event technical reports and the municipal 2016 damage survey [35].

In the following sections, the acquisition and elaboration of the Visso earthquake data are simulated through the MULTICLIMACT Interoperability Reference Model.

While the results of the damage assessment and urban resilience modeling will reflect realistic simulations based on the Visso event, the indoor comfort and health monitoring analyses will be based on generic but plausible sensor data, decoupled from the actual 2016 seismic sequence. This approach allows us to explore the potential of the SCP-MULTICLIMACT platform even in the absence of detailed historical indoor data recorded during the earthquake.

3.2. Data Workflow

Figure 7 illustrates the abstract data workflow implemented in the pilot and adopted to simulate the specific case of the Visso earthquake in Camerino; the sequence includes the following actions:

• When an earthquake event occurs, the Earthquake Event Monitoring solution transmits earthquake parameters (e.g., magnitude, epicentre, and depth) to the SCP-MULTICLIMACT platform.
• Once a day, at a scheduled time, the CIPCast Damage Risk Assessment and Urban Resilience Assessment modules retrieves data on the last earthquake from the SCP-MULTICLIMACT platform.
• The CIPCast Damage Risk Assessment and Urban Resilience Assessment modules check if the data received from the SCP-MULTICLIMACT platform are different from those received the day before (which indicates that a new earthquake has occurred); in this case, they perform impact and resilience assessments and send the results of the damage and risk evaluations to the SCP-MULTICLIMACT platform (e.g., number of buildings by damage state, identification of vulnerable infrastructures).
• Once a day, at a scheduled time, the LIS indoor comfort and health monitoring modules provide the SCP-MULTICLIMACT platform with physiological and environmental indicators (e.g., temperature, humidity, and air quality).

This workflow demonstrates how the implemented architecture is able to provide integrated and up-to-date information for emergency response, and supports long-term resilience planning in the municipality of Camerino.

More details on the data used for the simulation and the obtained results are provided in the following subsections.

3.2.1. Acquisition of Earthquake Event Data

The Earthquake Event Monitoring solution is responsible for the acquisition of earthquake data. On 26 October 2016, two significant seismic events affected the Visso area:

• First Event: Occurred at 19:10:36 (UTC+2) with a magnitude of 5.4, centered near Castelsantangelo sul Nera.
• Second Event: Followed at 21:18:05 (UTC+2) with a magnitude of 5.9, centered near Ussita.

The Earthquake Event Monitoring solution collected critical data, including magnitude, coordinates, depth, timestamp, and unique event IDs. These data were then transmitted to the SCP-MULTICLIMACT platform (Figure 8) for further processing and analysis. The integration of these data allows for the assessment of the events’ impacts on building structures and urban resilience, supporting timely and informed decision-making.

3.2.2. Assessment of Structural Damage to Buildings

The damage assessment for buildings in Camerino is based on an approach that uses fragility curves tailored to the specific characteristics of each building, as mentioned in Section 2.3.

The Ground Motion Prediction Equation (GMPE) used in this study was the one proposed by Ambraseys et al. [36]. This GMPE is widely recognized for its applicability in seismic hazard assessment, and it provides reliable estimates of ground motion parameters for various fault types and magnitudes. The model was particularly relevant for the seismic event in Camerino, which was simulated based on typical regional seismic characteristics.

Additionally, the study assumed a strike-slip fault type for the earthquake scenario. This assumption was based on the regional tectonic setting, where strike-slip faulting is prevalent in the area surrounding Camerino. Strike-slip faults are characterized by horizontal motion along the fault plane, which is common in regions where the tectonic plates are moving laterally relative to each other. Given the geological history of the region and the nature of past seismic events, a strike-slip fault model was considered appropriate for this study.

Fragility families were selected from the regionally relevant literature and systematically mapped to the inventory classes compiled for the Camerino pilot. In particular, the study relied on established typological and macroseismic fragility models developed for the Italian building stock [37,38], complemented by local studies of the 2016 sequence [35,39]. To characterize the robustness of the aggregated results to classification practice and parametric uncertainty, the authors performed a suite of sensitivity checks that explore a range of illustrative damage-probability thresholds (for example within $0.10–0.20$) and perturbations of fragility medians ($\pm 20\%$). These analyses quantify how aggregate indicators respond to plausible variations in fragility inputs and labeling choices and are intended to assess the stability of the conclusions rather than to define prescriptive thresholds. Where appropriate, alternative regional fragility parameterizations have been considered to test mapping choices [40]. For ease of reading and operational reporting, the following also present an illustrative categorical summary obtained by applying a single, non-prescriptive damage-probability threshold of $0.15$. This threshold is used only to produce a compact stakeholder-friendly summary; the underlying analysis and all primary results remain probabilistic. Under the illustrative $0.15$ threshold, the aggregated counts for the analyzed sample are as follows (Figure 9):

• A total of 303 buildings classified as experiencing at least low-level damage (D1), mainly concentrated in the historic centre of Camerino;
• A total of 261 buildings classified as experiencing at least moderate damage (D2);
• A total of 123 buildings classified as experiencing at least high-level damage (D3);
• A total of 0 buildings classified as reaching the severe/collapse categories (D4/D5) in the analyzed sample.

These categorical counts are illustrative summaries derived from the underlying probabilistic outputs; alternative reasonable thresholds produce systematic and interpretable changes (lower thresholds increase aggregated counts and higher thresholds decrease them).

3.2.3. Assessment of Urban Resilience

As a practical application of the Urban Resilience Assessment, a representative case from Camerino is presented. A full-scale analysis of all infrastructures is beyond the scope of this paper; instead, attention is focused on a hospital, a facility highly exposed to seismic risk and strongly dependent on other infrastructures. The aim is to show how the methodology translates interdependencies into resilience indicators, considering both functionality reduction and restoration capacity.

The first step was the identification of key POIs critical to the city’s operations. Among these, the following were selected for this case study:

• Hospital: Essential for emergency healthcare, depending on power, water, and communications.
• Substation: Ensures power supply to the hospital and other key facilities.
• Telecommunication tower: Provides communications, fundamental for coordinating emergency services.
• Water tower: Supplies clean water necessary for medical and non-medical hospital activities.
• Regional logistics center: Supports delivery of emergency medical supplies.

Using the MARIS methodology, the hospital was modeled as a node potentially damaged by the earthquake. Its functionality depends on a chain of services: electricity from the Camerino Substation, water from the water tower, communication from the telecommunication Tower, and supply chains from the regional logistics center. The resilience of each dependency was estimated, reflecting both backup options and recovery actions:

• Power supply: 55% (temporary generators mitigate but cannot replace the substation long-term).
• Water supply: 65% (reserves cover only limited periods).
• Communication: 50% (satellite phones provide partial redundancy but at reduced capacity).
• Medical supplies: 60% (delays possible, though alternative routes exist).

The overall resilience of the hospital’s dependency chain is determined by the weakest dependency, i.e., 50%, associated with the communication link. This indicates that the hospital’s ability to operate in emergencies is primarily constrained by vulnerabilities in the communication system. Strengthening communication services and ensuring reliable backup solutions are, thus, essential to improving urban resilience.

The assessment results were transferred to the SCP-MULTICLIMACT platform (Figure 10), where the hospital and its dependencies were visualized within the decision-support dashboard. This enables stakeholders to identify critical vulnerabilities, prioritize interventions, and plan for both emergency preparedness and long-term resilience building. The case illustrates how the Urban Resilience Assessment module supports data-driven strategies to enhance the robustness of essential services in seismic-prone urban areas.

3.2.4. Indoor Comfort and Health Monitoring

As the LIS-Platform became operational only in 2024 under the MULTICLIMACT project, no historical data were available from the period of the 2016 Visso earthquake. To demonstrate the feasibility of the proposed approach and the potentialities of the methodology, the authors realized a proof-on-concept conducting a test campaign in a controlled office setting on 10 March 2025. The tests were conducted in compliance with the WMA Declaration of Helsinki and have been approved by the Ethics Committee of Università Politecnica delle Marche (Ancona, Italy), where they were performed. This pre-deployment experiment involved three volunteer participants and was designed to validate the data acquisition procedure and integration workflows before scaling up in the municipality of Camerino. Two types of sensing systems were employed: environmental sensors (DomX Indoor Air Quality Sensor, DomX, Kalamaria, Greece) for indoor environmental monitoring and wearable devices [41] for physiological data acquisition, resulting in a robust multimodal dataset suitable for developing predictive models, supporting real-time alerts, and informing long-term comfort optimization. The acquisition was time-synchronized with subjective comfort evaluations self-reported by participants (five-point Likert scale). Data were resampled at 25 Hz and pre-processed to (i) exclude sensor channels with over 20% missing data, (ii) interpolate short dropouts (<0.12 s), and (iii) fill longer interruptions; edge segments were trimmed. The final dataset retained more than 99.5% valid entries.

It is worth noting that physiological and activity-related parameters are critical in scenarios where environmental stressors (e.g., extreme heat or seismic disruption) may induce physiological responses before manifesting in overt discomfort. Data streams were transmitted daily to the SCP-MULTICLIMACT platform via a dedicated API; two UrbanDatasets per session were generated: one containing physiological data sampled every 10 s (≈900 entries per user over 150 min), and another aggregating microclimate readings at 30 s intervals. Each entry was precisely timestamped, allowing for accurate synchronization and cross-correlation between comfort perceptions, environmental metrics, and physiological responses, thus supporting advanced analytics for real-time discomfort detection, occupant-focused risk assessment, and data-informed decision-making for emergency management and building operations. It is worth noting that wearable sensors like smartwatches are extremely powerful in emergency conditions since they have their own batteries ensuring proper data collection. Also, even in case of connectivity loss, data would be recorded locally and then made remote accessibly again once the connection is restored. It is beyond doubt that battery duration is limited and data collection can be guaranteed only for a limited amount of time.

Figure 11 presents the indoor monitoring dashboard of the SCP-MULTICLIMACT platform, which offers a unified visualization of environmental and physiological data streams. Real-time and historical trends of key indicators are displayed, enhancing situational awareness within indoor environments.

4. Discussion

The implemented case study demonstrates how the integration of indoor monitoring data into the SCP-MULTICLIMACT platform provides several strategic advantages, and supports local decision-makers during and after seismic events, enhancing situational awareness and enabling more informed and timely responses.

Leveraging the SCPS for interoperability, the authors developed a scenario focused on earthquake monitoring and resilience assessment, demonstrating how the platform can integrate heterogeneous data sources within a unified operational framework. Through standardized interoperability protocols, the platform enables seamless data exchange—facilitating real-time monitoring, cross-domain analysis, and data-informed decision-making. End-users gain access to an integrated data ecosystem encompassing seismic event detection, damage risk indicators, urban resilience metrics, and health-related parameters from building occupants. Indeed, by enabling multi-domain data fusion, the system supports the correlation of environmental conditions with physiological responses, improving its ability to identify early signs of discomfort, fatigue, or heat-induced stress. This integrated perspective is particularly valuable when combined with other platform modules such as earthquake event detection, damage risk assessment, and resilience evaluation, allowing for a holistic understanding of occupant vulnerability within broader hazard scenarios.

The SCP-MULTICLIMACT interface delivers daily updates and visual summaries, offering timely and actionable insights to local authorities, facility managers, and emergency planners. Deviations in comfort or health parameters can be used to prioritize interventions such as ventilation adjustments, activation of cooling systems, or the identification of at-risk populations. Moreover, the structured and time-aligned nature of the data facilitates long-term resilience planning. This includes the evaluation of indoor conditions during adverse events, the refinement of HVAC system performance, and the development of retrofit strategies based on empirically observed stress responses. Ultimately, the integration of multimodal indoor monitoring within the platform enhances urban adaptive capacity, reinforcing the ability of cities to manage both sudden emergencies and long-term environmental pressures.

Nevertheless, the authors acknowledge that the study presents some limitations. First is the small dataset used to conduct it and the absence of large-scale real-time data. Second, the case study involves only the Camerino municipality, so the replicability of the approach across the geographic spectrum has not been checked. Third is the limited number of involved solutions and, consequently, of different types of data flows: the case study has been focused on the integration of indoor monitoring and seismic events data, but the proposed architecture is ready to manage other types of information related to extreme natural hazards, such as floods and heat waves.

The next step will be to address these issues and demonstrate the replicability and scalability of the proposed framework. In fact, the approach has been conceived to be scalable and tuneable to different emergency scenarios and diverse urban contexts, specifically considering the peculiar operating conditions (e.g., depending on the climatic zone) and adapting the thresholds consequently. Also, new solutions for monitoring additional hazards can be connected to SCP-MULTICLIMACT and different sensing technologies can be integrated in the LIS platform (thanks to its interoperable architecture), thus providing granular information relevant for the specific system to be monitored. This allows stakeholders to design data-driven and evidence-based emergency protocols, enhancing the resilience of entire urban areas.

At this purpose, the following actions have been planned: (i) to identify some new geographic contexts to replicate the Camerino case study, possibly using large-scale real-time data (extensive experimental campaign will be conducted to further validate the approach and including intrinsic variability in data collection, thus making the whole platform more robust); (ii) to identify and develop some case studies involving different types of natural threats to validate the capacity of the framework to integrate multi-hazards and to connect different types of solutions for monitoring heterogeneous natural events.

Two initial steps in this direction could be as follows: (i) the replication of the framework in the other pilot sites of the MULTICLIMACT project, such as Roermond and Barcelona, where natural hazards like floods and heat waves are under investigation; (ii) the active involvement of the respective municipalities to facilitate the acquisition of larger historical datasets, enable real-time simulations, and support more comprehensive testing. When these steps are completed, the proposed framework could be considered validated.

It is noteworthy that the state of the art highlights the existence of other frameworks that tackle the interoperability issues with a multi-domain scenario perspective, such as the EIF4SCC and IES-City Framework cited in Section 1. However, unlike the SCPS/SCP framework, they merely define guidelines or principles for addressing interoperability issues and do not provide technical specifications or practical implementations, nor do they explicitly address specific application or geographical contexts. In contrast, SCPS/SCP goes beyond abstract guidelines to offer a set of technical specifications and a concrete operational platform, ready to be implemented, customized, and used across different applications and urban geographic contexts, resulting in a more comprehensive and ready-to-use framework for practical implementation.

5. Conclusions

This paper illustrates the application of the SCP-MULTICLIMACT platform through a case study conducted in the municipality of Camerino within the framework of the MULTICLIMACT project.

A key strength of the SCP-MULTICLIMACT platform lies in its capacity to harmonize and manage diverse information flows across multiple infrastructure domains.

Complementing this capability, the LIS platform supports multi-domain data fusion by integrating physiological, environmental, and structural health monitoring (SHM) sensors. This holistic approach enhances system observability, enabling the detection of critical situations and the generation of early warnings when predefined thresholds are exceeded.

In emergency contexts such as earthquakes or heatwaves, the centralized availability of synchronized data allows timely updates on the status of critical infrastructure (e.g., hospitals, power grids, and communication networks) and on the health conditions of exposed populations. This level of integration ensures that emergency response actions are both rapid and evidence-based.

Ultimately, the SCP-MULTICLIMACT platform contributes to strengthening urban resilience by equipping stakeholders with interoperable tools for continuous monitoring, coordinated response, and strategic planning, thus supporting the continuity of essential services and the proactive mitigation of systemic vulnerabilities.