Management of risk assessment for Cultural Heritage on GIS based solution for the Historic center of Venice

Management of risk assessment for Cultural Heritage on GIS based solution for the Historic center of Venice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Management of risk assessment for Cultural Heritage on GIS based solution for the Historic center of Venice Caterina Balletti, Enrico Gobbi, Greta Bruschi, Francesco Trovò This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8997539/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This contribution aims to analyze the specific vulnerabilities of the historic center of Venice and to develop an evaluative method capable of taking into account both the intrinsic and extrinsic characteristics of the built environment through the use of georeferenced information systems. In particular, the study focuses on the analysis of vulnerabilities related to the gradual phenomenon of mean sea level rise and on an approach for the identification of site-specific surface temperatures based on remote sensing data. Finally, the work addresses the integration of the different types of datasets into a single spatial management platform (GIS), referenced to individual historic buildings. Risk management geographic information system architectural Heritage Venetian built Heritage Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction In order to fully understand the methodological choices adopted, it is first necessary to refer to the research questions that guided the work and to which we sought to provide answers through the use of tools considered appropriate and comprehensive, among which the contribution of this article is situated. The research aimed to construct a knowledge framework of the built heritage of the historical centre of Venice, with the objective of identifying homogeneous macro-categories characterized by shared technical, material, and morphological features (Trovò, 2025a; Trovò, 2025b ). The definition of these categories formed the basis for the subsequent phases of the work, which were oriented toward the development of an approach to risk assessment for the Venetian built heritage, structured according to criteria capable of evaluating risk as a function of homogeneous classes. In this context, the use of Multiple-Criteria Decision Analysis (MCDA) emerges as an effective tool for synthesizing complex evaluations and for comparing and prioritizing different alternatives (Chen, 2001; Cinelli, 2014). The analysis of impacts and risks to cultural heritage is provided both in terms of the effects of gradual changes in climatic parameters (temperature, precipitation, relative humidity, etc.) and to the potential damage caused by extreme hydrometeorological events (floods, intense rainfall, prolonged drought periods). For both analyses, currently available knowledge and tools were employed (Sesana et al., 2021 ; Bonazza, 2022; Bonazza et al., 2023b; Di Ciaccio et al., 2025 ). Overall, 802 buildings were analysed, classified into three homogeneous categories: palaces, churches, and bell towers. The definition of risk, based on a multi-criteria model, involved the combined assessment of several factors. On the one hand, extrinsic factors related to individual buildings were considered, including, among others, the state of conservation and the forms of protection to which the buildings are subject. On the other hand, the construction characteristics and materials of the historic buildings represented key elements for the modulation of risk multipliers, in relation both to the buildings’ location within the Venetian urban fabric and to their behavior with respect to the main environmental parameters. These parameters include solar exposure, wind exposure, and sea level rise, all of which significantly affect the vulnerability of the built heritage. It is now widely recognized that tangible cultural heritage is threatened both by the progressive alteration of climatic conditions and by the increase in extreme events: rising temperatures, together with variations in precipitation, relative humidity, and wind regimes, can have negative effects on the materials that constitute cultural assets (Cinelli, 2014). When considering the area under the jurisdiction of the Municipality of Venice, these impacts are particularly evident. The entire municipal territory is directly affected by hazards related to heatwaves, river flooding, urban inundation, exceptional tides, and very intense winds. The European Environment Agency (EEA) itself identified these phenomena in 2016 as the main effects of climate change for the Mediterranean region (EEA, 2016). Referring back to the broader framework, it is important to clarify that the definition of risk requires “a holistic and multidisciplinary approach in order to identify all the critical parameters and factors that can put it in danger in a changing environment” (Bonazza et al., 2023a). In this context, the use of vulnerability values derived from quantitative data is not always feasible; consequently, most of the models currently in use are based on methodologies of a predominantly qualitative nature (Bonazza et al., 2023). For the analysis and management of risk in the city of Venice, the main challenge lay in combining information across different scales and heterogeneous levels in order to integrate them into a single system. The implementation of a unified GIS-based project applied to the Dorsoduro district made it possible to systematically organize multi-scale data, including vulnerability parameters and spatial information such as thermal and altimetric data. Its use also enabled the identification and visualization of data through thematic maps, produced by analyzing the different informational layers and performing spatial queries . In the field of management, control and intervention planning, GIS technology has been a valuable ally to public bodies and administrations in recent decades (Jones et al. 2019), specifically in obtaining thematic and stable data for the formulation of large-scale intervention strategies. UNESCO recognizes that the duty of ensuring the identification, protection, conservation, presentation, and transmission to future generations of cultural and natural heritage (UNESCO, 1972 ) lies with each Member State, with the aim of establishing a unified process for heritage conservation. Accordingly, “to ensure that effective and active measures are taken for the protection, conservation and presentation of the cultural and natural heritage situated on its territory, each State Party to this Convention shall endeavor, in so far as possible” (UNESCO, 1972 ), encouraging States Parties to: “(a) to adopt a general policy which aims to give the cultural and natural heritage a function in the life of the community and to integrate the protection of that heritage into comprehensive planning programmes; (b) to set up within its territories, where such services do not exist, one or more services for the protection, conservation and presentation of the cultural and natural heritage with an appropriate staff and possessing the means to discharge their functions; (c) to develop scientific and technical studies and research and to work out such operating methods as will make the State capable of counteracting the dangers that threaten its cultural or natural heritage; (d) to take the appropriate legal, scientific, technical, administrative and financial measures necessary for the identification, protection, conservation, presentation and rehabilitation of this heritage; […]” (UNESCO, 1972 ). In light of the above, it is clear that the use of GIS applied to cultural and natural heritage offers real opportunities to respond to the recommendations and objectives defined by the World Cultural and Natural Heritage Convention (Garzulino, 2021). Assessment of quantitative vulnerabilities This article initially proposes a possible approach for the identification of vulnerability of a quantitative nature with respect to the gradual phenomenon of mean sea-level rise. To this end, a Digital Terrain Model (DTM) with a spatial resolution of 25 cm was employed, developed by the Metropolitan City of Venice. The dataset consists of a raster representation of surface elevations, from which it is possible to extract morphological information for the study area, useful for understanding and generating altimetric terrain models that exclude physical–environmental and morpho-vegetational elements present on the surface (Musco et al. 2021). Table 1 Data used for the definition of the new DTM for the historic center of Venice. Value Category Description Type and Resolution Workflow SLR DTM Regione Veneto Raster-Tiff-20cm Elevation mesh model Elevation contour lines Rilievo Insula Spa Vectorial The DTM allows for the identification of urban areas subject to flooding in relation to different forecasted water levels. To improve elevation information in the historic center of Venice, it was decided to integrate the raster data using the pavement survey of the historic center of the island of Venice, carried out by Insula S.p.A. RAMSES. This project relied on an altimetric survey and 3D scanning of the public areas of the insula, collecting data with centimeter-level accuracy. This allows for refinement of the DTM in areas such as calli, rive, fondamenta, campi, and campielli (that is, the different names of Venetian open spaces - streets, squares, banks). The evaluation procedure involved projecting the raster data into the Roma 40 reference system, Gauss-Boaga projection, known in GIS environments as Monte Mario / Italy Zone 2 (EPSG authority ID: 3004), which also served as the basis for the subsequent database work. Subsequently, the raster data were segmented to isolate exclusively the land areas, that is, those areas subject to different conditions of environmental criticality. At this stage, the data derived from the laser scanner survey were processed, starting from the vector data represented by the contour lines (with 1 cm detail). In the GIS environment, a process was carried out to extract elevation points from these contour lines in order to generate a three-dimensional model using triangular meshes. This was achieved through the implementation of a TIN (Triangulated Irregular Network) algorithm written in Python. The mesh model was then converted into a raster format. The new model was subsequently integrated with the DTM of the islands of Venice. Once the new digital model was segmented according to the forecasted elevation levels, it enabled the extraction of different levels of criticality, to be used as the basis for defining flooding scenarios based on predictive forecasts. The analysis considered two different conditions: initially, vulnerability was assessed without accounting for the support of the mobile barrier mitigation system (MoSE), which became operational starting from 3rd October 2023, allowing for monitoring and mitigating the impact of exceptional tides on the city from an initial threshold of 130 cm, now reduced to 110 cm. The second scenario considers the benefits introduced by the mitigation system, particularly in the short and medium term, and, on the other hand, the emerging vulnerability under sea-level rise scenarios, as outlined by the RCP 8.5 model, in which projected sea-level increases exceed the operational limits of the barriers themselves. Another area of investigation was dedicated to the assessment of vulnerability through the use of remote sensing techniques as an approach for developing a quantitative temperature analysis. Evaluating the distribution of temperatures at the urban scale allows for an understanding of how these change in relation to differences in exposure. A theoretical subdivision of degradation mechanisms was adopted, assuming a single degradation mechanism as a reference—although somewhat artificial, this approach is functional for comprehension (Capponi, 2004), while acknowledging that these processes generally act in a combined and synergistic manner. Temperature assessment represents a valuable tool for identifying, within the built heritage, the areas most exposed and subject to elevated thermal conditions. Stone materials, for example, may experience stress not only from static or dynamic loads, but also from thermal cycles, which induce differential expansion within the material, particularly in anisotropic materials (Fiorani, 2009 ; Bonazza, 2009). In the present study, specific satellite data were selected to analyze surface temperatures and evaluate their effects on the Venetian built heritage, adopting an integrated approach that combines different spatial and spectral resolutions. Table 2 Data used for the definition of the NDVI and NDBI indices and for the analysis of surface temperatures . Value Category Description Type and Resolution Workflow SLT Remote sensing Multispectral Satellite Image (WV2_OPER_WV-110__2A_20110413T102815_N45-358_E012-293_4061) Raster Worldwide 2 NDBI, NDVI Remote sensing Multispectral Satellite Image (LC09_L2SP_192028_20240825_20240826_02_T1) Raster Landsat 9 SLT Spatial resolution is determined by the ground size of the elementary area from which reflected electromagnetic radiation is measured, also defined as the instantaneous field of view. Each elementary area in an image generated by a sensor is represented by a cell within which the radiation value is unique (Brivio, 2006). Initially, images from the Landsat 9 sensor were used. Although characterized by moderate spatial resolution (up to 30 meters for multispectral bands and 100 meters for thermal bands, recalibrated to 30 meters), they offer the possibility of utilizing specific thermal bands. The Thermal Infrared Sensor (TIRS) bands, in particular, allow for monitoring surface temperature due to their sensitivity in the thermal infrared wavelengths (Brivio, 2006). Landsat 9’s wide spectral coverage makes it a widely used tool for large-scale analyses, enabling the identification of significant thermal patterns, even if spatial precision is lower compared to other sensors. The second satellite data source used was WorldView-2, a high-resolution satellite capable of providing panchromatic images with a spatial resolution of 0.40 meters and multispectral images with a resolution of 1.6 meters. The high spatial precision of the panchromatic images allows for detailed analysis of the urban fabric and building structures, while the eight multispectral bands, covering the visible and near-infrared range, provide additional information for the characterization of surface materials and their responses to thermal variations. The combined use of these two satellite sources allowed the limitations of Landsat 9’s spatial resolution to be compensated by the detail of WorldView-2 images, while ensuring a comprehensive multispectral analysis and evaluation of the thermal and structural conditions of the studied heritage. The Landsat 9 data were integrated into the territorial data management platform, projected into the reference system consistent with the municipal dataset of Venice (Monte Mario / Italy Zone 2, EPSG authority ID: 3004), to ensure correct georeferencing and compatibility with the other datasets already present. For the higher-resolution WorldView-2 data, careful preprocessing was required. In particular, geometric distortion corrections were performed to preserve image accuracy. To analyze the distribution and composition of the Venetian built environment and to model thermal data according to different territorial conditions, spectral indices were used. These indices are calculated by combining spectral bands acquired from the sensors, each capturing specific wavelengths of electromagnetic radiation reflected from the Earth’s surface, allowing for analysis and monitoring of surface characteristics. Specifically, the Normalized Difference Vegetation Index (NDVI) was employed. This vegetation index is based on the normalized difference of reflectance values (ρ) in the near-infrared (0.75–1.11 µm) and red (0.58–0.75 µm) bands. It allows for assessing the presence, density, and health of vegetation on the surface (Fig. 5 ). The Normalized Difference Built-up Index (NDBI) was also employed to analyze urbanized or built-up areas. This index is based on the mid-infrared (MIR) and near-infrared (NIR) spectral bands. Built surfaces reflect more energy in the mid-infrared than in the near-infrared, whereas vegetation reflects more energy in the near-infrared than in the mid-infrared. This contrast allows for easy differentiation between urbanized areas and vegetation. To evaluate the distribution of temperatures in relation to the island’s context, thermal data were processed from the Landsat dataset using a Regression Kriging approach. In this methodology, the Land Surface Temperature (LST) data were used as the dependent variable, while the previously derived NDVI and NDBI indices were used as independent variables. This allowed for the integration of spatial and spectral information in order to estimate the distribution of surface temperatures. Structuring of the information system The GIS information system was used to organize and standardize the data for calculating vulnerabilities and risk parameters. Specifically, the system was aligned with the one already in use by the Metropolitan City of Venice, making the new dataset interoperable with the existing one. As a representative case study, the Dorsoduro district was selected for its relevance in the urban context and its ability to reflect the typological and structural characteristics of Venetian built heritage. As outlined in the introduction, the system allows for the storage of the large amount of data associated with each building, making it comparable and easily inspectable. The adopted configuration includes two main sets of information: the first informational level concerns the intrinsic characteristics of the building, comprising descriptions of its protection status, conservation condition, and maintenance. The second level focuses on extrinsic characteristics, outlining calculated values for different vulnerabilities. These were computed based on the constitutive materials of the historic buildings and their varied responses to climatic factors, such as mean sea-level rise, stresses from strong winds, precipitation, and solar exposure. To ensure integrability, the new attributes were coded according to the same structure as the existing dataset. This includes the use of alphanumeric data types with semantic consistency in the naming conventions. Once the update in tabular form was completed, it was associated with the main dataset through a shared unique key (FID). This linkage ensures that each record in the existing dataset corresponds uniquely to a set of values in the new attribute table. In a preliminary phase, some features of the municipal database were appropriately modified and reorganized to ensure greater consistency between the available data and the macro-categories of the built environment adopted in this study. This operation allowed the information structure to be aligned with the constitutive elements of the building, which form the basis for the detailed risk analysis. The configuration thus structured provides high flexibility in assigning different values and, above all, in updating them over time. In this way, data related to the conservation status of the building can be modified and updated based on new information or survey campaigns; such updates are automatically reflected in the vulnerability values, which are recalculated consistently and dynamically. Table 3 Description of the attributes used in the risk assessment for the built heritage of the Dorsoduro district, Venice. COD Value Description EDF_NM Building Name Description : Each feature was associated with the building name, allowing for precise spatial positioning of the built heritage. EDF_TY Macro-category of the Building Description : Based on the macro-categories defined during the analysis phase, elaborated by considering building typology and original use (identified within the Dorsoduro district). A01 Palazzi A02 Chiese A03 Campanili A04 Università EDF_VAL_ELCON Distinguishing Features Description : Presence of specific distinguishing elements of significant artistic value. 0 Absence of Distinctive Features 0.5 Presence of Distinctive Features EDF_VAL_MAN Maintenance Condition Description : Identification and assessment of the maintenance condition of the built heritage, where possible integrated with information from the philological surveys prepared by the Veneto Region. 2 Abandoned 1,7 Not Declared 1,5 Past Interventions 1 Recent Interventions EDF_VAL_CONS State of Conservation Description : Identification and assessment of the conservation status of the built heritage, where possible integrated with information from the philological surveys prepared by the Veneto Region. 2,2 Good 2,4 Moderate 2,6 Bad EDF_VAL_VNC Type of Heritage Protection Description : Identification within the database of the Superintendence for Fine Arts and Landscape for the Municipality of Venice and the Lagoon. 0 DLgs. 42/2004 parte III 2,2 DLgs. 42/2004 parte II EDF_VAL_AFF Facade Orientation Description : Identification of homogeneous macro-areas in relation to their location within the Venetian urban fabric. 2 Urban Fabric 3 Waterfront 4 Urban Edge EXP_SOL Solar Exposure Description : Qualitative assessment of the vulnerability of buildings in relation to sun exposure, differentiated according to their constituent materials. 2 Stone Buildings 1 Plaster and/or Concrete or brick Building EXP_OMB Shading Description : Qualitative assessment of the vulnerability of buildings in relation to shading, differentiated according to their constituent materials. 1,5 Stone Buildings 1,5 Brick Buildings 1,5 Plaster and/or Concrete Building EXP_PGG Rain Description : Qualitative assessment of the vulnerability of buildings in relation to heavy rainfall, differentiated according to their constituent materials and exposure. 2,5 High Building 2 Building Situated on the Edge 1,5 Building Situated on campo 1 Building Situated on calle EXP_VNT Wind Description : Qualitative assessment of the vulnerability of buildings in relation to strong winds, differentiated according to their exposure. 2,5 High Building 2 Building Situated on the Edge 1,5 Building Situated on campo 1 Building Situated on calle EXP_SLR Vulnerability to Gradual Variation – SLR Description : Quantitative assessment of vulnerability in relation to gradual changes due to mean sea-level rise. 2 SLR_2030 With MoSE activation 6 SLR_2050 With MoSE activation 5 SLR_2100 With MoSE activation 4 SLR_2100 + With MoSE activation EDF_VAL_BN Materiali di pregio come materiale libraio o opere permanenti Descrizione : Presenza di materiale libraio o opere permanenti all’interno del fabbricato. 0 No presence 1 Presence VAL_TOT Built Heritage Risk Value Description : Risk value classified into five categories. > 24,1 Vert High 24 < x < 21,2 High 21,1 < x < 18,2 Medium 18,1 < x < 15,3 Moderate 15,2< Low Discussion and Conclusions The topic of specific risk analysis applied to built cultural heritage inherently opens up a multidisciplinary field of investigation, allowing for the integration of aspects related to conservation and mitigation — even at the architectural scale — with those of prevention (Fiorani, 2023; Mioli, 2023). In this context, the present work sought to develop a largely unexplored research area, namely the quantitative assessment of the vulnerabilities of built heritage, which to date has often been predominantly qualitative. This condition is due both to the scale at which risk assessments are often conducted and to the high heterogeneity of the cultural and natural heritage considered, whose complexity makes it difficult to interpret individual characteristics at the adopted reference scale (Bonazza et al., 2023). The distinction of different categories of the built environment and their respective morphologies in the Venetian context allowed the analysis to be increasingly detailed, placing the individual historic-architectural artifact at the center, with its specific characteristics and intrinsic peculiarities. This approach promoted the development of a potential risk monitoring system based on an information system that can be continuously implemented and updated. This aspect is particularly relevant for public authorities, which can regulate and calibrate mitigation strategies according to specific territorial areas, enabling a more conscious and targeted use of resources, especially in relation to the most vulnerable areas. In this regard, the scale factor of the analysis, initially highlighted, plays a determining role not only in understanding risk but also in implementing intervention policies, which can range from broad strategies to precise and targeted measures on individual critical elements. The work also opens the door to potential future developments and integrations. First, expanding the information database to cover the entire historic center would allow a more extensive testing of the actual applicability and effectiveness of the proposed system. At the same time, the definition of risk values and related multipliers — not explored in detail here — would require an additional phase of review and calibration. The use of GIS also allows for interpretations in an integrated perspective with the Internet of Things (IoT), introducing processes based on the continuous updating of specific parameters, with the goal of making the analysis dynamic over time and transforming it into an active monitoring tool for the historic center. In this direction, the integration of BIM models within the GIS environment would allow the construction of an even more detailed information system, useful for the knowledge, storage, and management of data related to historic artifacts, directly linking them to the most relevant climatic indices. Finally, the use of remote sensing as a tool for analyzing specific vulnerabilities represents an additional area of development, particularly if implemented as a monitoring system based on open-source data. A particularly interesting area of experimentation could involve the transposition of raster data through the assignment of values in the form of tabular attributes, thus promoting greater integration between spatial and informational data. Declarations This contribution is developed within the framework of a Collaboration Agreement (pursuant to Art. 15 of Law 241/1990, as amended) aimed at further investigating the impacts of climate change on the monumental heritage of Venice, funded by MASE (Ministry of the Environment and Energy Security). The project is led by the Università Iuav di Venezia, Department of Cultures of the Project, with Prof. Francesco Trovò as project coordinator, Prof. Greta Bruschi as co-coordinator, Arch. Enrico Gobbi as research fellow, and the Geomatics Laboratory under the responsibility of Prof. Caterina Balletti. Author Contribution Conceptualisation, methodology, C.B., G.B., E.G., F.T.; software, E.G.; writing—review, C.B., G.B., E.G., F.T.; project administration, funding acquisition, F.T. All authors have read and agreed to the published version of the manuscript. Acknowledgement This research was carried out within a Collaboration Agreement (Art. 15, Law 241/1990) funded by the Ministry of the Environment and Energy Security (MASE), aimed at investigating the impacts of climate change on the monumental heritage of Venice, and led by the Università Iuav di Venezia. This article presents part of the results developed during the research activity; the authors wish to thank the Municipality of Venice for its support and acknowledge the valuable collaboration of Alessandra Bonazza. Data Availability The authors declare that the data supporting the findings of this study are available in the document. References Bonazza A (2022) Sustainable Heritage and Climate Change. 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Adopted by the General Conference at its seventeenth session, Paris, 16 November 1972 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 15 Mar, 2026 Editor assigned by journal 10 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 28 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8997539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606433997,"identity":"2b90ecbd-eb4a-4eb7-a65e-71c5e6702c9f","order_by":0,"name":"Caterina Balletti","email":"","orcid":"","institution":"Università Iuav di Venezia","correspondingAuthor":false,"prefix":"","firstName":"Caterina","middleName":"","lastName":"Balletti","suffix":""},{"id":606433998,"identity":"21f73299-90fc-4b6a-9e9f-854f96a209f9","order_by":1,"name":"Enrico Gobbi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIie3QPQ6CMBTA8deYMDWyGQhGr9DVBOEqGBJcZGdkYpK4YuQgjG06uBBn1t6AETcbkAQHPkaH/ocmHX55fQVQqf402p4GBloDbDVAMXhLCcsAcEfmTE9WWJLuNkF0KxW0icC5WSnjx8LG6w2PQTTjxMyfhF1L8O/5y+NhGWDNOk0/jFQBcJSAT6oL4WHC54n7Qw5LCDE64rQELSGGnCJ3MTxT7sLS7y7UC8aJngWruolsV34dr9+Fvds/zkI09jjph8nHyFDcXekskLkwJCqVSqUa9gFBz1Gro5LYoQAAAABJRU5ErkJggg==","orcid":"","institution":"Politecnico di Milano","correspondingAuthor":true,"prefix":"","firstName":"Enrico","middleName":"","lastName":"Gobbi","suffix":""},{"id":606433999,"identity":"4c025128-8471-42b9-8455-d94774f221e3","order_by":2,"name":"Greta Bruschi","email":"","orcid":"","institution":"Università Iuav di Venezia","correspondingAuthor":false,"prefix":"","firstName":"Greta","middleName":"","lastName":"Bruschi","suffix":""},{"id":606434000,"identity":"934395d0-aff2-4381-b576-c75ae95307c1","order_by":3,"name":"Francesco Trovò","email":"","orcid":"","institution":"Università Iuav di Venezia","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Trovò","suffix":""}],"badges":[],"createdAt":"2026-02-28 19:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8997539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8997539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104824599,"identity":"69a677bb-63fd-40fb-a69d-0e8b0d7aa198","added_by":"auto","created_at":"2026-03-17 15:12:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1405287,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow diagram for processing the altimetric model used. Specifically, at the top left is the survey carried out by Insula Spa with the extrapolation of elevation points and the processing of a new mesh model. At the top right is the mesh model of the DTM of the Municipality of Venice. At the bottom is the new model in raster format.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/1e09956674aa08d59d376870.jpg"},{"id":104824595,"identity":"0c45dd86-207b-48a0-b026-5fdf79745650","added_by":"auto","created_at":"2026-03-17 15:12:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5957424,"visible":true,"origin":"","legend":"\u003cp\u003eRaster clipped at the 2030 critical water level without MoSE intervention\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/faa0b9c43f1ace89e5cd8178.jpg"},{"id":104835535,"identity":"86b3771a-dcee-45dc-9c93-0c0a1baf0562","added_by":"auto","created_at":"2026-03-17 17:45:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5903262,"visible":true,"origin":"","legend":"\u003cp\u003eRaster clipped at the 2050 critical water level without MoSE intervention\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/c8f611c9a3da14cca5a79a48.jpg"},{"id":104824603,"identity":"c886e3ce-168b-4202-a464-8d841763fae3","added_by":"auto","created_at":"2026-03-17 15:12:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5249857,"visible":true,"origin":"","legend":"\u003cp\u003eRaster clipped at the 2100 critical water level without MoSE intervention\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/ca90eac3779514216dadb98e.jpg"},{"id":104824600,"identity":"cf30f9e7-cdae-4daa-a193-890368e25ea5","added_by":"auto","created_at":"2026-03-17 15:12:43","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3582332,"visible":true,"origin":"","legend":"\u003cp\u003eCalcolo dell’indice NDVI normalizzato nell’intervallo [0-1].\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/362622c4591eb55c39501e0e.jpg"},{"id":104824596,"identity":"a79d71b5-8996-4274-85a2-c4a1670ebeb8","added_by":"auto","created_at":"2026-03-17 15:12:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":308273,"visible":true,"origin":"","legend":"\u003cp\u003eSurface temperature distribution through Kriging regression\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/6fa2b7697d1acbc4783490f7.jpg"},{"id":104824594,"identity":"1607c3a7-7d67-432c-808c-96ff41b03a47","added_by":"auto","created_at":"2026-03-17 15:12:40","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5244245,"visible":true,"origin":"","legend":"\u003cp\u003eDataset map: Conservation Status layer\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/d39db3bbef719166afd57e01.jpg"},{"id":104824604,"identity":"66b01360-8913-4a5e-a224-e01147617100","added_by":"auto","created_at":"2026-03-17 15:12:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2262976,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of the integrated database, including research data coded to match the structure of the existing dataset\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/35aeeb176a5fd34f80f424a3.png"},{"id":104824593,"identity":"2af91f55-2235-4c28-9f6a-b5d814388496","added_by":"auto","created_at":"2026-03-17 15:12:39","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":5241980,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the dataset: Rainfall Vulnerability layer\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/b11afcb41890423de159c701.jpg"},{"id":104824601,"identity":"d94af80d-2d70-40e6-9fb8-e461750e340b","added_by":"auto","created_at":"2026-03-17 15:12:44","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":5005527,"visible":true,"origin":"","legend":"\u003cp\u003eDataset map: Built Environment Risk Values layer\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/6a57bec1fd85a91daaeb3378.jpg"},{"id":106401641,"identity":"f88e5b10-39aa-441d-b75f-3c1231978edf","added_by":"auto","created_at":"2026-04-08 09:08:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":41317979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8997539/v1/ed01477f-4864-4e9c-88da-1cee0df914b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Management of risk assessment for Cultural Heritage on GIS based solution for the Historic center of Venice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn order to fully understand the methodological choices adopted, it is first necessary to refer to the research questions that guided the work and to which we sought to provide answers through the use of tools considered appropriate and comprehensive, among which the contribution of this article is situated.\u003c/p\u003e \u003cp\u003eThe research aimed to construct a knowledge framework of the built heritage of the historical centre of Venice, with the objective of identifying homogeneous macro-categories characterized by shared technical, material, and morphological features (Trov\u0026ograve;, 2025a; Trov\u0026ograve;, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). The definition of these categories formed the basis for the subsequent phases of the work, which were oriented toward the development of an approach to risk assessment for the Venetian built heritage, structured according to criteria capable of evaluating risk as a function of homogeneous classes. In this context, the use of Multiple-Criteria Decision Analysis (MCDA) emerges as an effective tool for synthesizing complex evaluations and for comparing and prioritizing different alternatives (Chen, 2001; Cinelli, 2014).\u003c/p\u003e \u003cp\u003eThe analysis of impacts and risks to cultural heritage is provided both in terms of the effects of gradual changes in climatic parameters (temperature, precipitation, relative humidity, etc.) and to the potential damage caused by extreme hydrometeorological events (floods, intense rainfall, prolonged drought periods). For both analyses, currently available knowledge and tools were employed (Sesana et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bonazza, 2022; Bonazza et al., 2023b; Di Ciaccio et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, 802 buildings were analysed, classified into three homogeneous categories: palaces, churches, and bell towers. The definition of risk, based on a multi-criteria model, involved the combined assessment of several factors. On the one hand, extrinsic factors related to individual buildings were considered, including, among others, the state of conservation and the forms of protection to which the buildings are subject. On the other hand, the construction characteristics and materials of the historic buildings represented key elements for the modulation of risk multipliers, in relation both to the buildings\u0026rsquo; location within the Venetian urban fabric and to their behavior with respect to the main environmental parameters.\u003c/p\u003e \u003cp\u003eThese parameters include solar exposure, wind exposure, and sea level rise, all of which significantly affect the vulnerability of the built heritage. It is now widely recognized that tangible cultural heritage is threatened both by the progressive alteration of climatic conditions and by the increase in extreme events: rising temperatures, together with variations in precipitation, relative humidity, and wind regimes, can have negative effects on the materials that constitute cultural assets (Cinelli, 2014). When considering the area under the jurisdiction of the Municipality of Venice, these impacts are particularly evident. The entire municipal territory is directly affected by hazards related to heatwaves, river flooding, urban inundation, exceptional tides, and very intense winds. The European Environment Agency (EEA) itself identified these phenomena in 2016 as the main effects of climate change for the Mediterranean region (EEA, 2016).\u003c/p\u003e \u003cp\u003eReferring back to the broader framework, it is important to clarify that the definition of risk requires \u0026ldquo;a holistic and multidisciplinary approach in order to identify all the critical parameters and factors that can put it in danger in a changing environment\u0026rdquo; (Bonazza et al., 2023a). In this context, the use of vulnerability values derived from quantitative data is not always feasible; consequently, most of the models currently in use are based on methodologies of a predominantly qualitative nature (Bonazza et al., 2023).\u003c/p\u003e \u003cp\u003eFor the analysis and management of risk in the city of Venice, the main challenge lay in combining information across different scales and heterogeneous levels in order to integrate them into a single system. The implementation of a unified GIS-based project applied to the Dorsoduro district made it possible to systematically organize multi-scale data, including vulnerability parameters and spatial information such as thermal and altimetric data. Its use also enabled the identification and visualization of data through thematic maps, produced by analyzing the different informational layers and performing spatial \u003cem\u003equeries\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn the field of management, control and intervention planning, GIS technology has been a valuable ally to public bodies and administrations in recent decades (Jones et al. 2019), specifically in obtaining thematic and stable data for the formulation of large-scale intervention strategies. UNESCO recognizes that the duty of ensuring the identification, protection, conservation, presentation, and transmission to future generations of cultural and natural heritage (UNESCO, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1972\u003c/span\u003e) lies with each Member State, with the aim of establishing a unified process for heritage conservation. Accordingly, \u0026ldquo;to ensure that effective and active measures are taken for the protection, conservation and presentation of the cultural and natural heritage situated on its territory, each State Party to this Convention shall endeavor, in so far as possible\u0026rdquo; (UNESCO, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1972\u003c/span\u003e), encouraging States Parties to: \u0026ldquo;(a) to adopt a general policy which aims to give the cultural and natural heritage a function in the life of the community and to integrate the protection of that heritage into comprehensive planning programmes; (b) to set up within its territories, where such services do not exist, one or more services for the protection, conservation and presentation of the cultural and natural heritage with an appropriate staff and possessing the means to discharge their functions; (c) to develop scientific and technical studies and research and to work out such operating methods as will make the State capable of counteracting the dangers that threaten its cultural or natural heritage; (d) to take the appropriate legal, scientific, technical, administrative and financial measures necessary for the identification, protection, conservation, presentation and rehabilitation of this heritage; [\u0026hellip;]\u0026rdquo; (UNESCO, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1972\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn light of the above, it is clear that the use of GIS applied to cultural and natural heritage offers real opportunities to respond to the recommendations and objectives defined by the World Cultural and Natural Heritage Convention (Garzulino, 2021).\u003c/p\u003e"},{"header":"Assessment of quantitative vulnerabilities","content":"\u003cp\u003eThis article initially proposes a possible approach for the identification of vulnerability of a quantitative nature with respect to the gradual phenomenon of mean sea-level rise. To this end, a Digital Terrain Model (DTM) with a spatial resolution of 25 cm was employed, developed by the Metropolitan City of Venice. The dataset consists of a raster representation of surface elevations, from which it is possible to extract morphological information for the study area, useful for understanding and generating altimetric terrain models that exclude physical\u0026ndash;environmental and morpho-vegetational elements present on the surface (Musco et al. 2021).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData used for the definition of the new DTM for the historic center of Venice.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eType and Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorkflow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegione Veneto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRaster-Tiff-20cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElevation mesh model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation contour lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRilievo Insula Spa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVectorial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe DTM allows for the identification of urban areas subject to flooding in relation to different forecasted water levels. To improve elevation information in the historic center of Venice, it was decided to integrate the raster data using the pavement survey of the historic center of the island of Venice, carried out by Insula S.p.A. RAMSES. This project relied on an altimetric survey and 3D scanning of the public areas of the insula, collecting data with centimeter-level accuracy.\u003c/p\u003e \u003cp\u003eThis allows for refinement of the DTM in areas such as \u003cem\u003ecalli, rive, fondamenta, campi, and campielli\u003c/em\u003e (that is, the different names of Venetian open spaces - streets, squares, banks).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe evaluation procedure involved projecting the raster data into the Roma 40 reference system, Gauss-Boaga projection, known in GIS environments as Monte Mario / Italy Zone 2 (EPSG authority ID: 3004), which also served as the basis for the subsequent database work.\u003c/p\u003e \u003cp\u003eSubsequently, the raster data were segmented to isolate exclusively the land areas, that is, those areas subject to different conditions of environmental criticality.\u003c/p\u003e \u003cp\u003eAt this stage, the data derived from the laser scanner survey were processed, starting from the vector data represented by the contour lines (with 1 cm detail). In the GIS environment, a process was carried out to extract elevation points from these contour lines in order to generate a three-dimensional model using triangular meshes. This was achieved through the implementation of a TIN (Triangulated Irregular Network) algorithm written in Python.\u003c/p\u003e \u003cp\u003eThe mesh model was then converted into a raster format. The new model was subsequently integrated with the DTM of the islands of Venice.\u003c/p\u003e \u003cp\u003eOnce the new digital model was segmented according to the forecasted elevation levels, it enabled the extraction of different levels of criticality, to be used as the basis for defining flooding scenarios based on predictive forecasts.\u003c/p\u003e \u003cp\u003eThe analysis considered two different conditions: initially, vulnerability was assessed without accounting for the support of the mobile barrier mitigation system (MoSE), which became operational starting from 3rd October 2023, allowing for monitoring and mitigating the impact of exceptional tides on the city from an initial threshold of 130 cm, now reduced to 110 cm. The second scenario considers the benefits introduced by the mitigation system, particularly in the short and medium term, and, on the other hand, the emerging vulnerability under sea-level rise scenarios, as outlined by the RCP 8.5 model, in which projected sea-level increases exceed the operational limits of the barriers themselves.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnother area of investigation was dedicated to the assessment of vulnerability through the use of remote sensing techniques as an approach for developing a quantitative temperature analysis. Evaluating the distribution of temperatures at the urban scale allows for an understanding of how these change in relation to differences in exposure. A theoretical subdivision of degradation mechanisms was adopted, assuming a single degradation mechanism as a reference\u0026mdash;although somewhat artificial, this approach is functional for comprehension (Capponi, 2004), while acknowledging that these processes generally act in a combined and synergistic manner.\u003c/p\u003e \u003cp\u003eTemperature assessment represents a valuable tool for identifying, within the built heritage, the areas most exposed and subject to elevated thermal conditions. Stone materials, for example, may experience stress not only from static or dynamic loads, but also from thermal cycles, which induce differential expansion within the material, particularly in anisotropic materials (Fiorani, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bonazza, 2009).\u003c/p\u003e \u003cp\u003eIn the present study, specific satellite data were selected to analyze surface temperatures and evaluate their effects on the Venetian built heritage, adopting an integrated approach that combines different spatial and spectral resolutions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eData used for the definition of the NDVI and NDBI indices and for the analysis of surface temperatures\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eType and Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorkflow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRemote sensing\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultispectral Satellite Image (WV2_OPER_WV-110__2A_20110413T102815_N45-358_E012-293_4061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRaster\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWorldwide 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNDBI, NDVI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRemote sensing\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultispectral Satellite Image (LC09_L2SP_192028_20240825_20240826_02_T1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRaster\u003c/em\u003e\u003c/p\u003e \u003cp\u003eLandsat 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSpatial resolution is determined by the ground size of the elementary area from which reflected electromagnetic radiation is measured, also defined as the instantaneous field of view. Each elementary area in an image generated by a sensor is represented by a cell within which the radiation value is unique (Brivio, 2006).\u003c/p\u003e \u003cp\u003eInitially, images from the Landsat 9 sensor were used. Although characterized by moderate spatial resolution (up to 30 meters for multispectral bands and 100 meters for thermal bands, recalibrated to 30 meters), they offer the possibility of utilizing specific thermal bands. The Thermal Infrared Sensor (TIRS) bands, in particular, allow for monitoring surface temperature due to their sensitivity in the thermal infrared wavelengths (Brivio, 2006). Landsat 9\u0026rsquo;s wide spectral coverage makes it a widely used tool for large-scale analyses, enabling the identification of significant thermal patterns, even if spatial precision is lower compared to other sensors.\u003c/p\u003e \u003cp\u003eThe second satellite data source used was WorldView-2, a high-resolution satellite capable of providing panchromatic images with a spatial resolution of 0.40 meters and multispectral images with a resolution of 1.6 meters. The high spatial precision of the panchromatic images allows for detailed analysis of the urban fabric and building structures, while the eight multispectral bands, covering the visible and near-infrared range, provide additional information for the characterization of surface materials and their responses to thermal variations. The combined use of these two satellite sources allowed the limitations of Landsat 9\u0026rsquo;s spatial resolution to be compensated by the detail of WorldView-2 images, while ensuring a comprehensive multispectral analysis and evaluation of the thermal and structural conditions of the studied heritage.\u003c/p\u003e \u003cp\u003eThe Landsat 9 data were integrated into the territorial data management platform, projected into the reference system consistent with the municipal dataset of Venice (Monte Mario / Italy Zone 2, EPSG authority ID: 3004), to ensure correct georeferencing and compatibility with the other datasets already present. For the higher-resolution WorldView-2 data, careful preprocessing was required. In particular, geometric distortion corrections were performed to preserve image accuracy.\u003c/p\u003e \u003cp\u003eTo analyze the distribution and composition of the Venetian built environment and to model thermal data according to different territorial conditions, spectral indices were used. These indices are calculated by combining spectral bands acquired from the sensors, each capturing specific wavelengths of electromagnetic radiation reflected from the Earth\u0026rsquo;s surface, allowing for analysis and monitoring of surface characteristics.\u003c/p\u003e \u003cp\u003eSpecifically, the Normalized Difference Vegetation Index (NDVI) was employed. This vegetation index is based on the normalized difference of reflectance values (ρ) in the near-infrared (0.75\u0026ndash;1.11 \u0026micro;m) and red (0.58\u0026ndash;0.75 \u0026micro;m) bands. It allows for assessing the presence, density, and health of vegetation on the surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Normalized Difference Built-up Index (NDBI) was also employed to analyze urbanized or built-up areas. This index is based on the mid-infrared (MIR) and near-infrared (NIR) spectral bands. Built surfaces reflect more energy in the mid-infrared than in the near-infrared, whereas vegetation reflects more energy in the near-infrared than in the mid-infrared. This contrast allows for easy differentiation between urbanized areas and vegetation.\u003c/p\u003e \u003cp\u003eTo evaluate the distribution of temperatures in relation to the island\u0026rsquo;s context, thermal data were processed from the Landsat dataset using a Regression Kriging approach. In this methodology, the Land Surface Temperature (LST) data were used as the dependent variable, while the previously derived NDVI and NDBI indices were used as independent variables. This allowed for the integration of spatial and spectral information in order to estimate the distribution of surface temperatures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Structuring of the information system","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003eThe GIS information system was used to organize and standardize the data for calculating vulnerabilities and risk parameters. Specifically, the system was aligned with the one already in use by the Metropolitan City of Venice, making the new dataset interoperable with the existing one. As a representative case study, the Dorsoduro district was selected for its relevance in the urban context and its ability to reflect the typological and structural characteristics of Venetian built heritage.\u003c/p\u003e \u003cp\u003eAs outlined in the introduction, the system allows for the storage of the large amount of data associated with each building, making it comparable and easily inspectable. The adopted configuration includes two main sets of information: the first informational level concerns the intrinsic characteristics of the building, comprising descriptions of its protection status, conservation condition, and maintenance. The second level focuses on extrinsic characteristics, outlining calculated values for different vulnerabilities. These were computed based on the constitutive materials of the historic buildings and their varied responses to climatic factors, such as mean sea-level rise, stresses from strong winds, precipitation, and solar exposure.\u003c/p\u003e\u003cp\u003eTo ensure integrability, the new attributes were coded according to the same structure as the existing dataset. This includes the use of alphanumeric data types with semantic consistency in the naming conventions. Once the update in tabular form was completed, it was associated with the main dataset through a shared unique key (FID). This linkage ensures that each record in the existing dataset corresponds uniquely to a set of values in the new attribute table.\u003c/p\u003e \u003cp\u003eIn a preliminary phase, some features of the municipal database were appropriately modified and reorganized to ensure greater consistency between the available data and the macro-categories of the built environment adopted in this study. This operation allowed the information structure to be aligned with the constitutive elements of the building, which form the basis for the detailed risk analysis. The configuration thus structured provides high flexibility in assigning different values and, above all, in updating them over time. In this way, data related to the conservation status of the building can be modified and updated based on new information or survey campaigns; such updates are automatically reflected in the vulnerability values, which are recalculated consistently and dynamically.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the attributes used in the risk assessment for the built heritage of the Dorsoduro district, Venice.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEDF_NM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBuilding Name\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Each feature was associated with the building name, allowing for precise spatial positioning of the built heritage.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEDF_TY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMacro-category of the Building\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Based on the macro-categories defined during the analysis phase, elaborated by considering building typology and original use (identified within the Dorsoduro district).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePalazzi\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChiese\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCampanili\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA04\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniversit\u0026agrave;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEDF_VAL_ELCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDistinguishing Features\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Presence of specific distinguishing elements of significant artistic value.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsence of Distinctive Features\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e0.5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePresence of Distinctive Features\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEDF_VAL_MAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMaintenance Condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Identification and assessment of the maintenance condition of the built heritage, where possible integrated with information from the philological surveys prepared by the Veneto Region.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbandoned\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1,7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Declared\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1,5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePast Interventions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecent Interventions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEDF_VAL_CONS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eState of Conservation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Identification and assessment of the conservation status of the built heritage, where possible integrated with information from the philological surveys prepared by the Veneto Region.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2,2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2,4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2,6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBad\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEDF_VAL_VNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eType of Heritage Protection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Identification within the database of the Superintendence for Fine Arts and Landscape for the Municipality of Venice and the Lagoon.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDLgs. 42/2004 parte III\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2,2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDLgs. 42/2004 parte II\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEDF_VAL_AFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eFacade Orientation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Identification of homogeneous macro-areas in relation to their location within the Venetian urban fabric.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban Fabric\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaterfront\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban Edge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEXP_SOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSolar Exposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Qualitative assessment of the vulnerability of buildings in relation to sun exposure, differentiated according to their constituent materials.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStone Buildings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlaster and/or Concrete or brick Building\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEXP_OMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eShading\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Qualitative assessment of the vulnerability of buildings in relation to shading, differentiated according to their constituent materials.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1,5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStone Buildings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1,5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrick Buildings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1,5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlaster and/or Concrete Building\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEXP_PGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Qualitative assessment of the vulnerability of buildings in relation to heavy rainfall, differentiated according to their constituent materials and exposure.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2,5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Building\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding Situated on the Edge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1,5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding Situated on \u003cem\u003ecampo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding Situated on \u003cem\u003ecalle\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEXP_VNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eWind\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Qualitative assessment of the vulnerability of buildings in relation to strong winds, differentiated according to their exposure.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2,5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh Building\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding Situated on the Edge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1,5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding Situated on \u003cem\u003ecampo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding Situated on \u003cem\u003ecalle\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEXP_SLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eVulnerability to Gradual Variation \u0026ndash; SLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Quantitative assessment of vulnerability in relation to gradual changes due to mean sea-level rise.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLR_2030 With MoSE activation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLR_2050 With MoSE activation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLR_2100 With MoSE activation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLR_2100\u0026thinsp;+\u0026thinsp;With MoSE activation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEDF_VAL_BN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMateriali di pregio come materiale libraio o opere permanenti\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescrizione\u003c/b\u003e: Presenza di materiale libraio o opere permanenti all\u0026rsquo;interno del fabbricato.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo presence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVAL_TOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBuilt Heritage Risk Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDescription\u003c/b\u003e: Risk value classified into five categories.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;24,1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVert High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e24\u0026thinsp;\u0026lt;\u0026thinsp;x\u0026thinsp;\u0026lt;\u0026thinsp;21,2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e21,1\u0026thinsp;\u0026lt;\u0026thinsp;x\u0026thinsp;\u0026lt;\u0026thinsp;18,2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e18,1\u0026thinsp;\u0026lt;\u0026thinsp;x\u0026thinsp;\u0026lt;\u0026thinsp;15,3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e15,2\u0026lt;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion and Conclusions","content":"\u003cp\u003eThe topic of specific risk analysis applied to built cultural heritage inherently opens up a multidisciplinary field of investigation, allowing for the integration of aspects related to conservation and mitigation \u0026mdash; even at the architectural scale \u0026mdash; with those of prevention (Fiorani, 2023; Mioli, 2023). In this context, the present work sought to develop a largely unexplored research area, namely the quantitative assessment of the vulnerabilities of built heritage, which to date has often been predominantly qualitative. This condition is due both to the scale at which risk assessments are often conducted and to the high heterogeneity of the cultural and natural heritage considered, whose complexity makes it difficult to interpret individual characteristics at the adopted reference scale (Bonazza et al., 2023).\u003c/p\u003e \u003cp\u003eThe distinction of different categories of the built environment and their respective morphologies in the Venetian context allowed the analysis to be increasingly detailed, placing the individual historic-architectural artifact at the center, with its specific characteristics and intrinsic peculiarities. This approach promoted the development of a potential risk monitoring system based on an information system that can be continuously implemented and updated. This aspect is particularly relevant for public authorities, which can regulate and calibrate mitigation strategies according to specific territorial areas, enabling a more conscious and targeted use of resources, especially in relation to the most vulnerable areas.\u003c/p\u003e \u003cp\u003eIn this regard, the scale factor of the analysis, initially highlighted, plays a determining role not only in understanding risk but also in implementing intervention policies, which can range from broad strategies to precise and targeted measures on individual critical elements.\u003c/p\u003e \u003cp\u003eThe work also opens the door to potential future developments and integrations. First, expanding the information database to cover the entire historic center would allow a more extensive testing of the actual applicability and effectiveness of the proposed system. At the same time, the definition of risk values and related multipliers \u0026mdash; not explored in detail here \u0026mdash; would require an additional phase of review and calibration.\u003c/p\u003e \u003cp\u003eThe use of GIS also allows for interpretations in an integrated perspective with the Internet of Things (IoT), introducing processes based on the continuous updating of specific parameters, with the goal of making the analysis dynamic over time and transforming it into an active monitoring tool for the historic center. In this direction, the integration of BIM models within the GIS environment would allow the construction of an even more detailed information system, useful for the knowledge, storage, and management of data related to historic artifacts, directly linking them to the most relevant climatic indices.\u003c/p\u003e \u003cp\u003eFinally, the use of remote sensing as a tool for analyzing specific vulnerabilities represents an additional area of development, particularly if implemented as a monitoring system based on open-source data. A particularly interesting area of experimentation could involve the transposition of raster data through the assignment of values in the form of tabular attributes, thus promoting greater integration between spatial and informational data.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis contribution is developed within the framework of a Collaboration Agreement (pursuant to Art. 15 of Law 241/1990, as amended) aimed at further investigating the impacts of climate change on the monumental heritage of Venice, funded by MASE (Ministry of the Environment and Energy Security). The project is led by the Universit\u0026agrave; Iuav di Venezia, Department of Cultures of the Project, with Prof. Francesco Trov\u0026ograve; as project coordinator, Prof. Greta Bruschi as co-coordinator, Arch. Enrico Gobbi as research fellow, and the Geomatics Laboratory under the responsibility of Prof. Caterina Balletti.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualisation, methodology, C.B., G.B., E.G., F.T.; software, E.G.; writing\u0026mdash;review, C.B., G.B., E.G., F.T.; project administration, funding acquisition, F.T. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis research was carried out within a Collaboration Agreement (Art. 15, Law 241/1990) funded by the Ministry of the Environment and Energy Security (MASE), aimed at investigating the impacts of climate change on the monumental heritage of Venice, and led by the Universit\u0026agrave; Iuav di Venezia. This article presents part of the results developed during the research activity; the authors wish to thank the Municipality of Venice for its support and acknowledge the valuable collaboration of Alessandra Bonazza.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the data supporting the findings of this study are available in the document.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBonazza A (2022) Sustainable Heritage and Climate Change. In: Fouseki K, Cassar M, Dreyfuss G et al (eds) Routledge Handbook of Sustainable Heritage, 1st edn. Routledge, London, pp 263\u0026ndash;271\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonazza A, Maxwell I, Drdacky M et al (2018) Safeguarding Cultural Heritage from Natural and Man-Made Disasters. A Comparative Analysis of Risk Management in the EU. 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Il Poligrafo, Padova\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrov\u0026ograve; F, Bruschi G (2025a) The Venice and its Lagoon UNESCO site (Italy): Cultural Heritage and Climate Change. In: Mileto C et al (eds) Historic Settlements. Conservation, Regeneration, Management. Atti del convegno Heritage 2025. EdUPV, Valencia, pp 875\u0026ndash;882\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrov\u0026ograve; F, Bruschi G (2025b) Patrimonio culturale e cambiamenti climatici. il caso Venezia. In: Driussi G (ed) Le prossime sfide per i beni culturali. Atti del XL Convegno Scienza e Beni Culturali. Arcadia Ricerche, Venezia, pp 205\u0026ndash;216\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrov\u0026ograve; F, Bruschi G, Umar N et al (2024) Historical cities against climate change. management conservation plans as a possible strategy? In: Driussi G (ed) La conservazione preventiva e programmata. Atti del XXXIX\u0026deg; Convegno Scienza e Beni Culturali. Arcadia Ricerche, Venezia, pp 299\u0026ndash;310\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNESCO (1972) Convention Concerning the Protection of the World Cultural and Natural Heritage. Adopted by the General Conference at its seventeenth session, Paris, 16 November 1972\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Risk management, geographic information system, architectural Heritage, Venetian built Heritage","lastPublishedDoi":"10.21203/rs.3.rs-8997539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8997539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis contribution aims to analyze the specific vulnerabilities of the historic center of Venice and to develop an evaluative method capable of taking into account both the intrinsic and extrinsic characteristics of the built environment through the use of georeferenced information systems. In particular, the study focuses on the analysis of vulnerabilities related to the gradual phenomenon of mean sea level rise and on an approach for the identification of site-specific surface temperatures based on remote sensing data. Finally, the work addresses the integration of the different types of datasets into a single spatial management platform (GIS), referenced to individual historic buildings.\u003c/p\u003e","manuscriptTitle":"Management of risk assessment for Cultural Heritage on GIS based solution for the Historic center of Venice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 15:12:18","doi":"10.21203/rs.3.rs-8997539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-11T06:23:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T20:22:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107085521161254583220648357437610141547","date":"2026-03-20T07:50:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-15T19:20:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-10T13:01:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T08:00:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Geomatics","date":"2026-02-28T18:56:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2bf5e601-81ea-46b5-b1da-cd4cb1a7b4ca","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T19:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 15:12:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8997539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8997539","identity":"rs-8997539","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}