Cultural heritage building information models (HBIMs) incorporate specific geometric and semantic data that are mandatory for supporting the workflows and decision making during a heritage study. The Industry Foundation Classes (IFC) open data exchange standard can be used to migrate these data between different software solutions as an openBIM approach, and has the potential to mitigate data loss. Specific data-exchange scenarios can be supported by firstly developing an Information Delivery Manual (IDM) and subsequently filtering portions of the IFC schema and producing a specialized Model View Definition (MVD). This paper showcases the creation of a specialized IDM for the heritage domain in consultation with experts in the restoration and preservation of built heritage. The IDM was then translated into a pilot MVD for heritage. We tested our developments on an HBIM case study, where a historic building was semantically enriched with information about the case study’s conservation plan and then checked against the specified IDM requirements using the developed MVD. We concluded that the creation of an IDM and then the MVD for the heritage domain are achievable and will bring us one step closer to BIM standardisation in the field of digitised cultural buildings.

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<p>OpenStreetMap (OSM) is the largest crowd-sourced mapping effort to date, with an infrastructure network that is considered near-complete. The mapping activities started as any crowd-sourced information platform: the community expanded OSM anywhere there was a collective interest. Initial efforts were found around universities, hometowns of mappers and areas designated by organizations like the Humanitarian OSM Team (HOT). This resulted in a map that is of non-uniform completeness, with some areas having all building footprints in, while other areas remain incomplete or even untouched. Currently, with 530 million footprints, OSM identifies between a quarter and half of the total building footprints in the world, if we estimate that there are around 1-2 billion buildings in the world.</p><p>A global view on the completeness of buildings existing in OSM did not yet exist. Unlike other efforts, that only look at a subset of OSM building data (Biljecki & Ang 2020; Orden et al., 2020; Zhou et al., 2020), we have used the Global Human Settlement Layer (GHSL) to estimate completeness of the entire dataset. The remote sensing dataset is distributed onto a grid and in each tile of the grid, the built area of GHSL is compared to the total area of OSM building footprints. The computed ratio is measured against a completeness threshold that is calibrated using areas that were manually assessed.</p><p>Using information derived from remote sensing datasets can be problematic: GHSL does not only measure building footprints: it includes any human-built structures, including infrastructure and industrial areas. Next to that, due to circumstances like imperfect input data or failing algorithms, the dataset is not of the same quality as the crowd-sourced data in OSM in areas that are complete. False positives (i.e. rocky coasts) and false negatives (i.e. buildings missing in mountainous areas) exist in automatically generated data.</p><p>Even with these limitations, a comprehensive global completeness assessment is created. The assessment should not be used as ground truth, but rather as reflection on the OSM building dataset as is and as a guideline for priorities for the future. Statistics on regional completeness can be created and the quality of GHSL could be assessed on countries that are considered to be complete, such as France or the Netherlands.</p>

<p>Building exposure, that is, characterization of buildings, number of people occupying it and its replacement cost, for a given region or area, is a primary component when determining building risk due to a hazard or group of hazards. Building characterization includes parameters like construction material, number of stories, lateral load resisting components, etc. There exists a large spatial variation in building exposure, as seen with the development of past global and regional exposure databases (Jaiswal et al., 2008, Yepes-Estrada et al., 2017). Such efforts have also resulted in uniformity and collection of building-type definitions globally.</p> <p>We have developed an exposure model for Japan at the municipality level. This model is entirely derived from open building data (e.g., census) and resulting in an open inventory of uniformly defined building parameters available for risk assessments. The Japanese land and housing database (https://www.e-stat.go.jp/en) provides information about building types and their number, dwelling type, their number and size, tenure type, construction material type, year of construction, number of stories, persons per dwelling and size of dwelling (area in square meters). After interpreting this information, different combinations of building type, construction material, year of construction, and number of stories were mapped to building classes based on the taxonomy of the Global Earthquake Model (GEM). Furthermore, building parameters required for loss assessments (e.g., replacement cost per unit area of a dwelling, number of people per dwelling) were assigned to the identified building class at the municipality level and stored in a database open for public access.</p> <p>The quality of the model is greatly affected by the availability of building parameters and their survey quality in the census data. The commercial and industrial data from the census lacks information on number of stories and have to be derived from other sources for our model. Additionally, the district information defining the model has layers of complexity due to Japan being divided in multiple administrative units at the same administrative level. Such constraints and complexities, limit the flexibility and versatility of the model to be up-scaled or down-scaled from higher to lower resolution and vice versa, also to due to the large scale uncertainty of the survey methods of the data.</p> <p>Despite the limitations, the model gives a clear snapshot of the distribution of regional building classes and their possible count. It also, thus represents an open catalogue of rules for mapping Japanese building attributes to a GEM taxonomy-based building class and, in doing so, creates an opportunity for the scientific and non-scientific community to collaborate on the assessment. We welcome, corrections, and expansion of individual aspects of the aggregated exposure model and like tohopefully encourages the development of such open aggregated exposure models for other countries.</p> <p> </p> <p>References:</p> <p>Jaiswal K, Wald D, Porter K (2010) A global building inventory for earthquake loss estimation and risk</p> <p>management. Earthq Spectra 26:731–748. </p> <p>Yepes-Estrada C, Silva V, Valcárcel J, Acevedo AB, Tarque N, Hube MA, Coronel G, María HS 2017 Modeling the Residential building inventory in South America for seismic risk assessment. Earthquake Spectra 33:299–322. https ://doi.org/10.1193/10191 5EQS1 55DP</p>

<p>Effects of a hazard can be expressed in terms of the losses and damages caused by it. These risk assessments help decision makers and disaster managers to better prepare for and cope with the disasters arising from hazards in all three phases (preparation, response and recovery) of disaster risk reduction and resilience. With the growth of disaster risk globally (GAR, 2022), and also the related research on different components of risk (hazard, exposure, vulnerability), there is a rising demand to compute risk for multi-hazard events, for which an integrated tool is very beneficial, but most of the tools used for multi-hazard risk assessment purposes are not available for public use (Cees J. van Westen and Stefan Greiving, 2017).</p><p>The ‘risk-calculator’ is an open-source Python program that enables users to do multi-hazard scenario risk assessments. This program is  capable of assessing loss and damage for different sets of discrete or continuous vulnerability or fragility functions for different concurrent hazards, such as floods, earthquakes and tsunamis.</p><p>The three main inputs are: (1) hazard, expressed as an intensity field (e.g ShakeMap for earthquakes or inundation field for tsunamis and floods), (2) the exposure model provided in a geospatial database or as CSV files to be imported, and (3) vulnerability/fragility functions describing the level of loss or damage at different intensity measure levels dependent on the asset taxonomy and the hazard type. The tool works on basis of the standard taxonomy as defined by the Global Earthquake Model. This taxonomy can also be expanded to include damage states of previous events for a loss computation of cascading events such an earthquake followed by a tsunami.</p><p>Apart from using the program directly, users can connect to the API designed for this program to run loss assessments. The resulting losses and damages are provided in a geospatial database (SpatiaLite or GeoPackage), for sake of easier handling and plotting. This user-friendly program provides database views, connecting data in a meaningful way from different tables to allow for various ways of analyzing and visualizing the loss and damage assessments.</p>