BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2025//speaker//NF G8VH BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2025-RXU77L@talks.staging.osgeo.org DTSTART;TZID=CET:20250716T140000 DTEND;TZID=CET:20250716T143000 DESCRIPTION:Geospatial data is essential for urban planning\, disaster risk assessment\, and infrastructure management. However\, the independent nat ure of various taxonomies - such as the Global Earthquake Model (GEM)\, OA SIS disaster risk framework\, Building Stock Observatory (BSO)\, 3DCityDB for urban modeling\, and Industry Foundation Classes (IFC) for BIM applica tions - creates barriers to interoperability. While these taxonomies provi de structured\, detailed datasets within their respective domains\, their lack of standardization across platforms limits their usability in broader geospatial applications. OpenStreetMap (OSM)\, as a globally recognized o pen mapping platform\, presents an opportunity to act as a baseline for in tegrating these diverse datasets. By aligning different taxonomies within an OSM-compatible framework\, it becomes possible to enhance the comparabi lity of structured geospatial datasets\, ensuring that OSM data can be enr iched and cross-referenced with external sources. \n\nThis work proposes an ontology-based approach to structuring and integrating multi-taxonomy b uilding information within the OSM ecosystem. By defining mappings between attributes used in different taxonomies and translating them into standar dized OSM tagging schemes\, this approach allows for the seamless conversi on of structured geospatial datasets into OSM-compatible formats. To integ rate structured geospatial data into OSM\, this work employs a multi-step methodology focusing on schema mapping\, conversion pipelines\, and taggin g standardization. The first step in the process involves establishing cor respondences between attributes across different taxonomies. Attributes su ch as building material\, height\, structural system\, and occupancy class ifications are identified in OSM and mapped to their equivalent definition s in GEM\, OASIS\, BSO\, IFC\, and 3DCityDB. For instance\, a building tag ged as `building:material=brick` in OSM corresponds to `wall_type=brick` i n GEM\, `Construction Material: Brick` in BSO\, and `IfcMaterialDefinition =Brick` in IFC. This mapping ensures that structured datasets can be consi stently translated into OSM-compatible key-value pairs\, preserving the me aning of the original attributes.\n\nOnce these relationships are establis hed\, a structured conversion process is implemented to transform data int o OSM’s key-value format. A transformation pipeline extracts structured geospatial attributes from external taxonomies\, applies pre-defined mappi ng rules\, and outputs the resulting dataset in an OSM-compatible tagging format. This step also involves data validation\, where inconsistencies in classification are detected and adjusted to maintain uniformity. While th ere are cases of one-to-one transformation\, there are also cases when tha t is not possible. For example\, take the tag `MCF` which stands for mater ial masonry\, reinforcement confined. In these cases the nested informatio n is transformed into two tags: `material=masonry\;material:reinforcement= confined`. In case of later merging datasources\, later there can be check ed conflicting information\, and priority in case of conflict is allowed. By employing a structured pipeline\, batch processing of large datasets is possible\, ensuring that structured geospatial information from various s ources can be efficiently imported into OSM without requiring manual recla ssification.\n\nThe integration of structured taxonomies into OSM is reinf orced through the development of tagging presets for OSM editors. By gener ating JOSM XML and iD Editor JSON presets\, contributors can apply predefi ned tags corresponding to structured taxonomies\, reducing inconsistencies and improving data quality. These presets guide users in applying standar dized geospatial attributes\, ensuring that contributions align with struc tured datasets used in risk assessment\, urban planning\, and infrastructu re monitoring. \n\nThe development of a multi-taxonomy integration model can have several advantages in structured geospatial data translation. By ensuring consistent mapping between GEM\, OASIS\, BSO\, IFC\, and 3DCityD B\, the system enables conversion of structured data into OSM-compatible t agging formats. This enhances the accuracy of OSM mapping by aligning its tags with established geospatial standards. Furthermore\, humanitarian org anizations can directly leverage structured tagging presets to improve the consistency and reliability of OSM contributions. The use of structured p resets reduces tagging inconsistencies and allows for direct comparisons b etween OSM-mapped infrastructure and standardized exposure models. The int egration of structured datasets into OSM improves the platform’s usabili ty for disaster resilience planning\, enabling geospatial analysts to inco rporate OSM data into machine-learning-based risk assessment models and st ructured exposure frameworks like GEM and OASIS. \n\nThe broader implicat ions of this work extend to multiple fields. In disaster risk and exposure modeling\, OSM can serve as a structured baseline for risk analysis in ea rthquake\, flood\, and climate resilience applications. In urban infrastru cture monitoring\, standardized tagging allows for better integration of O SM data with smart city models and BIM applications. Humanitarian mapping efforts can also benefit from this integration\, as structured tagging ens ures that OSM contributions align with professional risk assessment framew orks. The development of conversion pipelines and tagging presets ensures that structured datasets can be integrated into OSM without requiring manu al intervention\, significantly improving data comparability across platfo rms. \n\nFuture research will focus on expanding ontology coverage to add itional geospatial datasets\, including cadastral data and ISO standards. Further work will also explore the development of analytical tools to comp are structured datasets with OSM data\, enabling geospatial professionals to assess the completeness and accuracy of OSM-mapped building data. This initiative aligns with efforts to improve the interoperability of open geo spatial data\, making OSM an essential tool for humanitarian response\, ur ban resilience\, and infrastructure planning. DTSTAMP:20260527T015037Z LOCATION:SA02 SUMMARY:Unifying open data of buildings through building a translator of ta xonomies based on OpenStreetMap tagging format. - Doren Calliku URL:https://talks.staging.osgeo.org/foss4g-europe-2025/talk/RXU77L/ END:VEVENT END:VCALENDAR