BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//speaker//RVFJSB BEGIN:VTIMEZONE TZID:JST BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:JST TZOFFSETFROM:+0900 TZOFFSETTO:+0900 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2026-BEY8W9@talks.osgeo.org DTSTART;TZID=JST:20260901T143000 DTEND;TZID=JST:20260901T150000 DESCRIPTION:Conventional hazard maps\, public GIS layers\, and disaster edu cation materials remain essential resources for community disaster risk re duction. However\, they do not always support in-situ spatial understandin g during field-based learning and training. In many disaster walking tours \, participants can read hazard maps and view web GIS layers\, yet still s truggle to connect those representations with the terrain\, streetscape\, and built environment around them. This gap is especially evident for chil dren\, first-time visitors\, and residents who are not accustomed to trans lating two-dimensional hazard information into situated judgment. To addre ss this problem\, we developed MUSUBOU-AR\, an open-source geospatial augm ented reality framework for disaster walking tours\, community disaster ri sk communication\, and place-based disaster learning. Rather than treating AR only as an immersive visualization layer\, the framework is designed a s a reusable geospatial system that connects public GIS resources\, local scenario authoring\, on-site AR interpretation\, and post-activity review. \nMUSUBOU-AR is a mobile application that switches between a conventional map mode and an AR mode. In AR mode\, virtual hazards are displayed relati ve to the user’s current position so that disaster scenarios can be expe rienced where risks may emerge. The system supports multiple disaster type s\, including flood inundation\, fire\, landslide\, building collapse\, bl ock-wall collapse\, and liquefaction. Hazards can also be configured as ti me-varying events\, enabling organizers to express dynamic scenarios such as expanding floodwater or spreading urban fire during field-based trainin g. This design supports a transition from passive map reading to situated risk interpretation.\nExisting studies in disaster education and geospatia l learning have explored practical drills\, ICT-based learning materials\, game-based activities\, and AR-supported virtual disaster experiences. In contrast\, our contribution is not centered on AR experience alone. We po sition MUSUBOU-AR as an open geospatial framework that integrates publicly available GIS layers\, reusable route and scenario authoring\, field logg ing\, and optional LiDAR-enhanced visualization within a single deployable workflow.\nA core contribution of the framework is its interoperability w ith open geospatial data. In map mode\, MUSUBOU-AR supports the standard X YZ tile scheme used in Japanese public map distribution\, making it possib le to overlay publicly available GIS layers\, including Geospatial Informa tion Authority of Japan tiles and hazard-related layers distributed throug h the national hazard map portal. Project-specific or locally prepared dat asets can also be incorporated through standard GIS workflows by generatin g custom XYZ tiles with QGIS and plugins such as QTiles and deploying them on a web server for use in the application. The framework also integrates Apple Watch-based trajectory and activity logging\, synchronizes position al and biometric records into a unified GPX-based dataset\, and supports o pen-source web review through GPXreaderWeb. In addition\, LiDAR-enhanced r endering on supported iPhone Pro and iPad Pro devices improves spatial rec ognition and allows floodwater to be rendered at a specified height above the recognized ground surface while preserving backward compatibility for non-LiDAR devices.\nAnother key contribution is the authoring workflow. Al though public GIS and open data make disaster-related information widely a vailable\, preparing field-ready AR content remains a practical bottleneck . To reduce that barrier\, we developed a web-based authoring environment for MUSUBOU-AR datasets by customizing the open-source Re:Earth platform. This tool allows users to create\, edit\, and export the core data require d by the application\, including points\, routes\, and GIS layers. The con tribution is therefore not limited to the application itself\; it also inc ludes a reusable workflow for transforming public geospatial resources and local field knowledge into deployable AR scenarios.\nWe position MUSUBOU- AR not merely as an educational app but as an open geospatial workflow for field deployment. The framework has already been deployed in multiple com munity- and school-based activities in Japan\, including disaster walking tours\, local awareness events\, and place-based disaster learning program s. In this paper\, we draw on these deployments and present a joint disast er walking tour conducted in the Hiro area of Kure City\, Hiroshima Prefec ture\, as a representative validation case. In that case\, route design an d hazard placement were based on publicly available flood and landslide ha zard maps\, local field inspection\, and additional local disaster-related map information.\nThis case is used as field validation\, not as the prim ary contribution of the paper. Before the activity\, university students s tudied topography\, hazard maps\, and three-dimensional terrain representa tions of the area. During the field session\, they walked with elementary school pupils and used MUSUBOU-AR to explain local hazards in situ. Pre- a nd post-activity questionnaires administered to the university students we re analyzed using multilevel models. The results showed a significant over all shift toward higher response levels after the activity\, with an odds ratio of 21.7 for moving to a higher response category in the cumulative l ogit mixed model. Particularly relevant from a geospatial perspective\, si gnificant gains were observed in noticing environmental danger signs\, und erstanding hazard maps and disaster-related signs\, identifying dangerous and relatively safe places on maps\, and planning evacuation routes while using maps. Open-ended responses further indicated that participants frequ ently identified roadside ditches or waterways and narrow roads as hazardo us places\, mainly in relation to flood or inundation risk.\nThis study ha s limitations. The field validation involved a relatively small sample and did not include a control group\, so causal claims about educational effe ctiveness should be made cautiously. Nevertheless\, the main contribution of this work lies in presenting an openly reusable geospatial AR framework that combines interoperability with public GIS and open data\, a web-base d authoring workflow for routes\, points\, and tiled layers\, mobile AR ha zard visualization with optional LiDAR enhancement\, and GPX-based field l ogging with open-source web review. The source code of MUSUBOU-AR is publi cly available in a GitHub repository\, supporting reuse and reproducibilit y. We therefore position MUSUBOU-AR as a practical contribution to the FOS S4G community\, demonstrating how open geospatial data\, open-source softw are\, and field-deployable AR can be combined for disaster risk communicat ion. DTSTAMP:20260717T225730Z LOCATION:Cosmos2 SUMMARY:MUSUBOU-AR: An Open-Source Geospatial AR Framework for Integrating Public GIS Data\, Field Authoring\, and Disaster Walking Tours - Daisuke Y oshida URL:https://talks.osgeo.org/foss4g-2026/talk/BEY8W9/ END:VEVENT END:VCALENDAR