https://video.osgeo.org/videos/watch/4e77ed7f-d387-4785-a3fd-a8c8dc2fdbf3 Flood is one of the most devastating natural hazards, imposing enormous social, economic, and infrastructural impacts on nations worldwide. Major flood events disrupt communities by damaging critical infrastructure, displacing large populations, and causing extensive financial losses. These disasters not only strain emergency response systems but also hinder long-term development, emphasizing the urgent need for reliable, timely, and detailed spatial data to guide both immediate action and future mitigation planning. Flood disasters often expose critical gaps in the availability of timely and accurate geospatial data. This study investigates the role of open source data in enhancing disaster preparedness and response, with a particular focus on community-mapped data, using Jakande housing estate, 1st gate Platinum Wy Lekki Penninsula II Lekki 106104, presently located in the Eti-Osa Local government of Lagos State as a case study. By harnessing the power of volunteered geographic information contributed by local residents, the research addresses critical data gaps in under-mapped regions (Goodchild, 2007; Haworth & Bruce, 2015). Community mapping initiatives not only provide timely, real‐time updates during flood events but also incorporate localized insights that traditional mapping methods may overlook. This participatory approach enriches the spatial dataset, offering details on flood extents, infrastructure damage, and population displacement. In integrating these community-driven datasets with advanced geospatial tools, the study demonstrates a significant improvement in situational awareness, ultimately supporting more informed and effective decision-making during emergency response efforts. The methodology comprises a multi-tiered approach. Initially, high-resolution satellite imagery of the case study area was acquired over a six-year period, enabling a temporal analysis of land cover changes and pre- and post-flood conditions. This remote sensing phase provided an extensive visual record that served as a baseline for further spatial analysis. A review of existing OpenStreetMap (OSM) data revealed that the targeted area was largely unmapped—a gap that hindered the region’s disaster response capacity (Herfort et al., 2021). In response, a dedicated mapping task was initiated using the Humanitarian OpenStreetMap Team (HOT) Tasking Manager to invite contributions from volunteer mappers, thereby creating an up-to-date geographic dataset. Furthermore, the presented methodology offers insights into how to verify OSM data and contribute to the improvement of its accuracy and thoroughness. To augment the remote mapping effort, on-ground data collection was undertaken using Open Data Kit (ODK). Field surveys focused on gathering real-time information on the condition of local infrastructure and documenting patterns of population displacement due to flooding. The data collection process incorporated stringent quality checks by cross-referencing field findings with local community insights, ensuring both accuracy and contextual relevance. This integrated approach highlights the synergy between remote sensing, open-sourced mapping, and community-based data acquisition—a combination increasingly recognized as critical for effective disaster management. Subsequent spatial analysis was performed using QGIS, First, a multi-temporal change detection algorithm was applied by comparing classified satellite imagery from pre- and post-flood periods. This method, which utilized indices such as the Normalized Difference Vegetation Index (NDVI), allowed us to assess changes in land cover dynamics over time. Overlay analysis was then performed by intersecting the delineated flood extent polygons with mapped infrastructure layers—including residential, commercial, and roads—to pinpoint areas where vulnerable structures were concentrated. Additionally, spatial queries, including buffer and proximity analysis, were executed to delineate high-risk zones where flood extents and population clusters overlapped which allowed for a comprehensive assessment of flood-induced damage, identification of vulnerable infrastructure, and quantification of displacement metrics and to provide a clearer picture of the flood’s spatial extent. The spatial analysis outputs were visualized as detailed maps that conveyed spatial patterns and risk zones to emergency responders and policymakers. The spatial analysis revealed that the flood inundated a vast portion of the study area, with a significant concentration of affected housing and critical infrastructure. Detailed overlay analysis showed that more than 40% of the mapped residential zones were located in high-risk flood areas. In addition, clusters of commercial and public service facilities—such as police ... Gresa Neziri Adeola Anthonia OYETUNDE https://talks.osgeo.org/foss4g-europe-2025/talk/VWG7HU/ Room: PA01 (Quarticle) @ 18.07.2025 12:15:00 #foss4ge2025 #AcademicTrack Fri, 31 Jul 2026 09:29:14 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.osgeo.org https://video.osgeo.org/lazy-static/avatars/de841c88-3a57-45ac-9eff-a00237356bfc.png https://video.osgeo.org/videos/watch/4e77ed7f-d387-4785-a3fd-a8c8dc2fdbf3 All rights reserved, unless otherwise specified in the terms specified at https://video.osgeo.org/about and potential licenses granted by each content's rightholder.