During and shortly after a disaster, data about the hazard and itsconsequences are scarce and not readily available. Information provided byeyewitnesses via social media is a valuable information source, which shouldbe explored in a~more effective way. This research proposes a methodologythat leverages social media content to support rapid inundation mapping,including inundation extent and water depth in the case of floods. Thenovelty of this approach is the utilization of quantitative data that arederived from photos from eyewitnesses extracted from social media posts andtheir integration with established data. Due to the rapid availability ofthese posts compared to traditional data sources such as remote sensing data,areas affected by a flood, for example, can be determined quickly. Thechallenge is to filter the large number of posts to a manageable amount ofpotentially useful inundation-related information, as well as to interpretand integrate the posts into mapping procedures in a timely manner. Tosupport rapid inundation mapping we propose a methodology and develop"PostDistiller", a tool to filter geolocated posts from social mediaservices which include links to photos. This spatial distributedcontextualized in situ information is further explored manually. In anapplication case study during the June 2013 flood in central Europe weevaluate the utilization of this approach to infer spatial flood patterns andinundation depths in the city of Dresden.
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