Street-Level Disaster Location Detection Using Image Matching of Social Media Images

Abstract. Rapid identification of disaster locations is essential for effective emergency response and situational awareness. However, a large proportion of images shared on social media during disasters lack geographic metadata, limiting their usefulness for operational decision-making. Existing approaches mainly rely on geotags or textual geoparsing, which often fail when metadata is missing or ambiguous. As a result, valuable visual information from social media images remains underutilized for disaster mapping. This study proposes a deep learning–based framework to estimate the geographic location of disaster images using visual scene matching. The approach compares query images from social media with georeferenced Google Street View imagery to infer their potential locations. The framework integrates image pre-processing, deep feature extraction, and similarity-based matching to identify the most likely geographic correspondence. Preliminary experiments demonstrate promising results in detecting key scene elements within disaster imagery, providing a foundation for reliable visual matching. By leveraging visual cues rather than textual metadata, the proposed framework aims to improve the usability of non-geotagged social media images for disaster response. The proposed approach has the potential to support rapid disaster mapping by transforming citizen-generated imagery into spatially actionable information for emergency management.