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Abstract. We present a building damage dataset following the 2024 Noto Peninsula Earthquake. The database was compiled from freely available, multi-source, remote sensing data, verified through opt-in crowd-sourced information. The dataset consists of geo-referenced vector polygons representing the pre-event building footprints of 140,208 structures. Each building was classified through visual inspection using pre-disaster and post disaster vertical, oblique, survey, and verifiable news reporting imagery. Entries were validated using voluntary-submission data sourced through a web-API hosting a live version of the database. We calculate classification metrics for a subset of the database where ground survey photographs were provided by independent surveyors. An average F1-score of 0.94 suggests that the proposed assessment is consistent and high quality. We aim to inform future disaster research such as disaster dynamics models; statistical and machine learning damage models; logistics and evacuation studies. The present work describes the data collection process, damage assessment methodology, and rationale; including limitations encountered, the crowd sourcing validation process, and the dataset structure.
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