The 2024 Noto Peninsula earthquake building damage dataset: multi-source visual assessment

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 georeferenced 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 research such as disaster-specific physical dynamics models, statistical and machine learning damage models, and 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 (https://doi.org/10.5281/zenodo.11055711, Vescovo et al., 2025).