Evidence-Aware Data Model for Explainable Post-Hurricane Building Damage Assessment

Abstract

Recent learning-based damage assessment methods using remote sensing data have greatly contributed to the rapid identification and classification of damaged buildings from pre- and post-disaster imagery. However, most outputs are provided in the form of final damage labels or scores. To address this limitation, this study proposes an evidence-aware data model for explainable post-disaster building damage assessment. The proposed model is derived from a component-level roof-damage assessment workflow using optical imagery and post-disaster surface-height data, and is designed to describe the observational evidence, intermediate indices, and decision rules leading to a specific damage grade, as well as its interpretation as a post-disaster change event. Based on the proposed logical data model, a hybrid spatial–graph database was implemented to support both spatial retrieval and the semantic explanation of damage assessment results. The proposed model was evaluated through six competency questions derived from different damage assessment requirements. The results confirmed that the proposed model can support component-level retrieval of damage results, tracing of damage evidence and assessment rules, spatial visualization of damaged building components, and integrated spatial–semantic explanation for individual objects. The results provide a foundation for managing disaster damage assessment processes and results as explainable and reusable structured data.