Reconstructing missing data of damaged buildings from post-hurricane reconnaissance data using XGBoost

Assessing building damage in coastal communities after a hurricane event is crucial for reducing both immediate and long-term disaster impacts, as well as for enhancing resilience planning and disaster preparedness. Despite the extensive data collection efforts of the post-hurricane reconnaissance teams, some information on the structural features of damaged buildings is often missing due to various reasons, like the absence of relevant documents or severe building damage, thereby limiting our comprehensive understanding of building resilience to natural disasters. This study introduces a machine learning approach based on extreme gradient boosting (XGBoost) to reconstruct missing structural features of the damaged buildings from four types of data (known structural, geospatial, hazard, and damage level information). XGBoost models were trained based on the reconnaissance datasets collected from four regions affected by Hurricanes. For each region, we analyzed the model’s performance depending on the missing structural features. We also demonstrated the importance of including geospatial, hazard, and damage level data by showing improved performance of XGBoost models compared to those trained only on known structural data. Furthermore, we examined how the accuracy of the XGBoost approach changes if multiple structural features are missing. This XGBoost approach has the potential to support post-hurricane building damage assessments by providing missing building details, enabling comprehensive post-disaster analysis.