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The development of artificial intelligence (AI) provides an opportunity for rapid and accurate assessment of earthquake-induced infrastructure damage using social media images. Nevertheless, data collection and labeling remain challenging due to limited expertise among annotators. This study introduces a novel four-class Earthquake Infrastructure Damage (EID) assessment data set compiled from a combination of images from several other social media image databases but with added emphasis on data quality. Unlike the previous data sets such as Damage Assessment Dataset (DAD) and Crisis Benchmark, the EID includes comprehensive labeling guidelines and a multiclass classification system aligned with established damage scales, such as HAZUS and EMS-98, to enhance the accuracy and utility of social media imagery for disaster response. By integrating detailed descriptions and clear labeling criteria, the labeling approach of EID reduces the subjective nature of image labeling and the inconsistencies found in existing data sets. The findings demonstrate a significant improvement in annotator agreement, reducing disagreement from 39.7% to 10.4%, thereby validating the efficacy of the refined labeling strategy. The EID, containing 13,513 high-quality images from five significant earthquakes, is designed to support community-level assessments and advanced computational research, paving the way for enhanced disaster response strategies through improved data utilization and analysis. The data set is available at DesignSafe:https://doi.org/10.17603/ds2-yj8p-hs62. 

This research explores the utilization of the Black Marble nighttime light (NTL) product to detect and assess damage caused by hurricanes, tornadoes, and earthquakes. The study first examines average regional NTL trends before and after each disaster, demonstrating that NTL patterns for hurricanes closely align with the features of a resilience curve, unlike those for earthquakes and tornadoes. The relative NTL change ratio is computed using monthly and daily NTL data, effectively reducing variance due to daily fluctuations. Results indicate the robustness of the NTL change ratio in detecting hurricane damage, whereas its performance in earthquake and tornado assessment was inconsistent and inadequate. Furthermore, NTL demonstrates a high performance in identifying hurricane damage in well-lit areas and the potential to detect damage along tornado paths. However, a low correlation between the NTL change ratio and the degree of damage highlights the method’s limitation in quantifying damage. Overall, the study offers a promising, prompt approach for detecting damaged/undamaged areas, with specific relevance to hurricane reconnaissance, and points to avenues for further refinement and investigation.