Pre-hurricane building imagery for StEER dataset

This dataset provides a valuable resource for research on hurricane-induced building damage prediction by offering pre-hurricane imagery for buildings impacted by Hurricanes Harvey, Florence, Michael, Sally, and Laura, as documented in the NSF-funded StEER dataset. Comprising short-range and long-range satellite images, along with ground view images, the dataset is organized using fulcrum_id to facilitate integration with corresponding tabular data. It serves as a foundation for the study Predicting Hurricane-Induced Building Damage Using Multimodal Data: Insights from the StEER Dataset (in revision) and is particularly geared toward advancing the integration of image modalities in machine learning models. This dataset is highly adaptable, supporting applications such as feature extraction, model validation, and comparative studies, and offers significant potential for broader exploration of multimodal learning approaches in disaster impact research.