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During flash flooding, quick and effective rescue operations are crucial to minimizing harm to vulnerable communities. While much research focused on emergency response and evacuation, few studies address how overhead powerline obstructions impact rescue operations. Additionally, existing research on vulnerable communities often emphasizes long-term flood mitigation and recovery but less so on immediate responses. To ensure rapid and equitable flood rescue operations, this study derives an integrated metric to quantify rescue demands that incorporate rescue efficiency, community flood severity, and social vulnerability. In detail, rescue efficiency is calculated by analyzing a network that captures the geospatial interdependencies between the residential buildings' road networks and overhead power lines; community flood severity is quantified as the percentage of building damage resulting from flood impacts; and social vulnerability is an integrated indication of key household composition factors (e.g., elders, single parents, and minorities). Based on this metric, a systematic step is designed to suggest the sequence of rescue operations and the strategies for distributing rescue lifeboats at emergency facilities. The applicability and feasibility of the proposed approach were demonstrated using lifeboat rescue operations in Manville, New Jersey, during Hurricane Ida. This study calculates dynamic changes in rescue loads of all emergency facilities and then finds the optimal strategies for distributing lifeboats. The results highlight the significant impact of overhead power line obstructions on the optimal rescue lifeboat distribution. Additionally, the results suggest prioritizing emergency evacuation for socially vulnerable households in Manville township. Practically, the generated rescue sequence and rescue lifeboat distribution are expected to help emergency response agencies perform effective and rapid rescue operations. 

Image data collected after natural disasters play an important role in the forensics of structure failures. However, curating and managing large amounts of post-disaster imagery data is challenging. In most cases, data users still have to spend much effort to find and sort images from the massive amounts of images archived for past decades in order to study specific types of disasters. This paper proposes a new machine learning based approach for automating the labeling and classification of large volumes of post-natural disaster image data to address this issue. More specifically, the proposed method couples pre-trained computer vision models and a natural language processing model with an ontology tailed to natural disasters to facilitate the search and query of specific types of image data. The resulting process returns each image with five primary labels and similarity scores, representing its content based on the developed word-embedding model. Validation and accuracy assessment of the proposed methodology was conducted with ground-level residential building panoramic images from Hurricane Harvey. The computed primary labels showed a minimum average difference of 13.32% when compared to manually assigned labels. This versatile and adaptable solution offers a practical and valuable solution for automating image labeling and classification tasks, with the potential to be applied to various image classifications and used in different fields and industries. The flexibility of the method means that it can be updated and improved to meet the evolving needs of various domains, making it a valuable asset for future research and development.