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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. 

Personal exposures to environmental stressors including extreme heat and air pollution vary widely depending on schedules and activities. This paper shares results of a city-scale project to build fixed indoor and outdoor sensor networks while also deploying mobile sensors. The network helps building occupants, building operators, and public officials to safely manage extreme heat and air pollution. The Exposure Duration Curve (EDC) concept is introduced to facilitate comparisons. 

To increase the storage capacity of hard disk drives, Heat-Assisted Magnetic Recording (HAMR) takes advantage of laser heating to temporarily reduce the coercivity of recording media, enabling the writing of very small data bits on materials with high thermal stability. One key challenge in implementing HAMR is effective thermal management, which requires reliable determination of the thermal properties of HAMR materials over their range of operating temperature. This work reports the thermal properties of dielectric (amorphous silica, amorphous alumina, and AlN), metallic (gold and copper), and magnetic alloy (NiFe and CoFe) thin films used in HAMR heads from room temperature to 500 K measured with time-domain thermoreflectance. Our results show that the thermal conductivities of amorphous silica and alumina films increase with temperature, following the typical trends for amorphous materials. The polycrystalline AlN film exhibits weak thermal anisotropy, and its in-plane and through-plane thermal conductivities decrease with temperature. The measured thermal conductivities of AlN are significantly lower than that which would be present in single-crystal bulk material, and this is attributed to enhanced phonon-boundary scattering and phonon-defect scattering. The gold, copper, NiFe, and CoFe films show little temperature dependence in their thermal conductivities over the same temperature range. The measured thermal conductivities of gold and copper films are explained by the diffuse electron-boundary scattering using an empirical model. 

Abstract As coastal populations surge, the devastation caused by hurricanes becomes more catastrophic. Understanding the extent of the damage is essential as this knowledge helps shape our plans and decisions to reduce the effects of hurricanes. While community and property-level damage post-hurricane damage assessments are common, evaluations at the building component level, such as roofs, windows, and walls, are rarely conducted. This scarcity is attributed to the challenges inherent in automating precise object detections. Moreover, a significant disconnection exists between manual damage assessments, typically logged-in spreadsheets, and images of the damaged buildings. Extracting historical damage insights from these datasets becomes arduous without a digital linkage. This study introduces an innovative workflow anchored in state-of-the-art deep learning models to address these gaps. The methodology offers enhanced image annotation capabilities by leveraging large-scale pre-trained instance segmentation models and accurate damaged building component segmentation from transformer-based fine-tuning detection models. Coupled with a novel data repository structure, this study merges the segmentation mask of hurricane-affected components with manual damage assessment data, heralding a transformative approach to hurricane-induced building damage assessments and visualization. 

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. 

First Floor Elevation (FFE) of a house is crucial information for flood management and for accurately assessing the flood exposure risk of a property. However, the lack of reliable FFE data on a large geographic scale significantly limits efforts to mitigate flood risk, such as decision on elevating a property. The traditional method of collecting elevation data of a house relies on time-consuming and labor-intensive on-site inspections conducted by licensed surveyors or engineers. In this paper, we propose an automated and scalable method for extracting FFE from mobile LiDAR point cloud data. The fine-tuned yolov5 model is employed to detect doors, windows, and garage doors on the intensity-based projection of the point cloud, achieving an mAP@0.5:0.95 of 0.689. Subsequently, FFE is estimated using detected objects. We evaluated the Median Absolute Error (MAE) metric for the estimated FFE in Manville, Ventnor, and Longport, which resulted in values of 0.2 ft, 0.27 ft, and 0.24 ft, respectively. The availability of FFE data has the potential to provide valuable guidance for setting flood insurance premiums and facilitating benefit-cost analyses of buyout programs targeting residential buildings with a high flood risk. 

Accurate flood forecasting and efficient emergency response operations are vital, especially in the case of urban flash floods. The dense distribution of power lines in urban areas significantly impacts search and rescue operations during extreme flood events. However, no existing emergency response frameworks have incorporated the impacts of overhead power lines on lifeboat rescue operations. This study aims to determine the necessity and feasibility of incorporating overhead power line information into an emergency response framework using Manville, New Jersey during Hurricane Ida as a test bed. We propose an integrated framework, which includes a building-scale flood model, urban point cloud data, a human vulnerability model, and network analysis, to simulate rescue operation feasibility during Hurricane Ida. Results reveal that during the most severe point of the flood event, 46% of impacted buildings became nonrescuable due to complete isolation from the road network, and a significant 67.7% of the municipality’s areas that became dangerous for pedestrians also became inaccessible to rescue boats due to overhead power line obstruction. Additionally, we identify a continuous 10-hour period during which an average of 43.4% of the 991 impacted buildings faced complete isolation. For these structures, early evacuation emerges as the sole means to prevent isolation. This research highlights the pressing need to consider overhead power lines in emergency response planning to ensure more effective and targeted flood resilience measures for urban areas facing increasingly frequent extreme precipitation events. 

We analyze the effect of a bicycle lane on traffic speeds. Computer vision techniques are used to detect and classify the speed and trajectory of over 9,000 motor-vehicles at an intersection that was part of a pilot demonstration in which a bicycle lane was temporarily implemented. After controlling for direction, hourly traffic flow, and the behavior of the vehicle (i.e., free-flowing or stopped at a red light), we found that the effect of the delineator-protected bicycle lane (marked with traffic cones and plastic delineators) was associated with a 28 % reduction in average maximum speeds and a 21 % decrease in average speeds for vehicles turning right. For those going straight, a smaller reduction of up to 8 % was observed. Traffic moving perpendicular to the bicycle lane experienced no decrease in speeds. Painted-only bike lanes were also associated with a small speed reduction of 11–15 %, but solely for vehicles turning right. These findings suggest an important secondary benefit of bicycle lanes: by having a traffic calming effect, delineated bicycle lanes may decrease the risk and severity of crashes for pedestrians and other road users.