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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. 

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. 

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. 

With the objective of understanding microscopic principles governing thermal energy flow in nanojunctions, we study phononic heat transport through metal-molecule-metal junctions using classical molecular dynamics (MD) simulations. Considering a single-molecule gold-alkanedithiol-gold junction, we first focus on aspects of method development and compare two techniques for calculating thermal conductance: (i) The Reverse Nonequilibrium MD (RNEMD) method, where heat is inputted and extracted at a constant rate from opposite metals. In this case, the thermal conductance is calculated from the nonequilibrium temperature profile that is created at the junction. (ii) The Approach-to-Equilibrium MD (AEMD) method, with the thermal conductance of the junction obtained from the equilibration dynamics of the metals. In both methods, simulations of alkane chains of a growing size display an approximate length-independence of the thermal conductance, with calculated values matching computational and experimental studies. The RNEMD and AEMD methods offer different insights, and we discuss their benefits and shortcomings. Assessing the potential application of molecular junctions as thermal diodes, alkane junctions are made spatially asymmetric by modifying their contact regions with the bulk, either by using distinct endgroups or by replacing one of the Au contacts with Ag. Anharmonicity is built into the system within the molecular force-field. We find that, while the temperature profile strongly varies (compared with the gold-alkanedithiol-gold junctions) due to these structural modifications, the thermal diode effect is inconsequential in these systems—unless one goes to very large thermal biases. This finding suggests that one should seek molecules with considerable internal anharmonic effects for developing nonlinear thermal devices. 

An eXtreme Gradient Boosting (XGBoost) machine learning model is built to predict the electrocaloric (EC) temperature change of a ceramic based on its composition (encoded by Magpie elemental properties), dielectric constant, Curie temperature, and characterization conditions. A dataset of 97 EC ceramics is assembled from the experimental literature. By sampling data from clusters in the feature space, the model can achieve a coefficient of determination of 0.77 and a root mean square error of 0.38 K for the test data. Feature analysis shows that the model captures known physics for effective EC materials. The Magpie features help the model to distinguish between materials, with the elemental electronegativities and ionic charges identified as key features. The model is applied to 66 ferroelectrics whose EC performance has not been characterized. Lead-free candidates with a predicted EC temperature change above 2 K at room temperature and 100 kV/cm are identified.

 

The cloud imaging and particle size (CIPS) instrument onboard the Aeronomy of Ice in the Mesosphere satellite provides images of gravity waves (GWs) near the stratopause and lowermost mesosphere (altitudes of 50–55 km). GW identification is based on Rayleigh Albedo Anomaly (RAA) variances, which are derived from GW‐induced fluctuations in Rayleigh scattering at 265 nm. Based on 3 years of CIPS RAA variance data from 2019 to 2022, we report for the first time the seasonal distribution of GWs entering the mesosphere with high (7.5 km) horizontal resolution on a near‐global scale. Seasonally averaged GW variances clearly show spatial and temporal patterns of GW activity, mainly due to the seasonal variation of primary GW sources such as convection, the polar vortices and flow over mountains. Measurements of stratospheric GWs derived from Atmospheric InfraRed Sounder (AIRS) observations of 4.3 μm brightness temperature perturbations within the same 3‐year time range are compared to the CIPS results. The comparisons show that locations of GW hotspots are similar in the CIPS and AIRS observations. Variability in GW variances and the monthly changes in background zonal wind suggest a strong GW‐wind correlation. This study demonstrates the utility of the CIPS GW variance data set for statistical investigations of GWs in the lowermost mesosphere, as well as provides a reference for location/time selection for GW case studies.

 

Building an annotated damage image database is the first step to support AI-assisted hurricane impact analysis. Up to now, annotated datasets for model training are insufficient at a local level despite abundant raw data that have been collected for decades. This paper provides a systematic approach for establishing an annotated hurricane-damaged building image database to support AI-assisted damage assessment and analysis. Optimal rectilinear images were generated from panoramic images collected from Hurricane Harvey, Texas 2017. Then, deep learning models, including Amazon Web Service (AWS) Rekognition and Mask R-CNN (Region Based Convolutional Neural Networks), were retrained on the data to develop a pipeline for building detection and structural component extraction. A web-based dashboard was developed for building data management and processed image visualization along with detected structural components and their damage ratings. The proposed AI-assisted labeling tool and trained models can intelligently and rapidly assist potential users such as hazard researchers, practitioners, and government agencies on natural disaster damage management. 

High-resolution vehicle trajectory data can be used to generate a wide range of performance measures and facilitate many smart mobility applications for traffic operations and management. In this paper, a Longitudinal Scanline LiDAR-Camera model is explored for trajectory extraction at urban arterial intersections. The proposed model can efficiently detect vehicle trajectories under the complex, noisy conditions (e.g., hanging cables, lane markings, crossing traffic) typical of an arterial intersection environment. Traces within video footage are then converted into trajectories in world coordinates by matching a video image with a 3D LiDAR (Light Detection and Ranging) model through key infrastructure points. Using 3D LiDAR data will significantly improve the camera calibration process for real-world trajectory extraction. The pan-tilt-zoom effects of the traffic camera can be handled automatically by a proposed motion estimation algorithm. The results demonstrate the potential of integrating longitudinal-scanline-based vehicle trajectory detection and the 3D LiDAR point cloud to provide lane-by-lane high-resolution trajectory data. The resulting system has the potential to become a low-cost but reliable measure for future smart mobility systems.