Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of “just-in-time” injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption.

Personalized routing counts on traveler’s preferences which are usually based on different criteria, such as shortest, fastest, least traffic, or less expensive (e.g., less fuel cost, toll free). However, people are increasingly becoming concerned about the adverse health effects of exposure to air pollution in chosen routes. Exposures to elevated air pollution concentrations particularly endanger children, pregnant women, elderly people, and people with asthma and other respiratory conditions. Choosing routes with least air pollution exposure (APE) is seen as one approach to minimize the level of pollution exposed, which is a major public health issue. Routing algorithms use weights on segments of road networks to find optimum routes. While existing weights are commonly distance and time, among a few others, there is currently no weight based on APE to compute least APE routes. In this paper, we present a weight function that computes weight based on APE. Two different approaches, geostatistical and non-geostatistical, were used to compute APE weight. Each approach was evaluated, and the results indicate that the APE weight is suitable for computing least APE routes.

Web Mapping APIs (WMAs), such as Google Maps API, are widely used by researchers across different fields to develop geospatial Web applications. Among maps and map functionalities provided through WMAs, route and direction are prominent and commonly available. Given that each WMA uses a different map database and a different set of assumptions, the routes they generate, for the same pairs of origin and destination addresses, are different. Considering the current void in literature on WMAs and the routes they generate, in this paper, select common WMAs are compared and analyzed based on their routing techniques. The results of these comparisons will benefit researchers by helping them better understand the behavior of WMAs in producing routes, which in turn can be used for selecting suitable WMAs for research projects or developing applications (such as navigation and location-based services). The process in which routes are evaluated can also be used as a guideline to help researchers explore behavior of WMAs in generating routes.

There are generally different and less wayfinding options for pedestrians than those for drivers. This makes developing models and tools that assist pedestrians in finding routes more challenging. The problem is even further exacerbated when specific routing requirements of people with disabilities are considered. While currently much research is focused on developing solutions for wayfinding of pedestrians, very few address the specific requirements of individuals with disabilities and none is focused on evaluation of the accessibility of built environments. In this paper, we propose a new approach in evaluating the accessibility of built environments for wayfinding of individuals with disabilities. The proposed approach involves simulation and visualization of the accessibility of sidewalk segments allowing urban planners, and other designers and engineers, to gain an understanding of how accessible built environments are and allowing individuals with disabilities to assess the accessibility of built environments with respect to their mobility needs. Simulations were conducted using a sidewalk network database which contains accessibility attributes based on the standards recommended by the Americans with Disabilities Act. To demonstrate the benefits of the proposed approach, a representative model was used to simulate scenarios for the wayfinding requirements at both community and individual levels. The results of the simulations are visualized in heat-maps.