Recent innovations in 3D processing and availability of geospatial data have contributed largely to more comprehensive solutions to data visualization. As various data formats are utilized to describe the data, a combination of layers from different sources allow us to represent 3D urban areas, contributing to ideas of emergency management and smart cities. This work focuses on 3D urban environment reconstruction using crowdsourced OpenStreetMap data. Once the data are extracted, the visualization pipeline draws features using coloring for added context. Moreover, by structuring the layers and entities through the addition of simulation parameters, the generated environment is made simulation ready for further use. Results show that urban areas can be properly visualized in 3D using OpenStreetMap data given data availability. The simulation-ready environment was tested using hypothetical flooding scenarios, which demonstrated that the added parameters can be utilized in environmental simulations. Furthermore, an efficient restructuring of data was implemented for viewing the city information once the data are parsed.
Due to false negative results of the real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test, the complemental practices such as computed tomography (CT) and X-ray in combination with RT-PCR are discussed to achieve a more accurate diagnosis of COVID-19 in clinical practice. Since radiology includes visual understanding as well as decision making under limited conditions such as uncertainty, urgency, patient burden, and hospital facilities, mistakes are inevitable. Therefore, there is an immediate requirement to carry out further investigation and develop new accurate detection and identification methods to provide automatically quantitative evaluation of COVID-19. In this paper, we propose a new computer-aided diagnosis application for COVID-19 detection using deep learning techniques. A new technique, which receives symmetric X-ray data as the input, is presented in this study by combining Convolutional Neural Networks (CNN) with Ant Lion Optimization Algorithm (ALO) and Multiclass Naïve Bayes Classifier (NB). Moreover, several other classifiers such as Softmax, Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are combined with CNN. The promising results of these classifiers are evaluated and presented for accuracy, precision, and F1-score metrics. NB classifier with Ant Lion Optimization Algorithm and CNN produced the best results with 98.31% accuracy, 100% precision and 98.25% F1-score and with the lowest execution time.
Flood modeling and analysis has been a vital research area to reduce damages caused by flooding and to make urban environments resilient against such occurrences. This work focuses on building a framework to simulate and visualize flooding in 3D using position-based fluids for real-time flood spread visualization and analysis. The framework incorporates geographical information and takes several parameters in the form of friction coefficients and storm drain information, and then uses mechanics such as precipitation and soil absorption for simulation. The preliminary results of the river flooding test case were satisfactory, as the flood extent was reproduced in 220 s with a difference of 7%. Consequently, the framework could be a useful tool for practitioners who have information about the study area and would like to visualize flooding using a particle-based approach for real-time particle tracking and flood path analysis, incorporating precipitation into their models.
Flood simulations are vital to gain insight into possible dangers and damages for effective emergency planning. With flexible and natural ways of visualizing water flow, more precise evaluation of the study area is achieved. In this study, we describe a method for flood visualization using both regular and adaptive grids for position-based fluids method to visualize the depth of water in the study area. The mapping engine utilizes adaptive cell sizes to represent the study area and utilizes Jenks natural breaks method to classify the data. Predefined single-hue and multi-hue color sets are used to generate a heat map of the study area. It is shown that the dynamic representation benefits the mapping engine through enhanced precision when the study area has non-disperse clusters. Moreover, it is shown that, through decreasing precision, and utilizing an adaptive grid approach, the simulation runs more efficiently when particle interaction is computationally expensive.
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