Recurrent nuisance flooding is common across many parts of the globe and causes extensive challenges for drivers on the roadways. The prevailing monitoring methods for roadway flooding are costly and not automated or effective. The ubiquity of visual data from cameras and advancements in computing such as deep learning may offer cost-effective methods for automated flood depth estimation on roadways based on reference objects such as cars. However, flood depth estimation faces challenges due to the limited amount of data annotated with water levels and diverse scenes showing reference objects at various scales and perspectives. This study proposes a novel deep learning approach to automated flood depth estimation on roadways. Our proposed pipeline addresses variations in object perspective and scale. We have developed an innovative approach to generate and annotate flood images by manipulating existing image datasets of cars in various orientations and scales to simulate four floodwater levels for augmenting real flood images. Furthermore, we propose object scale normalization for our reference objects (cars) to improve water level predictions. The proposed model achieves an accuracy of 74.85% and F1 score of 74.32% for four water levels when tested with real flood data. The proposed approach substantially reduces the time and labor required for labeling datasets while addressing challenges in perspective/scale, offering a promising solution for image-based flood depth estimation.
A compact light detection and ranging (LiDAR) is a system that provides aerosols profile measurements by identifying the aerosol scattering ratio as function of the altitude. The aerosol scattering ratios are used to obtain multiple aerosol intensive ratio parameters known as backscatter color ratio, depolarization ratio, and lidar ratio. The aerosol ratio parameters are known to vary with aerosol type, size, and shape. In this paper, we employed lidar measurements to detect the potential source of the aerosol in the neighborhood of the campus of Old Dominion University. The lidar is employed to collect measurements at several locations in the area of study. Then, the lidar ratio and the color ratio are retrieved from collected measurements. To find the source of aerosol in the measurements, a tracking algorithm is implemented and employed to track the concentration of that pollution in the data. The results show that the source of soot pollution in the area of study is Hampton Blvd, a major street, in the area of the campus where the diesel trucks travel between the ports in the city of Norfolk.
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