Development of a Pavement Evaluation Tool Using Aerial Imagery and Deep Learning

This paper presents the research results of using Google Earth imagery for visual condition surveying of highway pavement in the United States. A screenshot tool is developed to automatically track the highway for collecting end-to-end images and Global Position System (GPS). A highway segmentation tool based on a deep convolutional neural network (DCNN) is developed to segment the collected highway images into the predefined object categories, where the cracks are identified and labeled in each small patch of the overlapping assembled label-image prediction. Then, the longitudinal cracks and transverse cracks are detected using the x-gradient and y-gradient from the Sobel operator, and the developed pavement evaluation tool rates the longitudinal cracking in (linear feet per 100 ft. station) and transverse cracking in number per -Station (100 ft. station), which can be visualized in ArcGIS Online. Experiments were conducted on Interstate 43 (I-43) in Milwaukee County with pavement in both defective and sound visual conditions. Experimental results showed the patch-wise highway segmentation in Google Earth imagery from the DCNN model has as precise pixel accuracy as the U-net-based pixelwise crack/noncrack classifier. Compared to the manually crafted label image in the experimental area, the rated longitudinal cracking has an average error of overrating 20%, while transverse cracking has an average error of underrating 7%. This research project contributes to visual pavement condition surveying methodology with the free-to-access Google Earth imagery, which is a feasible, cost-effective option for accurately rating and geographically visualizing both project-level and network-level pavement

CONVERSATION AND COMMUNITY BUILDING IN PRIDE AND PREJUDICE

The thematic research on Jane Austen’s novels has been widely carried out, including marriage, gender, morality, politics, etc. The conception of community is also an important theme for her novels are set in a period when the notion of community is conceived and reinforced in the West. It is a transitional age that witnesses the change from the 18th century when the feudal aristocracy controls the agricultural economy to the 19th century which is dominated by the middle class as a result of the Industrial Revolution. However, the theme of community has not got enough critical attention and its research is sparse. Thus, this dissertation seeks to explore Austen’s contribution to the conception of community in Pride and Prejudice. Austen’s imagination of community is effectively displayed in Pride and Prejudice and conversation serves as a key approach. The Community is built at two levels, namely, familial level and social level. By means of conversation, a family bond based on mutual affirmation, which is the core of community building, is forged; and a community of spirit, the highest form of community, is established among social interactions outside families in two social spaces– Meryton and Pemburley. A stereoscopic vision of a community built by conversation emerges when the three levels are closely intertwined

Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies

This paper presents an accurate and stable method for object and defect detection and visualization on building and infrastructural facilities. This method uses drones and cameras to collect three- dimensional (3D) point clouds via photogrammetry, and uses orthographic or arbitrary views of the target objects to generate the feature images of points’ spectral, elevation, and normal features. U-Net is implemented in the pixelwise segmentation for object and defect detection using multiple feature images. This method was validated on four applications, including on-site path detection, pavement cracking detection, highway slope detection, and building facade window detection. The comparative experimental results confirmed that U-Net with multiple features has a better pixelwise segmentation performance than separately using each single feature. The developed method can implement object and defect detection with different shapes, including striped objects, thin objects, recurring and regularly shaped objects, and bulky objects, which will improve the accuracy and efficiency of inspection, assessment, and management of buildings and infrastructural facilities

Scan4Façade: Automated As-Is Façade Modeling of Historic High-Rise Buildings Using Drones and AI

This paper presents an automated as-is façade modeling method for existing and historic high-rise buildings, named Scan4Façade. To begin with, a camera drone with a spiral path is employed to capture building exterior images, and photogrammetry is used to conduct three-dimensional (3D) reconstruction and create mesh models for the scanned building façades. High-resolution façade orthoimages are then generated from mesh models and pixelwise segmented by an artificial intelligence (AI) model named U-net. A combined data augmentation strategy, including random flipping, rotation, resizing, perspective transformation, and color adjustment, is proposed for model training with a limited number of labels. As a result, the U-net achieves an average pixel accuracy of 0.9696 and a mean intersection over union of 0.9063 in testing. Then, the developed twoStagesClustering algorithm, with a two-round shape clustering and a two-round coordinates clustering, is used to precisely extract façade elements’ dimensions and coordinates from façade orthoimages and pixelwise label. In testing with the Michigan Central Station (office tower), a historic high-rise building, the developed algorithm achieves an accuracy of 99.77% in window extraction. In addition, the extracted façade geometric information and element types are transformed into AutoCAD command and script files to create CAD drawings without manual interaction. Experimental results also show that the proposed Scan4Façade method can provide clear and accurate information to assist BIM feature creation in Revit. Future research recommendations are also stated in this paper

Photometric redshift estimation of galaxies in the DESI Legacy Imaging Surveys

The accurate estimation of photometric redshifts plays a crucial role in accomplishing science objectives of the large survey projects. The template-fitting and machine learning are the two main types of methods applied currently. Based on the training set obtained by cross-correlating the DESI Legacy Imaging Surveys DR9 galaxy catalogue and SDSS DR16 galaxy catalogue, the two kinds of methods are used and optimized, such as EAZY for template-fitting approach and CATBOOST for machine learning. Then the created models are tested by the cross-matched samples of the DESI Legacy Imaging SurveysDR9 galaxy catalogue with LAMOST DR7, GAMA DR3 and WiggleZ galaxy catalogues. Moreover three machine learning methods (CATBOOST, Multi-Layer Perceptron and Random Forest) are compared, CATBOOST shows its superiority for our case. By feature selection and optimization of model parameters, CATBOOST can obtain higher accuracy with optical and infrared photometric information, the best performance ($MSE=0.0032$, $\sigma_{NMAD}=0.0156$ and $O=0.88$ per cent) with $g \le 24.0$, $r \le 23.4$ and $z \le 22.5$ is achieved. But EAZY can provide more accurate photometric redshift estimation for high redshift galaxies, especially beyond the redhisft range of training sample. Finally, we finish the redshift estimation of all DESI DR9 galaxies with CATBOOST and EAZY, which will contribute to the further study of galaxies and their properties.Comment: Accepted for publication in MNRAS. 14 pages, 9 figures, 11 table