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Wenchi Shou
School of Engineering, Design and Built Environment, Western Sydney University, Sidney, NSW 2751, Australia

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Journal article
Published: 17 July 2021 in Remote Sensing
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Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model’s performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions.

ACS Style

Hai Sun; Xiaoyi Dai; Wenchi Shou; Jun Wang; Xuejing Ruan. An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data. Remote Sensing 2021, 13, 2818 .

AMA Style

Hai Sun, Xiaoyi Dai, Wenchi Shou, Jun Wang, Xuejing Ruan. An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data. Remote Sensing. 2021; 13 (14):2818.

Chicago/Turabian Style

Hai Sun; Xiaoyi Dai; Wenchi Shou; Jun Wang; Xuejing Ruan. 2021. "An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data." Remote Sensing 13, no. 14: 2818.

Journal article
Published: 30 April 2021 in Applied Sciences
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Scaffolding serves as one construction trade with high importance. However, scaffolding suffers from low productivity and high cost in Australia. Activity Analysis is a continuous procedure of assessing and improving the amount of time that craft workers spend on one single construction trade, which is a functional method for monitoring onsite operation and analyzing conditions causing delays or productivity decline. Workface assessment is an initial step for activity analysis to manually record the time that workers spend on each activity category. This paper proposes a method of automatic scaffolding workface assessment using a 2D video camera to capture scaffolding activities and the model of key joints and skeleton extraction, as well as machine learning classifiers, were used for activity classification. Additionally, a case study was conducted and showed that the proposed method is a feasible and practical way for automatic scaffolding workface assessment.

ACS Style

Wenzheng Ying; Wenchi Shou; Jun Wang; Weixiang Shi; Yanhui Sun; Dazhi Ji; Haoxuan Gai; Xiangyu Wang; Mengcheng Chen. Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning. Applied Sciences 2021, 11, 4143 .

AMA Style

Wenzheng Ying, Wenchi Shou, Jun Wang, Weixiang Shi, Yanhui Sun, Dazhi Ji, Haoxuan Gai, Xiangyu Wang, Mengcheng Chen. Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning. Applied Sciences. 2021; 11 (9):4143.

Chicago/Turabian Style

Wenzheng Ying; Wenchi Shou; Jun Wang; Weixiang Shi; Yanhui Sun; Dazhi Ji; Haoxuan Gai; Xiangyu Wang; Mengcheng Chen. 2021. "Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning." Applied Sciences 11, no. 9: 4143.

Journal article
Published: 01 April 2021 in Applied Sciences
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Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management.

ACS Style

Shuyuan Xu; Jun Wang; Peng Wu; Wenchi Shou; Xiangyu Wang; Mengcheng Chen. Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study. Applied Sciences 2021, 11, 3152 .

AMA Style

Shuyuan Xu, Jun Wang, Peng Wu, Wenchi Shou, Xiangyu Wang, Mengcheng Chen. Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study. Applied Sciences. 2021; 11 (7):3152.

Chicago/Turabian Style

Shuyuan Xu; Jun Wang; Peng Wu; Wenchi Shou; Xiangyu Wang; Mengcheng Chen. 2021. "Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study." Applied Sciences 11, no. 7: 3152.

Journal article
Published: 14 February 2021 in Sensors
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Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior under earthquake disaster coditions, established crowd response rules in emergencies, and described the drill strategy and exit familiarity quantitatively through a cellular automata model. By setting different exit familiarity ratios, simulation experiments under different strategies were conducted to predict people’s reactions before an emergency. The corresponding simulation results indicated that the evacuees’ training level could affect a multi-exit zone’s evacuation pattern and clearance time. Their exit choice preferences may disrupt the exit options’ balance, leading to congestion in some of the exits. Secondly, due to people’s rejection of long distances, congestion, and unfamiliar exits, some people would hesitant about the evacuation direction during the evacuation process. This hesitation would also significantly reduce the overall evacuation efficiency. Finally, taking a community in Zhuhai City, China, as an example, put forward the best urban evacuation drill strategy. The quantitative relation between exit familiar level and evacuation efficiency was obtained. The final results showed that the optimized evacuation plan could improve evacuation’s overall efficiency through the self-organization effect. These studies may have some impact on predicting crowd behavior during evacuation and designing the evacuation plan.

ACS Style

Hai Sun; LanLing Hu; Wenchi Shou; Jun Wang. Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology. Sensors 2021, 21, 1353 .

AMA Style

Hai Sun, LanLing Hu, Wenchi Shou, Jun Wang. Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology. Sensors. 2021; 21 (4):1353.

Chicago/Turabian Style

Hai Sun; LanLing Hu; Wenchi Shou; Jun Wang. 2021. "Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology." Sensors 21, no. 4: 1353.