Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment
| العنوان: | Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment |
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| المؤلفون: | Yijun Liao, Mohammad Ebrahim Mohammadi, Richard L. Wood |
| المصدر: | Drones Volume 4 Issue 2 Drones, Vol 4, Iss 24, p 24 (2020) |
| بيانات النشر: | MDPI AG, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Computer science, lcsh:Motor vehicles. Aeronautics. Astronautics, 0211 other engineering and technologies, Point cloud, convolutional neural network, Aerospace Engineering, 020101 civil engineering, 02 engineering and technology, transfer learning, computer.software_genre, Convolutional neural network, 0201 civil engineering, Artificial Intelligence, point clouds, 021105 building & construction, Data processing, Data collection, Event (computing), business.industry, Deep learning, deep learning, structural damage assessment, Computer Science Applications, Workflow, Control and Systems Engineering, Artificial intelligence, Data mining, lcsh:TL1-4050, Transfer of learning, business, computer, Information Systems |
| الوصف: | Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures. |
| وصف الملف: | application/pdf |
| تدمد: | 2504-446X |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69aae2376e2bc31bff4af7dd4349e9deTest https://doi.org/10.3390/drones4020024Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....69aae2376e2bc31bff4af7dd4349e9de |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 2504446X |
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