CDS&E: Enabling Time-critical Decision-support for Disaster Response and Structural Engineering through Automated Visual Data Analytics

灾难发生后，由训练有素的工程师组成的团队负责收集易腐烂的数据。这些建筑侦察小组从遭受灾难的建筑中收集数据和信息，包括该地区的照片和测量。收集这些信息是为了更好地了解这些事件的后果，并改进未来结构的设计。在短短几天内就会生成大量的图像和视频，为了在允许的时间内收集最关键的信息，这些团队的工程师必须快速做出决定，决定在哪里收集哪些数据，以完成他们的任务。这项研究利用强大的计算机视觉方法来解决现实世界的土木工程问题，旨在开发有效的方法来分析和组织现场收集的图像，从而使团队能够收集最有用的数据，以在全球范围内建立有弹性的社区。该项目将利用项目研究人员以及国内外合作者数十年的实地考察经验。多元化的学生将有机会从事跨学科的研究。应用计算机视觉方法来解决灾害响应和结构工程问题并不简单或直接。该项目将系统地建立在时间紧迫的情况下成功实施所需的知识。具有丰富实地任务经验的工程师将对图像进行注释。这些记录将为确定在现场做出决策所需的视觉内容以及这些内容在图像中的空间互联方式提供基础。这构成了确定先验知识的基础，这些先验知识可以而且必须包含在深度神经网络结构中，以促进该领域的快速决策。为了定量评估该方法，将建立一个侦察试验台，使用来自过去数据收集任务的各种图像集。计算时间和准确性将被测量和记录，以建立分类结果的详细概况。这些知识将使在侦察任务中收集数据的团队能够通过确保他们能够在一定的时间内成功执行给定的任务，并应用于一组图像，从而最大限度地发挥他们收集的数据的价值。该功能将为进一步调查和/或设计指南的更改提供基础建议的证据。

After a disaster, teams of trained engineers are charged with the task of collecting perishable data. These building reconnaissance teams collect data and information from the buildings that experienced the disaster, including photographs and measurements in the region. This information is collected to better understand the consequences of these events, and to improve the design of future structures. An enormous amount of images and videos is generated in just a few days, and to gather the most critical information in the time allowed, the engineers on these teams must quickly make daily decisions on where and what data to collect to achieve their mission. This research, which harnesses powerful computer vision methods to address real world civil engineering problems, aims to develop efficient methods to analyze and organize the collected images in the field, thereby enabling teams to collect the most useful data for building resilient communities worldwide. The project will leverage decades of experience in field missions from project researchers and domestic and international collaborators. A diverse set of students will be engaged in interdisciplinary research with international opportunities.The application of computer vision methods to address disaster response and structural engineering problems is not simple or straightforward. This project will systematically build the knowledge needed for their successful implementation in time-critical situations. Engineers with significant field-mission experience will annotate images. These records will provide the basis for determining the visual contents needed to make decisions in the field and how the contents are spatially interconnected in the images. This forms the foundation for determining the prior knowledge that can and must be included in the deep neural network structures to facilitate rapid decision-making in the field. To quantitatively evaluate the approach, a reconnaissance testbed will be established using a diverse set of images from past data collection missions. The computational time and accuracy will be measured and documented to establish a detailed profile of the classification results. This knowledge will enable the team collecting data during a reconnaissance mission to maximize the value of the data they collect by ensuring that they can successfully perform a given task, in a certain amount of time, applied to a suite of images. This capability will provide the evidence on which to base recommendations for further investigations and/or changes to design guidelines.