Hazards SEES Type 1: Real-Time Geospatial Infrastructure Modeling for Disaster Response and Rapid Recovery

准确和及时的信息对于推动对自然灾害作出协调和有效的反应至关重要；地理空间数据用于决策支持和态势感知至关重要，因为它提供了有关地面条件的关键信息。不幸的是，地理空间数据的实时应用仍然受到严重瓶颈的阻碍，这些瓶颈延迟了对第一响应者的可用性，并且缺乏自动化工具来分析这些信息，以确定可采取行动的项目的优先次序并确定关键的响应领域。该项目将通过集成和测试硬件和软件解决方案来研究缓解瓶颈的方法，该解决方案将捕获、参考数据并传播给分布式用户组。将开发基于地理信息系统的多基础设施模型，帮助第一反应者将大量地理空间数据提炼成可操作的项目。由于激光雷达遥感数据具有直接提供3D测绘和变化检测的独特能力，因此该项目将重点关注激光雷达遥感数据。为了完成这些任务，将进行三项对实时态势感知至关重要的科学研究：(1)研究地理空间数据可获得的空间精度；(2)开发实时确定高分辨率变化的算法；(3)通过基于gis的多基础设施模型传播检测到的高分辨率3D变化。此外，该项目将定义实时分析地理空间数据的自动化过程，以协助对自然灾害的响应和快速恢复。开发的实时工具将允许利用现有的激光雷达数据集作为基线模型，在发生自然灾害时进行快速变化检测，并实现更有效的响应和更快的自然灾害恢复。飓风和地震等自然灾害可能对社区产生重大影响，最近的飓风桑迪和艾克以及2011年Tôhoku日本地震就清楚地证明了这一点。遗憾的是，飓风和地震的发生都是我们无法控制的。然而，可以控制的是对这些事件的反应，以尽量减少其影响并刺激事件后的恢复。目前，在自然事件发生后，决策者受到关于灾害地点、规模和性质的信息传播效率低下的阻碍。为了减轻这些挑战，研究小组将开发一套实时软件工具，使地理空间信息分析能够确定一套优先可行的项目，这将加快对自然灾害的反应，并帮助从自然灾害中快速恢复。该项目汇集了一个多学科的研究人员和学生团队，以及来自加利福尼亚州和德克萨斯州的地震和飓风应急专家，以开发实时模型，有效传播地理空间信息，以便对自然灾害作出快速和协调的反应。

Accurate and timely information are essential to drive a coordinated and effective response to natural disasters; and the use of geospatial data for decision support and situational awareness is vital, as it provides critical information regarding conditions on the ground. Unfortunately, the real-time application of geospatial data is still hampered by significant bottlenecks that delay its availability to first responders, and by the lack of automated tools to analyze this information to prioritize actionable items and identify critical areas for response. This project will investigate ways to mitigate bottlenecks by integrating and testing a hardware and software solution that will capture, georeference data and disseminate to a distributed user group. GIS-based multi-infrastructure models will be developed that will assist first-responders in distilling large volumes of geospatial data into actionable items. The effort will focus on the LiDAR remote sensing data due to its unique ability to directly provide 3D mapping and change detection. To accomplish these tasks, three scientific investigations that are fundamental to real-time situational awareness will be pursued: (1) an investigation of spatial accuracy obtainable with geospatial data, (2) development of algorithms for real-time determination of high-resolution change, and, (3) dissemination of detected high-resolution 3D change through GIS-based multi-infrastructure models. In addition, the project will define automated process to analyze geospatial data in real-time to assist in the response to and rapid recovery from natural disasters. The real-time tools developed will allow the leveraging of existing LiDAR datasets as a baseline model for rapid change detection in the event of a natural disaster, and enable more effective response and quicker recovery from natural hazard occurrences.Natural hazards such as hurricanes and earthquakes can have significant impact on communities, as clearly evidenced by recent hurricanes Sandy and Ike, and the 2011 Tôhoku earthquake in Japan. Regrettably, the occurrence of both hurricanes and earthquakes is beyond our control. However, can be controlled is the response to these events to minimize their impact and spur post-event recovery. Currently, in the aftermath of natural events, decision makers are hampered by the inefficient dissemination of information regarding the location, scale and nature of the devastation. To mitigate these challenges, the research team will develop a set of real-time software tools to enable the analysis of geospatial information to define a set of prioritized actionable items that will speed the response to, and aid in the rapid recovery from natural disasters. This project brings together a multi-disciplinary team of researchers and students, and experts in emergency response to earthquakes and hurricanes from California and Texas to develop real-time models for effective dissemination of geospatial information for rapid and coordinated response to natural hazards.