RAPID: Earthquake Damage Assessment from Social Media

在最近新西兰和日本的地震以及海地的风暴发生后的几分钟和几个小时内，数千名当地人在Facebook和Twitter等社交媒体网站上发布了照片。这些图片与极其细粒度的时空信息(例如，时间戳和GPS风格的地理编码)相结合，提供了紧急情况展开时的逐分钟和逐区域的图示描述。该项目的目标是在紧急情况发生后的几分钟和几小时内评估、描述和模拟发布到社交媒体上的这些图像的质量，以指导基于政策的利益相关者和资产。精心设计的图像可以向恢复专家传达丰富的结构信息：揭示破坏程度，指导资源分配，并指导其他基于政策的资产。首先，来自新西兰的数千张社交媒体图片的样本将由领域专家，特别是结构地震工程师进行评估。其次，在快速融资的情况下，该项目将把紧急情况下张贴的图像与克赖斯特彻奇进行的实际损失评估联系起来，以验证图像的质量。首先，来自新西兰的数千张社交媒体图片的样本将由领域专家，特别是结构地震工程师进行评估。该项目的结果将产生广泛影响，特别是在开发和部署基于社交媒体的新的地震损失快速评估工具方面。另一个更广泛的影响是辅助开发培训模块，以提高未来社会生成的图像捕获的有效性和图像质量，这将大大改善灾害的社会计算。该项目产生的方法和数据将被存档，供今后研究使用。

In the minutes and hours following the recent earthquakes in New Zealand and in Japan, and storm in Haiti, thousands of locals posted pictures to social media sites like Facebook and Twitter. These pictures when coupled with extremely granular spatio-temporal information (e.g., timestamps and GPS-style geocodes) provide a minute-by-minute and region-by-region pictorial account of the emergency as it unfolded. The goal of this project is to assess, characterize, and model the quality of these images posted to social media in the minutes and hours post-emergency for guiding policy-based stakeholders and assets. Carefully framed images can convey a wealth of structural information to recovery experts: revealing damage levels, guiding resource allocation, and directing other policy-based assets. First, a sample of several thousand social media images from New Zealand will be assessed by domain experts and specifically structural earthquake engineers. Second, with RAPID funding, this project will link images posted during the emergency to actual damage assessments made in Christchurch for validating the quality of images. First, a sample of several thousand social media images from New Zealand will be assessed by domain experts and specifically structural earthquake engineers. The results of this project will have broad impacts, particularly in the development and deployment of a new rapid assessment tool for earthquake damage assessment based on social media. An additional broader impact is the ancillary development of training modules for increasing the effectiveness and image quality of future socially-generated image capture, which would greatly improve social computing for disasters. The methods and data generated by this project will be archived and made available for future studies.