RAPID: Earthquake Damage Assessment from Social Media

在最近新西兰和日本发生地震以及海地发生风暴后的几分钟和几个小时内，数千名当地人将照片发布到Facebook和Twitter等社交媒体网站上。这些图片在与极细粒度的时空信息（例如，时间戳和全球定位系统式的地理编码）提供了紧急情况发生时的逐分钟和逐区域的图像说明。该项目的目标是评估、表征和建模这些在紧急情况发生后的几分钟和几小时内发布到社交媒体上的图像的质量，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：揭示损害程度，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像样本将由领域专家和结构地震工程师进行评估。第二，在RAPID的资助下，该项目将把紧急情况期间张贴的图像与基督城进行的实际损害评估联系起来，以验证图像的质量。 首先，来自新西兰的数千张社交媒体图像样本将由领域专家和结构地震工程师进行评估。 该项目的成果将产生广泛影响，特别是在开发和部署一个新的快速评估工具，用于基于社交媒体的地震损失评估方面。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.