Enabling enhancement of scientific environmental data by volunteered geographic information: Extraction and visual assessment of data from social media images (ENAP)

通过自愿提供的地理信息增强科学环境数据：从社交媒体图像中提取数据并进行视觉评估（ENAP）

• 批准号: 314596036
• 负责人: Professor Dr.-Ing. Joachim Denzler
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/314596036?language=en
• 关键词: Enabling; enhancement; scientific; environmental; data

The objective of the project is to enable creation of data composites combining data from sensors and social media images for scientific purpose. Data composites are applied in environmental science for instance as input values for models or for validation of simulation model output. To fill missing values in data composites scientists use proxy-data that are derived from other related information. For instance, to fill missing values in climate data scientists derive proxy-data from ice cores. Proxy data has varying accuracy; therefore, they have to be assessed seriously, if they are appropriate for scientific use. We aim at developing a semi-automatic process chain that enables derivation of proxy-data from social media images and assessment of proxy-data with respect to their scientific value for a data composite. Our concrete use case is quantification of flood damage; here the data composites are used as input for flood damage models. The research topics we address are: a) filtering relevant images from social media streams, b) derive proxy-data from filtered images, c) assess proxy-data with respect to its scientific value for the data composite. We investigate these topics in a combined approach of computer vision and visual analytics. Results will be: i) enhanced computer vision methods to filter relevant images from social media streams and to extract determined proxy-data from filtered images, ii) a novel visual analytics approach to assess scientific value proxy-data can contribute to data composite regarding accuracy, coverage and resolution of data.

该项目的目标是创建数据复合体，将传感器和社交媒体图像的数据结合起来用于科学目的。数据合成应用于环境科学，例如作为模型的输入值或用于验证模拟模型输出。为了填补数据组合中的缺失值，科学家们使用从其他相关信息中派生的代理数据。例如，为了填补气候数据中的缺失值，科学家们从冰芯中获取代理数据。代理数据的准确性各不相同;因此，如果它们适合科学使用，就必须对其进行认真评估。我们的目标是开发一个半自动的过程链，使代理数据从社交媒体图像和评估代理数据的科学价值的数据组合的派生。我们的具体用例是洪水损失的量化;这里的数据组合用作洪水损失模型的输入。我们的研究主题是：a）从社交媒体流中过滤相关图像，B）从过滤后的图像中获得代理数据，c）评估代理数据对数据组合的科学价值。我们研究这些主题的计算机视觉和视觉分析的结合方法。结果将是：i）增强的计算机视觉方法，用于从社交媒体流中过滤相关图像，并从过滤后的图像中提取确定的代理数据，ii）评估科学价值的新颖视觉分析方法，代理数据可以有助于数据的准确性，覆盖范围和分辨率的数据合成。