CAREER: Learning and Using Models of Geo-Temporal Appearance

数十亿带有地理标记和时间戳的图像通过Internet公开提供，提供了全球各地的人、地点和事物外观的丰富记录。这些图像是一个很大程度上尚未开发的资源，可以用来提高我们对世界的理解，以及它是如何随着时间的推移而变化的。该项目开发了从这些图像中提取有用信息的自动化方法，并将其融合到捕捉地球时间趋势的高分辨率全球模型中。一旦趋势被捕获，这些模型被用于提高计算机视觉任务的性能，并使地理标记图像成为其他学科教育和研究的可用和可导航的资源。该项目包括一个教育和推广组件，将现实世界的问题带给计算机科学（CS）学生，指导跨教育范围的学生，并使研究对公众开放。该项目开发计算机视觉技术来捕捉空间和时间的外观趋势，并分为四个主要研究重点：(1)研究利用弱监督学习从互联网图像中提取信息的新方法；(2)开发将地面图像与航空和卫星数据相结合的技术，以模拟世界上任何地方、任何时间的预期图像外观；(3)评估使用这些模型来提高计算机视觉算法性能的方法；(4)自动创建可视化表示，使新手用户能够通过互联网探索所学的地理时间趋势。项目网页：http://geotemporal.csr.uky.edu

Billions of geotagged and time-stamped images are publicly available via the Internet, providing a rich record of the appearance of people, places, and things across the globe. These images are a largely untapped resource that could be used to improve our understanding of the world and how it changes over time. This project develops automated methods of extracting useful information from this imagery and fusing it into high-resolution global models that capture geo-temporal trends. Once the trends have been captured, these models are used to improve performance on computer vision tasks and make geotagged imagery a usable and navigable resource for education and research in other disciplines. The project includes an education and outreach component that brings real-world problems to computer science (CS) students, mentors students across the educational spectrum, and makes the research accessible to the public.This project develops computer vision technologies to capture spatial and temporal appearance trends and is organized into four main research thrusts: (1) investigating novel methods for extracting information from Internet imagery using weakly supervised learning, (2) developing techniques that integrate ground-level imagery with aerial and satellite data to model the expected image appearance anywhere in the world at any time, (3) evaluating methods for using such models to improve the performance of computer vision algorithms, and (4) automatically creating visual representations that make it possible for novice users to explore the learned geo-temporal trends via the Internet.Project webpage: http://geotemporal.csr.uky.edu