CAREER: Learning and Using Models of Geo-Temporal Appearance

数以亿计的带有地理标记和时间戳的图像可通过互联网公开获取，为地球仪上的人物、地点和事物的外观提供了丰富的记录。这些图像是一个基本上未开发的资源，可以用来提高我们对世界的理解，以及它如何随着时间的推移而变化。该项目开发了从这些图像中提取有用信息的自动化方法，并将其融合到捕获地理时间趋势的高分辨率全球模型中。一旦捕捉到这些趋势，这些模型将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资源。该项目包括一个教育和外展部分，为计算机科学（CS）学生带来现实世界的问题，指导整个教育领域的学生，并使研究向公众开放。该项目开发计算机视觉技术，以捕捉空间和时间的外观趋势，并分为四个主要研究方向：（1）研究使用弱监督学习从互联网图像中提取信息的新方法，（2）开发整合地面-利用航空和卫星数据对图像进行水平成像，以在世界任何地方任何时间对预期的图像外观进行建模，（3）评估使用这种模型来提高计算机视觉算法性能的方法，以及（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习的地理时间趋势。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