Leveraging Overhead Imagery for Localization, Mapping, and Understanding

• Main Author: Workman, Scott
• Format: Others
• Published: UKnowledge 2018
• Subjects: computer vision; machine learning; remote sensing; geospatial analysis; Artificial Intelligence and Robotics; Computer Sciences; Remote Sensing
• Online Access: https://uknowledge.uky.edu/cs_etds/64; https://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1068&context=cs_etds

Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis is a general framework for estimating geospatial functions, such as land cover or land use, which integrates visual evidence from both ground-level and overhead image viewpoints.