Date Available
4-23-2018
Year of Publication
2018
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Engineering
Department/School/Program
Computer Science
Advisor
Dr. Nathan Jacobs
Abstract
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.
Digital Object Identifier (DOI)
https://doi.org/10.13023/ETD.2018.128
Recommended Citation
Workman, Scott, "Leveraging Overhead Imagery for Localization, Mapping, and Understanding" (2018). Theses and Dissertations--Computer Science. 64.
https://uknowledge.uky.edu/cs_etds/64
