Home > Seminars > Nathan Jacobs - Understanding Places Using Ground-Level and Overhead Views

Nathan Jacobs - Understanding Places Using Ground-Level and Overhead Views


2/28/2019 at 3:30PM


2/28/2019 at 4:45PM


126 DeBartolo


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Walter Scheirer

Walter Scheirer

VIEW FULL PROFILE Email: walter.scheirer@nd.edu
Phone: 574-631-2436
Website: http://www.nd.edu/~wscheire
Office: 321C Stinson-Remick Hall


College of Engineering Assistant Professor
Primary interests in Computer Vision, Machine Learning, Biometrics,and Digital Humanities. Specific areas of research include Open Set Recognition, Extreme Value Theory Models for Visual Recognition, Biologically-inspired Learning Algorithms, and Stylometry.
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Every day billions of images are uploaded to the Internet and trillions of pixels are gathered by satellites. Together they provide many perspectives of the world, ranging from what someone had for dinner to where new buildings have been constructed. This talk describes my work in extracting geospatial attributes of places from this imagery using deep convolutional neural networks. I will focus on methods that combine overhead and ground-level imagery in novel ways, with applications including image localization, population density mapping, cross-modal hallucination, weakly supervised learning, and fine-grained classification of buildings in urban areas.

Seminar Speaker:

Nathan Jacobs

Nathan Jacobs

University of Kentucky

Nathan Jacobs earned a Ph.D. in Computer Science at Washington University in St. Louis (2010). Since then, he has been a Professor of Computer Science at the University of Kentucky. Dr. Jacobs' research area is computer vision; his specialty is developing learning-based algorithms and systems for processing large-scale image collections. His current focus is on developing techniques for mining information about people and the natural world from geotagged imagery, including images from social networks, publicly available outdoor webcams, and satellites. His research has been funded by NSF, NIH, DARPA, IARPA, NGA, ARL, AFRL, and Google.