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20172022
most citedRevisiting Near/Remote Sensing with Geospatial Attention

1 citations · 1 across the 5 of their papers we have counts for

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cs.CV2022

Handling Image and Label Resolution Mismatch in Remote Sensing

Scott Workman, Armin Hadzic, M. Usman Rafique

Though semantic segmentation has been heavily explored in vision literature, unique challenges remain in the remote sensing domain. One such challenge is how to handle resolution m…

cs.CV20221 cited

Revisiting Near/Remote Sensing with Geospatial Attention

Scott Workman, M. Usman Rafique, Hunter Blanton +1

This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two m…

cs.CV2021

Augmenting Depth Estimation with Geospatial Context

Scott Workman, Hunter Blanton

Modern cameras are equipped with a wide array of sensors that enable recording the geospatial context of an image. Taking advantage of this, we explore depth estimation under the a…

cs.CV2020

Learning a Dynamic Map of Visual Appearance

Tawfiq Salem, Scott Workman, Nathan Jacobs

The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Every day billions of images capture this complex relati…

cs.CV2020

A Structure-Aware Method for Direct Pose Estimation

Hunter Blanton, Scott Workman, Nathan Jacobs

Estimating camera pose from a single image is a fundamental problem in computer vision. Existing methods for solving this task fall into two distinct categories, which we refer to…

cs.CV2020

Dynamic Traffic Modeling From Overhead Imagery

Scott Workman, Nathan Jacobs

Our goal is to use overhead imagery to understand patterns in traffic flow, for instance answering questions such as how fast could you traverse Times Square at 3am on a Sunday. A…