Search Results for author: Scott Workman

Found 18 papers, 5 papers with code

Revisiting Near/Remote Sensing with Geospatial Attention

no code implementations CVPR 2022 Scott Workman, M. Usman Rafique, Hunter Blanton, Nathan Jacobs

We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks.

Image Segmentation Semantic Segmentation

Augmenting Depth Estimation with Geospatial Context

no code implementations ICCV 2021 Scott Workman, Hunter Blanton

We propose an end-to-end architecture for depth estimation that uses geospatial context to infer a synthetic ground-level depth map from a co-located overhead image, then fuses it inside of an encoder/decoder style segmentation network.

Depth Estimation

Learning a Dynamic Map of Visual Appearance

no code implementations CVPR 2020 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.

A Structure-Aware Method for Direct Pose Estimation

no code implementations22 Dec 2020 Hunter Blanton, Scott Workman, Nathan Jacobs

Direct methods, such as PoseNet, regress pose from the image as a fixed function, for example using a feed-forward convolutional network.

Image Retrieval Pose Estimation +2

Dynamic Traffic Modeling From Overhead Imagery

no code implementations CVPR 2020 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.

Single Image Cloud Detection via Multi-Image Fusion

no code implementations29 Jul 2020 Scott Workman, M. Usman Rafique, Hunter Blanton, Connor Greenwell, Nathan Jacobs

A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data.

Cloud Detection object-detection +2

Learning Geo-Temporal Image Features

no code implementations16 Sep 2019 Menghua Zhai, Tawfiq Salem, Connor Greenwell, Scott Workman, Robert Pless, Nathan Jacobs

We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a set of location and time estimation tasks.

FARSA: Fully Automated Roadway Safety Assessment

1 code implementation17 Jan 2019 Weilian Song, Scott Workman, Armin Hadzic, Xu Zhang, Eric Green, Mei Chen, Reginald Souleyrette, Nathan Jacobs

An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars).

What Goes Where: Predicting Object Distributions from Above

no code implementations2 Aug 2018 Connor Greenwell, Scott Workman, Nathan Jacobs

In this work, we propose a cross-view learning approach, in which images captured from a ground-level view are used as weakly supervised annotations for interpreting overhead imagery.

A Unified Model for Near and Remote Sensing

no code implementations ICCV 2017 Scott Workman, Menghua Zhai, David J. Crandall, Nathan Jacobs

To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age.

Density Estimation

Understanding and Mapping Natural Beauty

no code implementations ICCV 2017 Scott Workman, Richard Souvenir, Nathan Jacobs

While natural beauty is often considered a subjective property of images, in this paper, we take an objective approach and provide methods for quantifying and predicting the scenicness of an image.

Detecting Vanishing Points using Global Image Context in a Non-Manhattan World

1 code implementation CVPR 2016 Menghua Zhai, Scott Workman, Nathan Jacobs

Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains.

Horizon Line Estimation

Horizon Lines in the Wild

1 code implementation7 Apr 2016 Scott Workman, Menghua Zhai, Nathan Jacobs

The horizon line is an important contextual attribute for a wide variety of image understanding tasks.

Horizon Line Estimation

Wide-Area Image Geolocalization with Aerial Reference Imagery

no code implementations ICCV 2015 Scott Workman, Richard Souvenir, Nathan Jacobs

We propose to use deep convolutional neural networks to address the problem of cross-view image geolocalization, in which the geolocation of a ground-level query image is estimated by matching to georeferenced aerial images.

Cloud Motion as a Calibration Cue

no code implementations CVPR 2013 Nathan Jacobs, Mohammad T. Islam, Scott Workman

We propose cloud motion as a natural scene cue that enables geometric calibration of static outdoor cameras.

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