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WATCH: Wide-Area Terrestrial Change Hypercube
Connor Greenwell;Jon Crall;Matthew Purri;Kristin Dana;Nathan Jacobs;Armin Hadzic;Scott Workman;Matt Leotta
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
Monitoring Earth activity using data collected from multiple satellite imaging platforms in a unified way is a significant challenge, especially with large variability in image resolution, spectral bands, and revisit rates. Further, the availability of sensor data varies across time as new platforms are launched. In this work, we introduce an adaptable framework and network architecture capable of...Show More
Handling Image and Label Resolution Mismatch in Remote Sensing
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
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 mismatch between overhead imagery and ground-truth label sources, due to differences in ground sample distance. To illustrate this problem, we introduce a new dataset and use it to showcase weaknesses inherent in existing...Show More
Revisiting Near/Remote Sensing with Geospatial Attention
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (10)
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 modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitl...Show More
Content-Aware Detection of Temporal Metadata Manipulation
Rafael Padilha;Tawfiq Salem;Scott Workman;Fernanda A. Andaló;Anderson Rocha;Nathan Jacobs
Most pictures shared online are accompanied by temporal metadata (i.e., the day and time they were taken), which makes it possible to associate an image content with real-world events. Maliciously manipulating this metadata can convey a distorted version of reality. In this work, we present the emerging problem of detecting timestamp manipulation. We propose an end-to-end approach to verify whethe...Show More
A Structure-Aware Method for Direct Pose Estimation
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2022 | Conference Paper |
Cited by: Papers (4)
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 as direct and indirect. Direct methods, such as PoseNet, regress pose from the image as a fixed function, for example using a feed-forward convolutional network. Such methods are desirable because they are deterministic ...Show More
Augmenting Depth Estimation with Geospatial Context
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
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 assumption that the camera is geocalibrated, a problem we refer to as geo-enabled depth estimation. Our key insight is that if capture location is known, the corresponding overhead viewpoint offers a valuable resource for...Show More
Single Image Cloud Detection via Multi-Image Fusion
Scott Workman;M. Usman Rafique;Hunter Blanton;Connor Greenwell;Nathan Jacobs
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Artifacts in imagery captured by remote sensing, such as clouds, snow, and shadows, present challenges for various tasks, including semantic segmentation and object detection. A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data. In this work, we explore how recent advances in multi-image fusion can be leveraged to bootstrap ...Show More
Learning a Dynamic Map of Visual Appearance
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (20)
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 relationship, many of which are associated with precise time and location metadata. We propose to use these images to construct a global-scale, dynamic map of visual appearance attributes. Such a map enables fine-grained under...Show More
Dynamic Traffic Modeling From Overhead Imagery
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (12)
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 traditional approach for solving this problem would be to model the speed of each road segment as a function of time. However, this strategy is limited in that a significant amount of data must first be collected before ...Show More
Extending Absolute Pose Regression to Multiple Scenes
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (19)
Direct pose regression using deep convolutional neural networks has become a highly active research area. However, even with significant improvements in performance in recent years, the best performance comes from training distinct, scene-specific networks. We propose a novel architecture, Multi-Scene PoseNet (MSPN), that allows for a single network to be used on an arbitrary number of scenes with...Show More
RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery
Armin Hadzic;Hunter Blanton;Weilian Song;Mei Chen;Scott Workman;Nathan Jacobs
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
Roadway free-flow speed captures the typical vehicle speed in low traffic conditions. Modeling free-flow speed is an important problem in transportation engineering with applications to a variety of design, operation, planning, and policy decisions of highway systems. Unfortunately, collecting large-scale historical traffic speed data is expensive and time consuming. Traditional approaches for est...Show More
Implicit Land Use Mapping Using Social Media Imagery
2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Land use classification is a central remote sensing task with a broad range of applications. Typically this is represented as a supervised learning problem, the first step of which is to develop a taxonomy of discrete labels. However, such categories are restricted in the range of uses they can convey and arbitrary decisions are often required when defining the categories. Instead, we argue that t...Show More
What Goes Where: Predicting Object Distributions from Above
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Year: 2018 | Conference Paper |
Cited by: Papers (8)
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. The outcome is a convolutional neural network for overhead imagery that is capable of predicting the type and count of objects that are likely to be seen from a ground-level perspective. We demonstrate our approach o...Show More
A Multimodal Approach to Mapping Soundscapes
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Year: 2018 | Conference Paper |
Cited by: Papers (15)
We explore the problem of mapping soundscapes, that is, predicting the types of sounds that are likely to be heard at a given geographic location. Using a novel dataset, which includes geo-tagged audio and overhead imagery, we develop an approach for constructing an aural atlas, which captures the geospatial distribution of soundscapes. We build on previous work relating sound to ground-level imag...Show More
FARSA: Fully Automated Roadway Safety Assessment
Weilian Song;Scott Workman;Armin Hadzic;Xu Zhang;Eric Green;Mei Chen;Reginald Souleyrette;Nathan Jacobs
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2018 | Conference Paper |
Cited by: Papers (10)
This paper addresses the task of road safety assessment. 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). Obtaining these ratings requires manual, fine-grained labeling of roadway features in streetlevel panoramas, a slow and costly process. We propose to au...Show More
A Unified Model for Near and Remote Sensing
2017 IEEE International Conference on Computer Vision (ICCV)
Year: 2017 | Conference Paper |
Cited by: Papers (33)
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-toend trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output...Show More
Understanding and Mapping Natural Beauty
2017 IEEE International Conference on Computer Vision (ICCV)
Year: 2017 | Conference Paper |
Cited by: Papers (27)
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. Using a dataset containing hundreds of thousands of outdoor images captured throughout Great Britain with crowdsourced ratings of natural beauty, we propose an approach to predict scenicness which expli...Show More
Predicting Ground-Level Scene Layout from Aerial Imagery
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2017 | Conference Paper |
Cited by: Papers (149)
We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semantic features automatically extracted from co-located ground imagery. Our network architecture takes an aerial image as input, extracts features using a convolutional neural network, and then applies an adapti...Show More
Cross-view convolutional networks
2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
Year: 2016 | Conference Paper |
Billions of geotagged ground-level images are available via social networks and Google Street View. Recent work in computer vision has explored how these images could serve as a resource for understanding our world. However, most ground-level images are captured in cities and around famous landmarks; there are still very large geographic regions with few images. This leads to artifacts when estima...Show More
Detecting Vanishing Points Using Global Image Context in a Non-ManhattanWorld
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2016 | Conference Paper |
We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidate vanishing points, then remove outliers by enforcing mutual orthogonality. Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains....Show More
Camera geo-calibration using an MCMC approach
2016 IEEE International Conference on Image Processing (ICIP)
Year: 2016 | Conference Paper |
We address the problem of single-image geo-calibration, in which an estimate of the geographic location, viewing direction and field of view is sought for the camera that captured an image. The dominant approach to this problem is to match features of the query image, using color and texture, against a reference database of nearby ground imagery. However, this fails when such imagery is not availa...Show More
Sky segmentation in the wild: An empirical study
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2016 | Conference Paper |
Cited by: Papers (21)
Automatically determining which pixels in an image view the sky, the problem of sky segmentation, is a critical preprocessing step for a wide variety of outdoor image interpretation problems, including horizon estimation, robot navigation and image geolocalization. Many methods for this problem have been proposed with recent work achieving significant improvements on benchmark datasets. However, s...Show More
A fast method for estimating transient scene attributes
Ryan Baltenberger;Menghua Zhai;Connor Greenwell;Scott Workman;Nathan Jacobs
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2016 | Conference Paper |
Cited by: Papers (5)
We propose the use of deep convolutional neural networks to estimate the transient attributes of a scene from a single image. Transient scene attributes describe both the objective conditions, such as the weather, time of day, and the season, and subjective properties of a scene, such as whether or not the scene seems busy. Recently, convolutional neural networks have been used to achieve state-of...Show More
Analyzing human appearance as a cue for dating images
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2016 | Conference Paper |
Cited by: Papers (12)
Given an image, we propose to use the appearance of people in the scene to estimate when the picture was taken. There are a wide variety of cues that can be used to address this problem. Most previous work has focused on low-level image features, such as color and vignetting. Recent work on image dating has used more semantic cues, such as the appearance of automobiles and buildings. We extend thi...Show More
Wide-Area Image Geolocalization with Aerial Reference Imagery
2015 IEEE International Conference on Computer Vision (ICCV)
Year: 2015 | Conference Paper |
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. We use state-of-the-art feature representations for ground-level images and introduce a cross-view training approach for learning a joint semantic feature representation for a...Show More
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