Geo-localization using Volumetric Representations of Overhead Imagery

Ozcanli, Ozge C.; Dong, Yi; Mundy, Joseph L.

doi:10.1007/s11263-015-0850-9

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Matching street viewpoints with aerial imagery has been a challenging computer vision problem [23,20,25,34]. Recent approaches include geometry-based methods and deep learning.…”

Section: Cross-view Matchingmentioning

confidence: 99%

Cross-View Policy Learning for Street Navigation

Mirowski

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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The ability to navigate from visual observations in unfamiliar environments is a core component of intelligent agents and an ongoing challenge for Deep Reinforcement Learning (RL). Street View can be a sensible testbed for such RL agents, because it provides real-world photographic imagery at ground level, with diverse street appearances; it has been made into an interactive environment called StreetLearn [27] and used for research on navigation. However, goal-driven street navigation agents have not so far been able to transfer to unseen areas without extensive retraining, and relying on simulation is not a scalable solution. Since aerial images are easily and globally accessible, we propose instead to train a multi-modal policy on ground and aerial views, then transfer the ground view policy to unseen (target) parts of the city by utilizing aerial view observations. Our core idea is to pair the ground view with an aerial view and to learn a joint policy that is transferable across views. We achieve this by learning a similar embedding space for both views, distilling the policy across views and dropping out visual modalities. We further reformulate the transfer learning paradigm into three stages: 1) cross-modal training, when the agent is initially trained on multiple city regions, 2) aerial view-only adaptation to a new area, when the agent is adapted to a held-out region using only the easily obtainable aerial view, and 3) ground view-only transfer, when the agent is tested on navigation tasks on unseen ground views, without aerial imagery. Experimental results suggest that the proposed cross-view policy learning enables better generalization of the agent and allows for more effective transfer to unseen environments.

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“…Matching street viewpoints with aerial imagery has been a challenging computer vision problem [23,20,25,34]. Recent approaches include geometry-based methods and deep learning.…”

Section: Cross-view Matchingmentioning

confidence: 99%

Cross-View Policy Learning for Street Navigation

Mirowski

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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“…Although ground-level image-to-image matching approaches have achieved promising results, however, due to the fact that only small number of cities in the world are covered by ground-level imagery, it has not been feasible to scale up this approach to global level. On the other hand, a more complete coverage for overhead reference data such as satellite/areial imagery and digital elevation model (DEM) has spurred a growing interest in cross-view geolocalization [1,10,2,25,11,13,22].…”

Section: Cross-view Geo-localizationmentioning

confidence: 99%

Cross-View Image Matching for Geo-Localization in Urban Environments

Tian

Chen

Shah

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

178

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In this paper, we address the problem of cross-view image geo-localization. Specifically, we aim to estimate the GPS location of a query street view image by finding the matching images in a reference database of geotagged bird's eye view images, or vice versa. To this end, we present a new framework for cross-view image geolocalization by taking advantage of the tremendous success of deep convolutional neural networks (CNNs) in image classification and object detection. First, we employ the Faster R-CNN [16] to detect buildings in the query and reference images. Next, for each building in the query image, we retrieve the k nearest neighbors from the reference buildings using a Siamese network trained on both positive matching image pairs and negative pairs. To find the correct NN for each query building, we develop an efficient multiple nearest neighbors matching method based on dominant sets. We evaluate the proposed framework on a new dataset that consists of pairs of street view and bird's eye view images. Experimental results show that the proposed method achieves better geo-localization accuracy than other approaches and is able to generalize to images at unseen locations.

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