Cross-View Image Geolocalization

By Tsung-Yi Lin, Serge J. Belongie, James Hays. 2013

309 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps (2018, background): “Cross-view geolocation recognition makes additional use of satellite or aerial imagery to determine query locations [37,38,39,40].”
Revisiting IM2GPS in the Deep Learning Era (2017, background): “Many approaches make used of aerial imagery for localization [3, 27, 17].”
PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, background): “Exceptions are Im2GPS [18, 17] and [32, 33, 49], which make additional use of satellite imagery.”
Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization (2020, background): “Some pioneering approaches [16], [17], [18], [19] focus on extracting hand-crafted features.”
Predicting Ground-Level Scene Layout from Aerial Imagery (2016, background): “Recent work on cross-view image geolocalization [17, 18, 32, 33] has shown that convolutional neural networks are capable of extracting features from aerial imagery that can be matched to features extracted from ground imagery.”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Lin et al. (2013) match ground-level queries to other ground-level reference photos as in traditional geolocalization, but then use the overhead appearance and land cover attributes of those ground-level matches to build slidingwindow classifiers in the aerial and land cover domain.”
Localizing and Orienting Street Views Using Overhead Imagery (2016, methodology): “Because of this widespread coverage, matching ground-level photos to overhead imagery has become an attractive geolocalization approach [19].”
Wide-Area Image Geolocalization with Aerial Reference Imagery (2015, background): “We propose a cross-view training strategy that uses deep convolutional neural networks to extract features from aerial imagery.”
Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification (2018, background): “For these reasons, the majority of approaches simplifies photo geolocalization by restricting the problem to urban photos of, for example, well-known landmarks and cities [3,25,34,43,45,48] or natural areas like deserts or mountains [5,33,38].”
Cross-View Image Matching for Geo-Localization in Urban Environments (2017, background): “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 geo-localization [1, 10, 2, 25, 11, 13, 22].”
VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval (2020, background): “[10] consider both satellite images and land cover attributes for cross-view geo-localization.”
CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization (2018, methodology): “As a result, matching ground view photos to aerial imagery gradually becomes an increasingly popular geo-localization approach [5, 22, 37, 23, 49, 50, 46, 41, 53, 43].”
Lending Orientation to Neural Networks for Cross-View Geo-Localization (2019, methodology): “Traditional hand-crafted features were used for cross-view localization [16, 6, 23].”
Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization (2019, methodology): “Several methods [3, 10, 13] warp ground images into bird-view images and then match the warped images to the aerial ones.”