Predicting Good Features for Image Geo-Localization Using Per-Bundle VLAD

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CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps (2018, background): “While global features have the benefit to retrieve diverse natural scene images based on ambient information, local image features yield higher precision in retrieving structured objects such as buildings and are thus more frequently used [8,9,10,11,12,13,14,15,16].”
PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, background): “…focused on covering restricted subsets of the problem, like landmark build-1 ings [1, 37, 54], cities where street view imagery is available [7, 26, 52], or places where coverage of internet photos is dense enough to allow building a structure-from-motion reconstruction that a query photo can…”
Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification (2018, methodology): “These mainly apply retrieval techniques to match a query image against a reference dataset [3,12,18,20,29,34,46].”
Learned Contextual Feature Reweighting for Image Geo-Localization (2017, methodology): “Visual image geo-localization has been an active research area for the past decade [2, 28, 48, 53], owing to its wide range of applications including augmented reality [36], autonomous driving [33], adding and refining geo-tags in image collections [19, 65], large-scale 3D reconstruction [11], and p”