Handling Urban Location Recognition as a 2D Homothetic Problem

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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): “Most local feature based approaches therefore focus on localization within cities, either based on photos from photo sharing websites [5, 36] or street view [2, 7, 26, 27, 43, 51, 52].”
Image Retrieval for Image-Based Localization Revisited (2012, background): “Further information, such as building outlines or 3D geometry, can be combined with the image database to improve localization approaches [2, 4].”
Visual landmark recognition from Internet photo collections: A large-scale evaluation (2014, methodology): “related to our problem, our goal diers from pose estimation, because camera pose does not necessarily determine what object the camera is really seeing (See Sec.3.3for more details.) The SanFrancisco[22] and Landmarks1K [23] datasets are 3 Figure 1: The architecture of a landmark recognition system.”