{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cross-view-image-matching-for-geo","title":"Cross-View Image Matching for Geo-localization in Urban Environments","arxiv_id":"1703.07815","date":"2017-03-22","proceeding":"CVPR 2017 7","authors":["Yicong Tian","Chen Chen","Mubarak Shah"],"abstract":"In this paper, we address the problem of cross-view image geo-localization.\nSpecifically, we aim to estimate the GPS location of a query street view image\nby finding the matching images in a reference database of geo-tagged bird's eye\nview images, or vice versa. To this end, we present a new framework for\ncross-view image geo-localization by taking advantage of the tremendous success\nof deep convolutional neural networks (CNNs) in image classification and object\ndetection. First, we employ the Faster R-CNN to detect buildings in the query\nand reference images. Next, for each building in the query image, we retrieve\nthe $k$ nearest neighbors from the reference buildings using a Siamese network\ntrained on both positive matching image pairs and negative pairs. To find the\ncorrect NN for each query building, we develop an efficient multiple nearest\nneighbors matching method based on dominant sets. We evaluate the proposed\nframework on a new dataset that consists of pairs of street view and bird's eye\nview images. Experimental results show that the proposed method achieves better\ngeo-localization accuracy than other approaches and is able to generalize to\nimages at unseen locations.","url_abs":"http://arxiv.org/abs/1703.07815v1","url_pdf":"http://arxiv.org/pdf/1703.07815v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cross-view-image-matching-for-geo","repo_url":"https://github.com/viibridges/crossnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"cross-view-image-to-image-translation","task_name":"Cross-View Image-to-Image Translation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"geo-localization","task_name":"geo-localization"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.07815","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}