IM2GPS

IM2GPS: estimating geographic information from a single image. Estimating geographic information from an image is an excellent, difficult high-level computer vision problem whose time has come. The emergence of vast amounts of geographically-calibrated image data is a great reason for computer vision to start looking globally — on the scale of the entire planet! In this paper, we propose a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach. For this task, we will leverage a dataset of over 6 million GPS-tagged images from the Internet. We represent the estimated image location as a probability distribution over the Earth’s surface. We quantitatively evaluate our approach in several geolocation tasks and demonstrate encouraging performance (up to 30 times better than chance). We show that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.


References in zbMATH (referenced in 5 articles )

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  1. Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017) arXiv
  2. Zhang, Luming; Song, Mingli; Liu, Xiao; Sun, Li; Chen, Chun; Bu, Jiajun: Recognizing architecture styles by hierarchical sparse coding of blocklets (2014) ioport
  3. Van Laere, Olivier; Schockaert, Steven; Dhoedt, Bart: Georeferencing Flickr resources based on textual meta-data (2013) ioport
  4. Lalonde, Jean-François; Efros, Alexei A.; Narasimhan, Srinivasa G.: Estimating the natural illumination conditions from a single outdoor image (2012) ioport
  5. Yang, Lin; Johnstone, John; Zhang, Chengcui: Ranking canonical views for tourist attractions (2010) ioport