Predicting Geo-informative Attributes in Large-Scale Image Collections Using Convolutional Neural Networks

By Stefan Lee, Haipeng Zhang, David J. Crandall. 2015

42 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Yi Yang
2. James Hays
3. Nam N. Vo
4. Tobias Weyand
5. Luc Van Gool
6. Dacheng Tao
7. Nathan Jacobs
8. Ming-Hsuan Yang

In Media Technology

1. Nam N. Vo
2. Tobias Weyand
3. James Hays
4. Nathan Jacobs
5. James Philbin
6. Ilya Kostrikov
7. Scott Workman
8. Mubarak Shah

Citations in the corpus

• Revisiting IM2GPS in the Deep Learning Era (2017, methodology): “Deep learning has been applied successfully to location prediction [36] and other tasks related to our problem: predicting scene type [40], perceptual attributes [6] such as safety, liveliness and geo-informative attributes [15] like GDP, elevation.”
• PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, methodology): “Instead of performing geolocation, [28] train a CNN on a large collection of geotagged Flickr photos to predict geographical attributes like “population“, “elevation“ or “household income“.”
• Wide-Area Image Geolocalization with Aerial Reference Imagery (2015, background): “Lee et al. [22] estimate geo-informative attributes from an image using convolutional neural network classifiers.”