English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose to use deep convolutional neural networks to address the problemof cross-view image geolocalization, in which the geolocation of a ground-levelquery image is estimated by matching to georeferenced aerial images. We usestate-of-the-art feature representations for ground-level images and introducea cross-view training approach for learning a joint semantic featurerepresentation for aerial images. We also propose a network architecture thatfuses features extracted from aerial images at multiple spatial scales. Tosupport training these networks, we introduce a massive database that containspairs of aerial and ground-level images from across the United States. Ourmethods significantly out-perform the state of the art on two benchmarkdatasets. We also show, qualitatively, that the proposed featurerepresentations are discriminative at both local and continental spatialscales.

Wide-Area Image Geolocalization with Aerial Reference Imagery