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

This paper introduces a new method of generating realistic pervasive changesin the context of evaluating the effectiveness of change detection algorithmsin controlled settings. The method, a cycle-consistent adversarial network(CycleGAN), requires low quantities of training data to generate realisticchanges. Here we show an application of CycleGAN in creating realisticsnow-covered scenes of multispectral Sentinel-2 imagery, and demonstrate howthese images can be used as a test bed for anomalous change detectionalgorithms.

Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagery