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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Automatic change detection and disaster damage assessment are currentlyprocedures requiring a huge amount of labor and manual work by satelliteimagery analysts. In the occurrences of natural disasters, timely changedetection can save lives. In this work, we report findings on problem framing,data processing and training procedures which are specifically helpful for thetask of building damage assessment using the newly released xBD dataset. Ourinsights lead to substantial improvement over the xBD baseline models, and wescore among top results on the xView2 challenge leaderboard. We release ourcode used for the competition.

Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion