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

Satellite-based remote sensing is instrumental in the monitoring andmitigation of the effects of anthropogenic climate change. Large scale, highresolution data derived from these sensors can be used to inform interventionand policy decision making, but the timeliness and accuracy of theseinterventions is limited by use of optical data, which cannot operate at nightand is affected by adverse weather conditions. Synthetic Aperture Radar (SAR)offers a robust alternative to optical data, but its associated complexitieslimit the scope of labelled data generation for traditional deep learning. Inthis work, we apply a self-supervised pretraining scheme, masked autoencoding,to SAR amplitude data covering 8.7\% of the Earth's land surface area, and tunethe pretrained weights on two downstream tasks crucial to monitoring climatechange - vegetation cover prediction and land cover classification. We showthat the use of this pretraining scheme reduces labelling requirements for thedownstream tasks by more than an order of magnitude, and that this pretraininggeneralises geographically, with the performance gain increasing when tuneddownstream on regions outside the pretraining set. Our findings significantlyadvance climate change mitigation by facilitating the development of task andregion-specific SAR models, allowing local communities and organizations todeploy tailored solutions for rapid, accurate monitoring of climate changeeffects.

Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data