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

Remote sensing change detection, identifying changes between scenes of thesame location, is an active area of research with a broad range ofapplications. Recent advances in multimodal self-supervised pretraining haveresulted in state-of-the-art methods which surpass vision models trained solelyon optical imagery. In the remote sensing field, there is a wealth ofoverlapping 2D and 3D modalities which can be exploited to superviserepresentation learning in vision models. In this paper we propose ContrastiveSurface-Image Pretraining (CSIP) for joint learning using optical RGB and aboveground level (AGL) map pairs. We then evaluate these pretrained models onseveral building segmentation and change detection datasets to show that ourmethod does, in fact, extract features relevant to downstream applicationswhere natural and artificial surface information is relevant.

Supervising Remote Sensing Change Detection Models with 3D Surface Semantics