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

Semantic segmentation from aerial views is a crucial task for autonomousdrones, as they rely on precise and accurate segmentation to navigate safelyand efficiently. However, aerial images present unique challenges such asdiverse viewpoints, extreme scale variations, and high scene complexity. Inthis paper, we propose an end-to-end multi-class semantic segmentationdiffusion model that addresses these challenges. We introduce recursivedenoising to allow information to propagate through the denoising process, aswell as a hierarchical multi-scale approach that complements the diffusionprocess. Our method achieves competitive results on the UAVid dataset andstate-of-the-art performance on the Vaihingen Building segmentation benchmark.Being the first iteration of this method, it shows great promise for futureimprovements.

Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion