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

Accurate cloud recognition and warning are crucial for various applications,including in-flight support, weather forecasting, and climate research.However, recent deep learning algorithms have predominantly focused ondetecting cloud regions in satellite imagery, with insufficient attention tothe specificity required for accurate cloud recognition. This limitationinspired us to develop the novel FY-4A-Himawari-8 (FYH) dataset, which includesnine distinct cloud categories and uses precise domain adaptation methods toalign 70,419 image-label pairs in terms of projection, temporal resolution, andspatial resolution, thereby facilitating the training of supervised deeplearning networks. Given the complexity and diversity of cloud formations, wehave thoroughly analyzed the challenges inherent to cloud recognition tasks,examining the intricate characteristics and distribution of the data. Toeffectively address these challenges, we designed a Distribution-awareInteractive-Attention Network (DIAnet), which preserves pixel-level detailsthrough a high-resolution branch and a parallel multi-resolution cross-branch.We also integrated a distribution-aware loss (DAL) to mitigate the imbalanceacross cloud categories. An Interactive Attention Module (IAM) further enhancesthe robustness of feature extraction combined with spatial and channelinformation. Empirical evaluations on the FYH dataset demonstrate that ourmethod outperforms other cloud recognition networks, achieving superiorperformance in terms of mean Intersection over Union (mIoU). The code forimplementing DIAnet is available at https://github.com/icey-zhang/DIAnet.

Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image