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

Forest plays a vital role in reducing greenhouse gas emissions and mitigatingclimate change besides maintaining the world's biodiversity. The existingsatellite-based forest monitoring system utilizes supervised learningapproaches that are limited to a particular region and depend on manuallyannotated data to identify forest. This work envisages forest identification asa few-shot semantic segmentation task to achieve generalization acrossdifferent geographical regions. The proposed few-shot segmentation approachincorporates a texture attention module in the prototypical network tohighlight the texture features of the forest. Indeed, the forest exhibits acharacteristic texture different from other classes, such as road, water, etc.In this work, the proposed approach is trained for identifying tropical forestsof South Asia and adapted to determine the temperate forest of Central Europewith the help of a few (one image for 1-shot) manually annotated support imagesof the temperate forest. An IoU of 0.62 for forest class (1-way 1-shot) wasobtained using the proposed method, which is significantly higher (0.46 forPANet) than the existing few-shot semantic segmentation approach. This resultdemonstrates that the proposed approach can generalize across geographicalregions for forest identification, creating an opportunity to develop a globalforest cover identification tool.

Texture based Prototypical Network for Few-Shot Semantic Segmentation of Forest Cover: Generalizing for Different Geographical Regions