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

The scientific outcomes of the 2022 Landslide4Sense (L4S) competitionorganized by the Institute of Advanced Research in Artificial Intelligence(IARAI) are presented here. The objective of the competition is toautomatically detect landslides based on large-scale multiple sources ofsatellite imagery collected globally. The 2022 L4S aims to fosterinterdisciplinary research on recent developments in deep learning (DL) modelsfor the semantic segmentation task using satellite imagery. In the past fewyears, DL-based models have achieved performance that meets expectations onimage interpretation, due to the development of convolutional neural networks(CNNs). The main objective of this article is to present the details and thebest-performing algorithms featured in this competition. The winning solutionsare elaborated with state-of-the-art models like the Swin Transformer,SegFormer, and U-Net. Advanced machine learning techniques and strategies suchas hard example mining, self-training, and mix-up data augmentation are alsoconsidered. Moreover, we describe the L4S benchmark data set in order tofacilitate further comparisons, and report the results of the accuracyassessment online. The data is accessible on \textit{Future DevelopmentLeaderboard} for future evaluation at\url{https://www.iarai.ac.at/landslide4sense/challenge/}, and researchers areinvited to submit more prediction results, evaluate the accuracy of theirmethods, compare them with those of other users, and, ideally, improve thelandslide detection results reported in this article.

The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery