  The scientific outcomes of the 2022 Landslide4Sense (L4S) competition
organized by the Institute of Advanced Research in Artificial Intelligence
(IARAI) are presented here. The objective of the competition is to
automatically detect landslides based on large-scale multiple sources of
satellite imagery collected globally. The 2022 L4S aims to foster
interdisciplinary research on recent developments in deep learning (DL) models
for the semantic segmentation task using satellite imagery. In the past few
years, DL-based models have achieved performance that meets expectations on
image interpretation, due to the development of convolutional neural networks
(CNNs). The main objective of this article is to present the details and the
best-performing algorithms featured in this competition. The winning solutions
are elaborated with state-of-the-art models like the Swin Transformer,
SegFormer, and U-Net. Advanced machine learning techniques and strategies such
as hard example mining, self-training, and mix-up data augmentation are also
considered. Moreover, we describe the L4S benchmark data set in order to
facilitate further comparisons, and report the results of the accuracy
assessment online. The data is accessible on \textit{Future Development
Leaderboard} for future evaluation at
\url{https://www.iarai.ac.at/landslide4sense/challenge/}, and researchers are
invited to submit more prediction results, evaluate the accuracy of their
methods, compare them with those of other users, and, ideally, improve the
landslide detection results reported in this article.
