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.
翻译:由人工智能高级研究所组织的2022年Landliide4Sense(L4S)竞赛的科学成果在此介绍,该竞赛的目的是根据全球收集的卫星图象的大规模多种来源自动检测滑坡;2022年L4S的目的是促进关于利用卫星图像进行语义分解任务的深层次学习(DL)模型的最新发展情况的跨学科研究;过去几年,基于DL的模型由于发展了进化神经网络,达到了对图像判读的预期效果,从而满足了对图像判读的预期;本文章的主要目的是介绍这次竞赛中的细节和最佳算法;胜出的解决办法是用Swin变异器、SegFormer和U-Net等最先进的模型来拟订的;还考虑了高级机器学习技术和战略,如硬实例采矿、自我培训和混合数据增强等;此外,我们描述了L4S基准数据集,以便于进一步进行比较,并在网上报告精确度评估的结果;4 与LARCurealem/Developalendal 4的用户一起,可以查阅这些数据。