MMLSv2: A Multimodal Dataset for Martian Landslide Detection in Remote Sensing Imagery
Fairfield University（费尔菲尔德大学）；Michigan Technological University（密歇根技术大学）；Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）；ESPOL Polytechnic University（ESPOL多科大学）；University of Kerala（喀拉拉大学）；University of Mississippi（密西西比大学）
Sidike Paheding, Abel Reyes-Angulo, Leo Thomas Ramos, Angel D. Sappa, Rajaneesh A., Hiral P. B., Sajin Kumar K. S., Thomas Oommen
We present MMLSv2, a dataset for landslide segmentation on Martian surfaces. MMLSv2 consists of multimodal imagery with seven bands: RGB, digital elevation model, slope, thermal inertia, and grayscale channels. MMLSv2 comprises 664 images distributed across training, validation, and test splits. In addition, an isolated test set of 276 images from a geographically disjoint region from the base dataset is released to evaluate spatial generalization. Experiments conducted with multiple segmentation models show that the dataset supports stable training and achieves competitive performance, while still posing challenges in fragmented, elongated, and small-scale landslide regions. Evaluation on the isolated test set leads to a noticeable performance drop, indicating increased difficulty and highlighting its value for assessing model robustness and generalization beyond standard in-distribution settings. Dataset will be available at: https://github.com/MAIN-Lab/MMLS_v2
Global-Local Contextual Progressive Expansion Network for Martian Landslide Segmentation in Multimodal Remote Sensing Imagery
Computer Vision Center, Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机视觉中心）；Fairfield University（费尔菲尔德大学）；Michigan Technological University（密歇根理工大学）；University of Kerala（喀拉拉大学）；ESPOL Polytechnic University（ESPOL理工大学）；University of Mississippi（密西西比大学）
MCTED: A Machine-Learning-Ready Dataset for Digital Elevation Model Generation From Mars Imagery
European Space Agency (ESA)（欧洲航天局）；European Space Astronomy Centre (ESAC)（欧洲空间天文中心）；UK Research and Innovation - Innovate UK（英国研究与创新-创新英国）；UK Space Agency (UKSA)（英国空间局）
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
Institute of Advanced Research in Artificial Intelligence (IARAI)（高级人工智能研究所（IARAI））；Wuhan University（武汉大学）；Xidian University（西安电子科技大学）；Technical University of Munich（慕尼黑工业大学）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Chengdu University of Technology（成都理工大学）
这里介绍了由人工智能高级研究所（IARAI）组织的2022年Landslide 4Sense（L4 S）竞赛的科学成果。比赛的目的是根据全球收集的大规模多来源卫星图像自动检测滑坡。2022 L4 S旨在促进对深度学习（DL）模型最新发展的跨学科研究，用于使用卫星图像的语义分割任务。在过去的几年中，由于卷积神经网络（CNN）的发展，基于DL的模型已经实现了满足图像判读期望的性能。本文的主要目的是介绍本次比赛的细节和最佳性能算法。获奖的解决方案采用了最先进的模型，如Swin Transformer、SegFormer和U-Net。还考虑了高级机器学习技术和策略，如硬示例挖掘，自训练和混合数据增强。此外，我们描述了L4 S基准数据集，以方便进一步的比较，并报告的准确性评估结果在线。这些数据可以在\textit{Future Development Leaderboard}上访问，以便将来在\url{https://www.iarai.ac.at/landslide4sense/challenge/}进行评估，研究人员被邀请提交更多的预测结果，评估他们的方法的准确性，将其与其他用户的方法进行比较，并在理想情况下，改进本文中报告的滑坡检测结果。
MARTIAN: A Rendering Framework for Aerial Mars Imagery from HiRISE Orbital Data
Space Robotics Research Group, SnT, University of Luxembourg（卢森堡大学空间机器人研究组）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）
EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models
University of Macau（澳门大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Xi’an University of Science and Technology（西安科技大学）
A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image
University of technology of Troyes（特鲁瓦技术大学）；ICC Dong Duong（东杜ong研究所）；Biosel AI（Biosel人工智能公司）；University of Technology Sydney（悉尼技术大学）；Austrian Institute of Technology（奥地利技术研究所）；Ho Chi Minh City University of Technology (HCMUT)（胡志明市技术大学）
