Landslides are one of the most critical and destructive geohazards. Widespread development of human activities and settlements combined with the effects of climate change on weather are resulting in a high increase in the frequency and destructive power of landslides, making them a major threat to human life and the economy. In this paper, we explore methodologies to map newly-occurred landslides using Sentinel-2 imagery automatically. All approaches presented are framed as a bi-temporal change detection problem, requiring only a pair of Sentinel-2 images, taken respectively before and after a landslide-triggering event. Furthermore, we introduce a novel deep learning architecture for fusing Sentinel-2 bi-temporal image pairs with Digital Elevation Model (DEM) data, showcasing its promising performances w.r.t. other change detection models in the literature. As a parallel task, we address limitations in existing datasets by creating a novel geodatabase, which includes manually validated open-access landslide inventories over heterogeneous ecoregions of the world. We release both code and dataset with an open-source license.
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。
Automated Linear Disturbance Mapping via Semantic Segmentation of Sentinel-2 Imagery
University of Winnipeg（温尼伯大学）；Hatfield Consultants Group（哈特菲尔德咨询集团）；University of Manitoba（曼尼托巴大学）；Pachama（帕查玛公司）；Environment and Climate Change Canada（加拿大环境与气候变化部）
Deep learning-based object detection of offshore platforms on Sentinel-1 Imagery and the impact of synthetic training data
German Remote Sensing Data Center, Earth Observation Center, EOC of the German Aerospace Center, DLR（德国遥感数据中心，地球观测中心，德国航空航天中心（DLR）东部观测中心）；Institute for Geography and Geology, Department of Remote Sensing, University of Würzburg（地理与地质研究所，遥感系，乌尔姆大学）
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan
Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection
Institute of Advanced Research in Artificial Intelligence（人工智能高级研究院）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology（亥姆霍兹弗赖贝格资源技术研究所）
Orion Lab, National Observatory of Athens & National Technical University of Athens（Orion实验室、雅典国家天文观测站及雅典技术大学）；Department of Informatics and Telematics, Harokopio University of Athens（信息与电信系、哈罗基奥大学）
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）
CloudBreaker: Breaking the Cloud Covers of Sentinel-2 Images using Multi-Stage Trained Conditional Flow Matching on Sentinel-1
Computer Science and Engineering（计算机科学与工程）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Institute of Water and Flood Management（水与洪水管理研究所）
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）
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
Technical University of Munich（慕尼黑工业大学）；EUMETSAT European Organisation for the Exploitation of Meteorological Satellites（欧洲气象卫星应用组织）；Munich University of Applied Sciences（慕尼黑应用技术大学）
Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学（LUMS））
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
University of California Berkeley（加州大学伯克利分校）；The Ohio State University（俄亥俄州立大学）；German Aerospace Center DLR（德国航空航天中心）；University of Valencia（瓦伦西亚大学）；National Observatory of Athens（雅典国家天文台）；United Nations Satellite Centre（联合国卫星中心）；University of Oxford（牛津大学）；NVIDIA（英伟达公司）；Universidad de Antioquia（安蒂奥基亚大学）
Vehicle speed dataset for the major European road network derived from Sentinel-2 imagery, 2022-2026
Heidelberg Institute for Geoinformation Technology (HeiGIT)（海德堡地理信息技术研究所）；Faculty of Geographical Sciences, University of Lodz（罗兹大学地理科学学院）；GIScience Research Group, Institute of Geography, University of Heidelberg（海德堡大学地理研究所地理信息科学研究组）
Flow matching for Sentinel-2 super-resolution: implementation, application, and implications
Department of Agricultural and Biological Engineering, Mississippi State University（农业与生物工程系，密苏里州立大学）；North Mississippi Research and Extension Center, Mississippi State University（北密苏里研究与推广中心，密苏里州立大学）
