Global semantic 3D understanding from single-view high-resolution remote sensing (RS) imagery is crucial for Earth Observation (EO). However, this task faces significant challenges due to the high costs of annotations and data collection, as well as geographically restricted data availability. To address these challenges, synthetic data offer a promising solution by being easily accessible and thus enabling the provision of large and diverse datasets. We develop a specialized synthetic data generation pipeline for EO and introduce SynRS3D, the largest synthetic RS 3D dataset. SynRS3D comprises 69,667 high-resolution optical images that cover six different city styles worldwide and feature eight land cover types, precise height information, and building change masks. To further enhance its utility, we develop a novel multi-task unsupervised domain adaptation (UDA) method, RS3DAda, coupled with our synthetic dataset, which facilitates the RS-specific transition from synthetic to real scenarios for land cover mapping and height estimation tasks, ultimately enabling global monocular 3D semantic understanding based on synthetic data. Extensive experiments on various real-world datasets demonstrate the adaptability and effectiveness of our synthetic dataset and proposed RS3DAda method. SynRS3D and related codes will be available.
Urban feature analysis from aerial remote sensing imagery using self-supervised and semi-supervised computer vision
The University of Melbourne（墨尔本大学）；Royal Netherlands Meteorological Institute (KNMI)（荷兰皇家气象研究所）；University of Melbourne（墨尔本大学）；Environment Protection Authority Victoria（维多利亚州环境保护局）
The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University（信息工程测绘遥感国家重点实验室（LIESMARS），武汉大学）；Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember（工程技术学院测绘工程系，印度尼西亚技术学院十月份十号学院）；Department of Informatics and Information Technology, College of Natural and Applied Sciences (CoNAS), Sokoine University of Agriculture（信息与信息技术系，索科因农业学院自然与应用科学学院）；Geomatics Department, Tshwane University of Technology（测绘系，塔什韦恩技术大学）；Department of Geography, Environmental Management and Energy Studies, Faculty of Science, University of Johannesburg（地理、环境管理与能源研究系，科学学院，约翰内斯堡大学）
TSE-Net: Semi-supervised Monocular Height Estimation from Single Remote Sensing Images
Chair of Data Science in Earth Observation, Technical University of Munich (TUM)（地球观测数据科学教授职位，慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Fudan University（复旦大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing Remote Sensing Information Institute（北京遥感信息研究所）；Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院摄影测量与遥感研究所）；Xiamen University（厦门大学）
CloudMamba: An Uncertainty-Guided Dual-Scale Mamba Network for Cloud Detection in Remote Sensing Imagery
Beihang University（北京航空航天大学）；Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education（教育部航天器设计优化与动态模拟技术重点实验室）
A Remote Approach to Cashew Orchard Detection: Leveraging Active Learning with Satellite Imagery in Guinea-Bissau
University of Porto（波尔图大学）；INESC-TEC（INESC-TEC机构）；University of Lisbon（里斯本大学）；CIBIO（CIBIO机构）；BIOPOLIS（BIOPOLIS机构）；InBIO Associated Laboratory（InBIO联合实验室）；CMUP（CMUP机构）
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）
Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery
Fraunhofer IOSB（弗劳恩霍夫光电、系统技术及图像处理研究所）；Institute of Photogrammetry and Remote Sensing, KIT（卡尔斯鲁厄理工学院摄影测量与遥感研究所）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）
IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所（IMF））；Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM)（慕尼黑工业大学地球观测信号处理研究所（SiPEO））
FaSS-MVS -- Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-borne Monocular Imagery
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB)（弗劳恩霍夫光学技术、系统技术与图像利用研究所）；Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
Universiti Malaya（马来大学）；Kunming University of Science and Technology（昆明理工大学）；Yunnan Normal University（云南师范大学）；Guangming Laboratory（光明实验室）；University of Central Florida（佛罗里达大学）
Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
New York University（纽约大学）；University of the Philippines Diliman（菲律宾大学迪里曼分校）；University of Washington（华盛顿大学）；German Corporation for International Cooperation GmbH (GIZ)（德国国际合作机构（GIZ））
Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
Beijing Institute of Technology（北京理工大学）；German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立高等物理、数学与信息学院）；University of Iceland（冰岛大学）
UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations
SatMamba: Development of Foundation Models for Remote Sensing Imagery Using State Space Models
University of Engineering and Technology, Vietnam National University（越南国家大学工程技术大学）；International Digital Earth Applied Science Research Center, Chubu University（中部大学国际数字地球应用科学研究中心）
