This paper presents BigEarthNet that is a large-scale Sentinel-2 multispectral image dataset with a new class nomenclature to advance deep learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326 image patches annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its most thematic detailed Level-3 class nomenclature. Initial research demonstrates that some CLC classes are challenging to be accurately described by considering only Sentinel-2 images. To increase the effectiveness of BigEarthNet, in this paper we introduce an alternative class-nomenclature to allow DL models for better learning and describing the complex spatial and spectral information content of the Sentinel-2 images. This is achieved by interpreting and arranging the CLC Level-3 nomenclature based on the properties of Sentinel-2 images in a new nomenclature of 19 classes. Then, the new class-nomenclature of BigEarthNet is used within state-of-the-art DL models in the context of multi-label classification. Results show that the models trained from scratch on BigEarthNet outperform those pre-trained on ImageNet, especially in relation to some complex classes including agriculture, other vegetated and natural environments. All DL models are made publicly available at http://bigearth.net/#downloads, offering an important resource to guide future progress on RS image analysis.
BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval
Technische Universität Berlin（柏林工业大学）；Direção-Geral do Território (DGT)（领土总局）；NOVA Information Management School (NOVA IMS), Universidade Nova Lisboa（里斯本新大学NOVA信息管理学院）
FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models
Space Applications Centre, ISRO（印度空间研究组织空间应用中心）；Indian Institute of Science Education and Research Bhopal（印度科学教育与研究学院博帕尔分校）；Indian Institute of Technology Bombay（印度理工学院孟买分校）
FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学学院，柏林技术大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation
BIFOLD（柏林智能数据与机器学习研究所）；Technische Universität Berlin（柏林工业大学）；University of Trento（特伦托大学）；National Technical University（雅典国家技术大学）；National Observatory of Athens（雅典国家天文台）
Benchmarking and scaling of deep learning models for land cover image classification
Institute of Astronomy, Astrophysics, Space Applications & Remote Sensing, National Observatory of Athens（雅典国家天文台天文、天体物理、空间应用与遥感研究所）；Harokopio University of Athens（雅典哈罗科皮奥大学）；University of the Peloponnese（伯罗奔尼撒大学）
Data-Efficient ConvNeXt Pretraining for High-Resolution Sentinel-1 GRD SAR Representation Learning
Nanyang Technological University（南洋理工大学）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Earth Observatory of Singapore, Nanyang Technological University（南洋理工大学地球观测中心）；Asian School of the Environment, Nanyang Technological University（南洋理工大学亚洲环境学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电子与电气工程学院）
AlignJEPA: Predictive Vision-Language Alignment for Remote Sensing Foundation Models
Space Applications Centre, ISRO（印度空间研究组织空间应用中心）；Indian Institute of Science Education and Research (IISER) Bhopal（印度科学教育与研究学院（博帕尔））；Indian Institute of Technology Bombay（印度理工学院孟买分校）
On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing
BIFOLD - Berlin Institute for the Foundations of Learning and Data（BIFOLD - 柏林学习与数据基础研究所）；Technische Universität Berlin（柏林工业大学）；National Technical University of Athens（雅典国家技术大学）；National Observatory of Athens（雅典国家天文台）；Harokopio University of Athens（哈罗科皮奥雅典大学）
SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting
Mediterranean Agronomic Institute of Montpellier - CIHEAM-IAMM（地中海农业研究院-CIHEAM-IAMM）；Inria（法国国家信息与自动化技术研究院）；INRAE（法国国家农业研究咨询中心）；Cirad（国际热带农业研究中心）；UMR TETIS（TETIS联合研究单位）；Univ. of Montpellier（蒙彼利埃大学）
The color out of space: learning self-supervised representations for Earth Observation imagery
University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）；Istituto Zooprofilattico Sperimentale dell’Abruzzo e del Molise ‘G.Caporale’（阿布鲁佐与莫利塞实验动物卫生研究所“G.卡波拉莱”）；Progressive Systems Srl（渐进系统有限公司）
Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks
National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing (SBIIP), Beijing Institute of Technology（北京理工大学空间智能信息处理国家重点实验室）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences（中国科学院地理空间信息处理与应用系统技术重点实验室）；Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology（北京理工大学前沿交叉科学研究院）；School of Mechatronical Engineering, Beijing Institute of Technology（北京理工大学机电学院）；School of Earth and Space Sciences, Peking University（北京大学地球与空间科学学院）；School of Electronics, Peking University（北京大学电子学院）；School of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing（华中科技大学计算机科学与技术学院&湖北省智能地理信息处理重点实验室）
