MergeSAM: Unsupervised change detection of remote sensing images based on the Segment Anything Model
School of Geography and Planning, Sun Yat-sen University（中山大学地理科学与规划学院）；Unit 33269 of the PLA（中国人民解放军33269部队）；Intelligent Science & Technology Academy Limited of CASIC（中国航天科工集团智能科技研究院有限公司）
Recently, large foundation models trained on vast datasets have demonstrated exceptional capabilities in feature extraction and general feature representation. The ongoing advancements in deep learning-driven large models have shown great promise in accelerating unsupervised change detection methods, thereby enhancing the practical applicability of change detection technologies. Building on this progress, this paper introduces MergeSAM, an innovative unsupervised change detection method for high-resolution remote sensing imagery, based on the Segment Anything Model (SAM). Two novel strategies, MaskMatching and MaskSplitting, are designed to address real-world complexities such as object splitting, merging, and other intricate changes. The proposed method fully leverages SAM's object segmentation capabilities to construct multitemporal masks that capture complex changes, embedding the spatial structure of land cover into the change detection process.
TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Wuhan University（武汉大学）；Dalian University of Technology（大连理工大学）；University of Chinese Academy of Sciences（中国科学院大学）
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot
University of Western São Paulo (UNOESTE)（圣保罗西部大学）；University of Tennessee（田纳西大学）；Federal University of Mato Grosso do Sul (UFMS)（南马托格罗索联邦大学）；São Paulo State University (UNESP)（圣保罗州立大学）；University of Waterloo（滑铁卢大学）
本文探索了 Meta AI 的 Segment Anything Model (SAM) 在遥感图像分割中的应用，通过结合文本提示与单样本训练的自动化技术提升了多尺度数据上的分割精度，并开源代码以推动 SAM 在遥感领域的进一步适配。
Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Model
School of Mathematical Sciences, Beijing Normal University（北京师范大学数学科学学院）；State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University（北京师范大学地理科学学部遥感科学国家重点实验室）
Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
Information Engineering University（信息工程大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；ETH Zurich（苏黎世联邦理工学院）；University of Trento（特伦托大学）
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
Wuhan University（武汉大学）；The University of Sydney（悉尼大学）；National Engineering Research Center of Speech and Language Information Processing（国家语音及语言信息处理工程技术研究中心）
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
School of Information Science and Technology, Southwest Jiaotong University（西南交通大学信息科学与技术学院）；I2R, A*STAR（新加坡科研局信息研究院）；South China University of Technology（华南理工大学）
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）
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
KOKONI, Moxin (Huzhou) Tech. Co., LTD（莫西（湖州）科技有限公司KOKONI）；Beihang University（北京航空航天大学）；Zhejiang University（浙江大学）；Huzhou University（湖州师范学院）；Singapore University of Technology and Design（新加坡科技设计大学）
A Unified Framework with Multimodal Fine-tuning for Remote Sensing Semantic Segmentation
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）科学与工程学院）；School of Information Science and Engineering, Wuhan University of Science and Technology（武汉科技大学信息科学与工程学院）；Department of Geography, The University of Hong Kong（香港大学地理系）
SAM2-ELNet: Label Enhancement and Automatic Annotation for Remote Sensing Segmentation
College of Instrumentation and Electrical Engineering, Jilin University（吉林大学仪器电气工程学院）；Key Laboratory of Geophysical Exploration Equipment, Ministry of Education（教育部地球物理探测装备重点实验室）；School of Mathematics, Jilin University（吉林大学数学学院）
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Wuhan University of Science and Technology（武汉科技大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Hong Kong（香港大学）
SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；School of Information and Communications Engineering, Xi’an Jiaotong University（西安交通大学信息与通信工程学院）；Ministry of Education Key Lab of Intelligent Network Security, Xi’an, China（教育部智能网络安全重点实验室）；Pazhou Laboratory (Huangpu), Radarweg 29, Guangzhou, China（琶洲实验室（黄埔），雷达路29号，广州，中国）
Duke University（杜克大学）；Duke Kunshan University（昆山杜克大学）；Nicholas Institute for Energy, Environment & Sustainability, Duke University（杜克大学尼古拉斯能源、环境与可持续发展研究所）；University of Montana（蒙大拿大学）
PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology（南京信息工程大学遥感与地理信息工程学院）；Guangxi Water & Power Design Institute CO., Ltd.（广西水电设计院有限公司）
HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of Iceland（冰岛大学）；Griffith University（格里菲斯大学）
FG-TreeSeg: Flow-Guided Tree Crown Segmentation without Instance Annotations
Department of Geography, University of South Carolina（南卡罗来纳大学地理系）；Department of Geography, Virginia Polytechnic Institute and State University（弗吉尼亚理工大学地理系）
DescribeEarth: Describe Anything for Remote Sensing Images
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；College of Artificial Intelligence, Xi’an Jiaotong University（西安交通大学人工智能学院）；School of Computer Science and Technology and Ministry of Education Key Lab For Intelligent Networks and Network Security, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院和教育部智能网络与网络安全重点实验室）；School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University（西安交通大学数学与统计学院和教育部智能网络与网络安全重点实验室）；Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China（琶洲实验室（黄埔），广州，广东，中国）
RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation
College of Information Science and Technology, Nanjing Forestry University（南京林业大学信息科学技术学院）；Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics（南京航空航天大学深圳研究院）
ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；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）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Target Cognition and Application Technology(TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences（目标认知与应用技术重点实验室）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）
Tsinghua University（清华大学）；China University of Geosciences, Wuhan（武汉地质大学）；State Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）
SAM-Based Building Change Detection with Distribution-Aware Fourier Adaptation and Edge-Constrained Warping
School of Information Science and Technology, Southwest Jiaotong University（西南交通大学信息科学与技术学院）；School of Computer Science, China University of Geosciences（中国地质大学计算机学院）；University of Extremadura（埃斯特雷马杜拉大学）
Remote SAMsing: From Segment Anything to Segment Everything
Department of Electrical Engineering, University of Bras\' i lia, Bras\' i lia, Brazil；Department of Geography, University of Bras\' i lia, Bras\' i lia, Brazil
UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction
School of Mathematics and Statistics, Southeast University（东南大学数学与统计学院）；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）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；Image Processing Laboratory (IPL), Universitat de València（瓦伦西亚大学图像处理实验室）；Data Science in Earth Observation, Technical University of Munich（慕尼黑工业大学地球观测数据科学研究所）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔理工学院、约瑟夫·傅里叶实验室）
SAM-Aug: Leveraging SAM Priors for Few-Shot Parcel Segmentation in Satellite Time Series
Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University（轻工业先进过程控制重点实验室，教育部，江南大学）；Academy of Biology and Biotechnology, Southern Federal University（生物与生物技术学院，南方联邦大学）；State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）
SegAssess: Panoramic quality mapping for robust and transferable unsupervised segmentation assessment
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感与信息工程学院）；College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）；Hubei Luojia Laboratory（湖北珞珈实验室）
