Unsupervised multimodal change detection is pivotal for time-sensitive tasks and comprehensive multi-temporal Earth monitoring. In this study, we explore unsupervised multimodal change detection between two key remote sensing data sources: optical high-resolution imagery and OpenStreetMap (OSM) data. Specifically, we propose to utilize the vision foundation model Segmentation Anything Model (SAM), for addressing our task. Leveraging SAM's exceptional zero-shot transfer capability, high-quality segmentation maps of optical images can be obtained. Thus, we can directly compare these two heterogeneous data forms in the so-called segmentation domain. We then introduce two strategies for guiding SAM's segmentation process: the 'no-prompt' and 'box/mask prompt' methods. The two strategies are designed to detect land-cover changes in general scenarios and to identify new land-cover objects within existing backgrounds, respectively. Experimental results on three datasets indicate that the proposed approach can achieve more competitive results compared to representative unsupervised multimodal change detection methods.
Segment Anything for Satellite Imagery: A Strong Baseline and a Regional Dataset for Automatic Field Delineation
University of Modena and Reggio Emilia（摩德纳和雷吉奥艾米利亚大学）；ABACO Group SpA（ABACO集团）；Institute of Informatics and Telematics, National Research Council（信息与电信研究所，国家研究 council）
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）
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）
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）
Eye image segmentation using visual and concept prompts with Segment Anything Model 3 (SAM3)
Lund University Humanities Lab \& Dept. of Psychology, Lund University Lund Sweden；Lund University Humanities Lab Lund Sweden；Lund University Humanities Lab \& Dept. of Psychology, Lund University；Lund University Humanities Lab
Zero-shot Shark Tracking and Biometrics from Aerial Imagery
Stanford University（斯坦福大学）；Flinders University（弗林德斯大学）；Centro de Investigación en Ciencias del Mar y Limnología, Universidad de Costa Rica（哥斯达黎加大学海洋与湖沼科学研究中心）
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。
SAMAug: Point Prompt Augmentation for Segment Anything Model
School of Computing, University of Georgia（佐治亚大学计算机学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；Department of Computer Science and Engineering, Lehigh University（里海大学计算机科学与工程系）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Department of Computer Science and Engineering, University of Texas at Arlington（德克萨斯大学阿灵顿分校计算机科学与工程系）；Department of Radiation Oncology, Mayo Clinic（梅奥诊所放射肿瘤科）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
SAM3-UNet: Simplified Adaptation of Segment Anything Model 3
Sun Yat-sen University（中山大学）；Jiangxi Normal University（江西师范大学）；Hainan University（海南大学）；Dalian University of Technology（大连理工大学）；Chizhou University（池州学院）；Wuhan University（武汉大学）
Nanyang Technological University（南洋理工大学）；SenseTime Research（商汤科技研究院）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releas…
AI-SAM: Automatic and Interactive Segment Anything Model
The Pennsylvania State University（宾夕法尼亚州立大学）；Huck Institutes of the Life Sciences（哈克生命科学研究所）；Northwestern Memorial Hospital（西北纪念医院）；Northwestern University（西北大学）
