This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitly considers the geospatial relationship between the pixels in a ground-level image and a geographic location. We propose an approach for computing geospatial attention that incorporates geometric features and the appearance of the overhead and ground-level imagery. We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks. The results demonstrate that our method significantly outperforms the previous state-of-the-art methods.
School of Electronic Information, Wuhan University of Science and Technology（武汉科技大学电子信息学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Oriental Space Port Research Institute（东方航天港研究院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
A Sensor Agnostic Domain Generalization Framework for Leveraging Geospatial Foundation Models: Enhancing Semantic Segmentation viaSynergistic Pseudo-Labeling and Generative Learning
University of Houston（休斯敦大学）；Purdue University（普渡大学）
CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院寒区旱区环境与工程研究所冻土科学与冻土工程重点实验室）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；School of Computer Science, Wuhan University（武汉大学计算机科学学院）；Zhongguancun Academy（中关村学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；School of Engineering Science, University of Science and Technology of China（中国科学技术大学工程科学学院）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）
Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
Beijing Institute of Technology（北京理工大学）；German Aerospace Center（德国航空航天中心）；Technical University of Munich（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（格勒诺布尔国立理工学院）；University of Iceland（冰岛大学）；Nanjing University of Science and Technology（南京理工大学）
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）
GeoViS: Geospatially Rewarded Visual Search for Remote Sensing Visual Grounding
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；National University of Defense Technology（国防科技大学）；Shanghai Jiao Tong University（上海交通大学）；Chongqing University（重庆大学）
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
Nanyang Technological University（南洋理工大学）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics（上海财经大学经济与计算交叉研究重点实验室）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（新加坡资讯与通讯研究院）
SIGMAE: A Spectral-Index-Guided Foundation Model for Multispectral Remote Sensing
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Artificial Intelligence and Automation, Wuhan University of Science and Technology（武汉科技大学人工智能与自动化学院）；School of Electronic Information, Wuhan University of Science and Technology（武汉科技大学电子信息学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Perturbation-Regularized Open-Vocabulary Remote Sensing Segmentation with Unified Multi-Domain Evaluation
Department of Electronic Engineering and Information Science, University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能学院，光学与电子学系（iOPEN），西北工业大学）；Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China（人工智能研究院（TeleAI），中国电信，中华人民共和国）
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）
GeoSeg-OV: Bridging Geospatial Gaps with Structural Guidance for Open-Vocabulary Remote Sensing Segmentation
School of Computer Science, China University of Geosciences（中国地质大学计算机学院）；Systems Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）系统中心）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机系）；School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；Data Science and AI Innovation Research Promotion Center, Shiga University（滋贺大学数据科学与人工智能创新研究促进中心）
Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation
School of Electronics and Information Engineering, Harbin Institute of Technology（电子信息工程学院，哈尔滨工业大学）；Faculty of Computing, Harbin Institute of Technology（计算机学院，哈尔滨工业大学）；Peng Cheng Laboratory, Shenzhen（鹏城实验室，深圳）
GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data
School of Earth and Space Sciences, Peking University（地球与空间科学学院，北京大学）；College of Urban and Environmental Sciences, Peking University（城市与环境科学学院，北京大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation, CAS（多模态人工智能系统国家重点实验室，中国科学院自动化研究所）；Intelligent Maintenance and Operations Systems Lab（智能维护与运营系统实验室）
