Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challenges
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Institute of Remote Sensing and Geographic Information System, Peking University（北京大学遥感与地理信息系统研究所）；School of Automation, Central South University（中南大学自动化学院）；Electronic Information School, Wuhan University（武汉大学电子信息学院）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯托克亥姆霍尔茨中心）；Lancaster Environment Centre, Lancaster University（兰卡斯特大学环境研究中心）；Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politécnica, University of Extremadura（埃斯特雷马杜拉大学技术计算机与通讯系超光谱计算实验室）
Liling Yang, Ning Chen, Jun Yue, Yidan Liu, Jiayi Ma, Pedram Ghamisi, Antonio Plaza, Leyuan Fang
Foundation models have transformed natural language processing and computer vision, and their impact is now reshaping remote sensing image analysis. With powerful generalization and transfer learning capabilities, they align naturally with the multimodal, multi-resolution, and multi-temporal characteristics of remote sensing data. To address unique challenges in the field, multimodal geospatial foundation models (GFMs) have emerged as a dedicated research frontier. This survey delivers a comprehensive review of multimodal GFMs from a modality-driven perspective, covering five core visual and vision-language modalities. We examine how differences in imaging physics and data representation shape interaction design, and we analyze key techniques for alignment, integration, and knowledge transfer to tackle modality heterogeneity, distribution shifts, and semantic gaps. Advances in training paradigms, architectures, and task-specific adaptation strategies are systematically assessed alongside a wealth of emerging benchmarks. Representative multimodal visual and vision-language GFMs are evaluated across ten downstream tasks, with insights into their architectures, performance, and application scenarios. Real-world case studies, spanning land cover mapping, agricultural monitoring, disaster response, climate studies, and geospatial intelligence, demonstrate the practical potential of GFMs. Finally, we outline pressing challenges in domain generalization, interpretability, efficiency, and privacy, and chart promising avenues for future research.
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）
Assessment of a new GeoAI foundation model for flood inundation mapping
School of Geographical Sciences and Urban Planning, Arizona State University（亚利桑那州立大学地理科学与城市规划学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；United States Geological Survey（美国地质调查局）
GeoViSTA: Geospatial Vision-Tabular Transformer for Multimodal Environment Representation
Department of Electrical and Computer Engineering, Rice University（理海大学电气与计算机工程系）；Center for Cardiovascular Computational and Precision Health, Department of Cardiology, DeBakey Heart and Vascular Center, Houston Methodist（休斯顿方法主义医疗中心心血管计算与精准健康中心、心内科部门、德贝基心脏和血管中心）
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）
Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events
Beijing University of Posts and Telecommunications（北京邮电大学）；Fuzhou University（福州大学）；Univ. Grenoble Alpes（格勒诺布尔大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔综合理工学院）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；German Aerospace Center（德国航空航天中心）
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
School of Geographical Sciences and Urban Planning（地理科学与城市规划学院）；Arizona State University（亚利桑那州立大学）；School of Computing and Augmented Intelligence（计算与增强智能学院）；NASA Marshall Space Flight Center（国家航空航天局马歇尔航天飞行中心）
CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation
Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Jiao Tong University（上海交通大学）；Beijing Institute of Technology（北京理工大学）；Wuhan University（武汉大学）；Zhongguancun Academy（中关村学院）；The University of Tokyo（东京大学）；Sun Yat-sen University（中山大学）；KU Leuven（比利时鲁汶大学）；KTH（皇家理工学院）
Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts
Technical University of Munich (TUM)（慕尼黑工业大学）；German Aerospace Center (DLR)（德国航空航天中心）；Massachusetts Institute of Technology (MIT)（麻省理工学院）；Taylor Geospatial（泰勒地理空间公司）
Towards Vision-Language Geo-Foundation Model: A Survey
School of GeoAI（地理人工智能学院）；Hindon STAI Institute（Hindon STAI研究所）；Key Laboratory of Geographic Information Science (Ministry of Education)（地理信息科学重点实验室）；East China Normal University（华东师范大学）；School of AI（人工智能学院）；Shanghai Jiao Tong University（上海交通大学）；School of Automation and Intelligent Sensing（自动化与智能感知学院）
Geospatial Foundation Models to Enable Progress on Sustainable Development Goals
Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯托克研究所）；University of Iceland（冰岛大学）；Wuhan University（武汉大学）；Wuhan University of Science and Technology（武汉科技大学）；Universitat de València（瓦伦西亚大学）
Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred to solve a wide variety of downstream supervised tasks. In this paper, we propose a self-supervised method for learning representations of geographic locations from unlabeled GPS trajectories to solve downstream geospatial computer vision tasks. Tiles resulting from a raster representation of the earth's surface are modeled as nodes on a graph or pixels of an image. GPS trajectories are modeled as allowed Markovian p…
PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
ESA Φ Φ \Phi roman_Φ -lab（欧洲航天局（ESA）Φ实验室）；Geomatics Section, Department of Civil Engineering, Faculty of Engineering Technology（工程科技学院土木工程系）；KTH - Royal Institute of Technology（皇家理工学院）；Conservatoire national des arts et métiers, CEDRIC（国家艺术与工艺学院，CEDRIC）；University of Glasgow（格拉斯哥大学）；Univ. Gustave Eiffel, ENSG, IGN, LASTIG（根特大学，IGN，LASTIG）
No One Knows the State of the Art in Geospatial Foundation Models
Taylor Geospatial（泰勒地理空间公司）；Technical University of Munich（慕尼黑技术大学）；Microsoft AI for Good Research Lab（微软AI for Good研究实验室）；Allen Institute for AI（艾伦人工智能研究所）；Vector Institute（向量研究所）；Carleton University（卡尔顿大学）；Clark University（克拉克大学）；University of British Columbia（不列颠哥伦比亚大学）；Arizona State University（亚利桑那州立大学）
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）
Leveraging AI multimodal geospatial foundation models for improved near-real-time flood mapping at a global scale
North Carolina State University（北卡罗来纳州立大学）；LabCorp（实验室集团）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Earth and Life Institute, UCLouvain（鲁汶天主教大学地球与生命研究所）；University of Helsinki（赫尔辛基大学）；Clark University（克拉克大学）；Thünen Institute of Farm Economics（蒂嫩农场经济研究所）
