{"total":71,"rows":[{"id":"2504.11171","title":"TerraMind: Large-Scale Generative Multimodality for Earth Observation","created":"2025-04-15","cited_by_count":104,"area":null},{"id":"2406.11519","title":"HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model","created":"2024-06-17","cited_by_count":73,"area":null},{"id":"2412.04204","title":"PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models","created":"2024-12-05","cited_by_count":68,"area":null},{"id":"2503.11849","title":"Towards a Unified Copernicus Foundation Model for Earth Vision","created":"2025-03-14","cited_by_count":53,"area":null},{"id":"2506.20380","title":"TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis","created":"2025-06-25","cited_by_count":39,"area":null},{"id":"2503.10845","title":"Panopticon: Advancing Any-Sensor Foundation Models for Earth Observation","created":"2025-03-13","cited_by_count":34,"area":null},{"id":"2503.22081","title":"A Survey on Remote Sensing Foundation Models: From Vision to Multimodality","created":"2025-03-28","cited_by_count":19,"area":null},{"id":"2504.11172","title":"TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data","created":"2025-04-15","cited_by_count":14,"area":null},{"id":"2412.14428","title":"WildSAT: Learning Satellite Image Representations from Wildlife Observations","created":"2024-12-19","cited_by_count":13,"area":null},{"id":"2412.05600","title":"Global and Dense Embeddings of Earth: Major TOM Floating in the Latent Space","created":"2024-12-07","cited_by_count":12,"area":null},{"id":"2603.00988","title":"Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality","created":"2026-03-01","cited_by_count":12,"area":null},{"id":"2605.12678","title":"No One Knows the State of the Art in Geospatial Foundation Models","created":"2026-05-12","cited_by_count":12,"area":null},{"id":"2503.16683","title":"GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations","created":"2025-03-20","cited_by_count":10,"area":null},{"id":"2505.24528","title":"Geospatial Foundation Models to Enable Progress on Sustainable Development Goals","created":"2025-05-30","cited_by_count":10,"area":null},{"id":"2511.02101","title":"Measuring the Intrinsic Dimension of Earth Representations","created":"2025-11-03","cited_by_count":10,"area":null},{"id":"2503.00168","title":"SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, Updated","created":"2025-02-28","cited_by_count":9,"area":null},{"id":"2506.19585","title":"SMARTIES: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images","created":"2025-06-24","cited_by_count":9,"area":null},{"id":"2507.09081","title":"From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion","created":"2025-07-11","cited_by_count":8,"area":null},{"id":"2509.26016","title":"GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data","created":"2025-09-30","cited_by_count":8,"area":null},{"id":"2601.16011","title":"THOR: A Versatile Foundation Model for Earth Observation Climate and Society Applications","created":"2026-01-22","cited_by_count":7,"area":null},{"id":"2602.06285","title":"MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Training","created":"2026-02-06","cited_by_count":7,"area":null},{"id":"2503.19331","title":"ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning","created":"2025-03-25","cited_by_count":6,"area":null},{"id":"2501.12535","title":"How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?","created":"2025-01-21","cited_by_count":5,"area":null},{"id":"2503.22516","title":"Ice-FMBench: A Foundation Model Benchmark for Sea Ice Type Segmentation","created":"2025-03-28","cited_by_count":5,"area":null},{"id":"2504.18256","title":"SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology","created":"2025-04-25","cited_by_count":5,"area":null},{"id":"2505.14951","title":"MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks","created":"2025-05-20","cited_by_count":5,"area":null},{"id":"2507.13385","title":"Using Multiple Input Modalities Can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery","created":"2025-07-15","cited_by_count":5,"area":null},{"id":"2511.02923","title":"Cropland Mapping using Geospatial Embeddings","created":"2025-11-04","cited_by_count":5,"area":null},{"id":"2601.13134","title":"Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access","created":"2026-01-19","cited_by_count":5,"area":null},{"id":"2504.16942","title":"S2Vec: Self-Supervised Geospatial Embeddings for the Built Environment","created":"2025-04-10","cited_by_count":4,"area":null},{"id":"2504.17177","title":"A Genealogy of Foundation Models in Remote Sensing","created":"2025-04-24","cited_by_count":4,"area":null},{"id":"2511.02831","title":"GeoCrossBench: Cross-Band Generalization for Remote Sensing","created":"2025-11-04","cited_by_count":4,"area":null},{"id":"2512.05025","title":"RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation","created":"2025-12-04","cited_by_count":4,"area":null},{"id":"2505.05644","title":"The Moon's Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction","created":"2025-05-08","cited_by_count":3,"area":null},{"id":"2507.07527","title":"MAPEX: Modality-Aware Pruning of Experts for Remote Sensing Foundation Models","created":"2025-07-10","cited_by_count":3,"area":null},{"id":"2510.22964","title":"Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challenges","created":"2025-10-27","cited_by_count":3,"area":null},{"id":"2407.19660","title":"Towards Knowledge Guided Pretraining Approaches for Multimodal Foundation Models: Applications in Remote Sensing","created":"2024-07-29","cited_by_count":2,"area":null},{"id":"2508.10894","title":"MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data","created":"2025-08-14","cited_by_count":2,"area":null},{"id":"2510.19579","title":"Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration","created":"2025-10-22","cited_by_count":2,"area":null},{"id":"2604.10591","title":"GeoMeld: Toward Semantically Grounded Foundation Models for Remote Sensing","created":"2026-04-12","cited_by_count":2,"area":null},{"id":"2605.21075","title":"SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining","created":"2026-05-20","cited_by_count":2,"area":null},{"id":"2606.20523","title":"SARLO-80: Worldwide Slant SAR Language Optic Dataset 80cm","created":"2026-06-18","cited_by_count":2,"area":null},{"id":"2607.03644","title":"Moonstone: A Multimodal Foundation Model and Benchmark for Lunar Remote Sensing","created":"2026-07-03","cited_by_count":2,"area":null},{"id":"2504.03181","title":"MIMRS: A Survey on Masked Image Modeling in Remote Sensing","created":"2025-04-04","cited_by_count":1,"area":null},{"id":"2508.21437","title":"Trees as Gaussians: Large-Scale Individual Tree Mapping","created":"2025-08-29","cited_by_count":1,"area":null},{"id":"2509.19087","title":"Zero-Shot Multi-Spectral Learning: Reimagining a Generalist Multimodal Gemini 2.5 Model for Remote Sensing Applications","created":"2025-09-23","cited_by_count":1,"area":null},{"id":"2510.10068","title":"Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning","created":"2025-10-11","cited_by_count":1,"area":null},{"id":"2510.14493","title":"Grazing Detection using Deep Learning and Sentinel-2 Time Series Data","created":"2025-10-16","cited_by_count":1,"area":null},{"id":"2511.17442","title":"REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent","created":"2025-11-21","cited_by_count":1,"area":null},{"id":"2602.12652","title":"CBEN -- A Multimodal Machine Learning Dataset for Cloud Robust Remote Sensing Image Understanding","created":"2026-02-13","cited_by_count":1,"area":null},{"id":"2603.01576","title":"Cryo-Bench: Benchmarking Foundation Models for Cryosphere Mapping","created":"2026-03-02","cited_by_count":1,"area":null},{"id":"2604.18881","title":"A Proxy Consistency Loss for Grounded Fusion of Earth Observation and Location Encoders","created":"2026-04-20","cited_by_count":1,"area":null},{"id":"2604.21104","title":"Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance","created":"2026-04-22","cited_by_count":1,"area":null},{"id":"2503.19719","title":"On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?","created":"2025-03-25","cited_by_count":0,"area":null},{"id":"2506.06852","title":"Position Prediction Self-Supervised Learning for Multimodal Satellite Imagery Semantic Segmentation","created":"2025-06-07","cited_by_count":0,"area":null},{"id":"2506.17302","title":"Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning","created":"2025-06-17","cited_by_count":0,"area":null},{"id":"2512.17492","title":"MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2601.12964","title":"Cross-Scale Pretraining: Enhancing Self-Supervised Learning for Low-Resolution Satellite Imagery for Semantic Segmentation","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2604.11415","title":"Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2604.21032","title":"Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.12542","title":"Earth Science Foundation Models: From Perception to Reasoning and Discovery","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2605.31068","title":"HQ-JEPA: Hybrid Quantum Joint-Embedding Predictive Architecture for Cross-Modal Remote Sensing Representation Learning","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2607.07758","title":"Scalable and Trustworthy Earth Observation Foundation Models","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2607.18504","title":"Now We Know? A Systematic Comparison of TerraMind and THOR","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2608.03410","title":"Earth Embeddings","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2608.09276","title":"Verifiably grounded machine interpretation of lunar geology","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2608.15282","title":"Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference","created":"2026-08-15","cited_by_count":0,"area":null},{"id":"2608.15456","title":"AlignJEPA: Predictive Vision-Language Alignment for Remote Sensing Foundation Models","created":"2026-08-16","cited_by_count":0,"area":null},{"id":"2608.30392","title":"Foundation Models Meet Agriculture: Challenges Beyond Pretraining","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.32756","title":"Reuse or Relearn? A Spectral View of Earth Observation Foundation Models","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.05790","title":"FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models","created":"2026-10-05","cited_by_count":0,"area":null}],"next_cursor":null}