{"total":130,"rows":[{"id":"2412.14123","title":"AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities","created":"2024-12-18","cited_by_count":74,"area":null},{"id":"2502.09356","title":"Galileo: Learning Global & Local Features of Many Remote Sensing Modalities","created":"2025-02-13","cited_by_count":43,"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":"2503.20563","title":"TerraTorch: The Geospatial Foundation Models Toolkit","created":"2025-03-26","cited_by_count":25,"area":null},{"id":"2506.06281","title":"TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation","created":"2025-06-06","cited_by_count":25,"area":null},{"id":"2511.13655","title":"OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation","created":"2025-11-17","cited_by_count":24,"area":null},{"id":"2511.15658","title":"GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI","created":"2025-11-19","cited_by_count":21,"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":"2603.00988","title":"Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality","created":"2026-03-01","cited_by_count":12,"area":null},{"id":"2504.04319","title":"Geo-OLM: Enabling Sustainable Earth Observation Studies with Cost-Efficient Open Language Models & State-Driven Workflows","created":"2025-04-06","cited_by_count":11,"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":"2504.17397","title":"Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models","created":"2025-04-24","cited_by_count":9,"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":"2504.06962","title":"Efficient Self-Supervised Learning for Earth Observation via Dynamic Dataset Curation","created":"2025-04-09","cited_by_count":7,"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":"2508.14112","title":"Surya: Foundation Model for Heliophysics","created":"2025-08-18","cited_by_count":6,"area":null},{"id":"2503.11906","title":"A Survey on SAR ship classification using Deep Learning","created":"2025-03-14","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":"2505.08529","title":"ExEBench: Benchmarking Foundation Models on Extreme Earth Events","created":"2025-05-13","cited_by_count":5,"area":null},{"id":"2505.08723","title":"TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series","created":"2025-05-13","cited_by_count":5,"area":null},{"id":"2603.27101","title":"PRUE: A Practical Recipe for Field Boundary Segmentation at Scale","created":"2026-03-28","cited_by_count":5,"area":null},{"id":"2606.23503","title":"UniverSat: Resolution- and Modality-Agnostic Transformers for Earth Observation","created":"2026-06-22","cited_by_count":5,"area":null},{"id":"2504.07394","title":"ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method","created":"2025-04-10","cited_by_count":4,"area":null},{"id":"2507.06806","title":"GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction","created":"2025-07-09","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":"2601.11183","title":"Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring","created":"2026-01-16","cited_by_count":4,"area":null},{"id":"2602.23678","title":"Any Model, Any Place, Any Time: Get Remote Sensing Foundation Model Embeddings On Demand","created":"2026-02-27","cited_by_count":4,"area":null},{"id":"2511.10370","title":"SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation","created":"2025-11-13","cited_by_count":3,"area":null},{"id":"2601.01558","title":"Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings","created":"2026-01-04","cited_by_count":3,"area":null},{"id":"2604.03456","title":"Earth Embeddings Reveal Diverse Urban Signals from Space","created":"2026-04-03","cited_by_count":3,"area":null},{"id":"2506.03938","title":"FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review","created":"2025-06-04","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":"2509.21273","title":"A Sentinel-3 foundation model for ocean colour","created":"2025-09-25","cited_by_count":2,"area":null},{"id":"2510.13714","title":"DeDelayed: Deleting Remote Inference Delay via On-Device Correction","created":"2025-10-15","cited_by_count":2,"area":null},{"id":"2602.00117","title":"IC-EO: Interpretable Code-based assistant for Earth Observation","created":"2026-01-27","cited_by_count":2,"area":null},{"id":"2603.12762","title":"TerraFlow: Multimodal, Multitemporal Representation Learning for Earth Observation","created":"2026-03-13","cited_by_count":2,"area":null},{"id":"2603.19873","title":"SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation","created":"2026-03-20","cited_by_count":2,"area":null},{"id":"2603.23408","title":"GeoSANE: Learning Geospatial Representations from Models, Not Data","created":"2026-03-24","cited_by_count":2,"area":null},{"id":"2604.04306","title":"HighFM: Towards a Foundation Model for Learning Representations from High-Frequency Earth Observation Data","created":"2026-04-05","cited_by_count":2,"area":null},{"id":"2604.07092","title":"Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data","created":"2026-04-08","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":"2604.23166","title":"A satellite foundation model for improved wealth monitoring","created":"2026-04-25","cited_by_count":2,"area":null},{"id":"2605.20804","title":"OlmoEarth v1.2: A more efficient family of OlmoEarth models","created":"2026-05-20","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":"2606.24120","title":"Flood Mapping from RGB imagery using a Vision Foundation Model","created":"2026-06-23","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":"2506.11314","title":"HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation","created":"2025-06-12","cited_by_count":1,"area":null},{"id":"2508.06057","title":"AGI for the Earth, the path, possibilities and how to evaluate intelligence of models that work with Earth Observation Data?","created":"2025-08-08","cited_by_count":1,"area":null},{"id":"2508.18829","title":"Assessing the Effectiveness of Deep Embeddings for Tree Species Classification in the Dutch Forest Inventory","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2509.25268","title":"A Weather Foundation Model for the Power Grid","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2510.05617","title":"InstaGeo: Compute-Efficient Geospatial Machine Learning from Data to Deployment","created":"2025-10-07","cited_by_count":1,"area":null},{"id":"2510.26609","title":"FARM: Fine-Tuning Geospatial Foundation Models for Intra-Field Crop Yield Regression","created":"2025-10-30","cited_by_count":1,"area":null},{"id":"2511.01990","title":"Assessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-users","created":"2025-11-03","cited_by_count":1,"area":null},{"id":"2511.04474","title":"Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability","created":"2025-11-06","cited_by_count":1,"area":null},{"id":"2511.05461","title":"The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2","created":"2025-11-07","cited_by_count":1,"area":null},{"id":"2512.01181","title":"First On-Orbit Demonstration of a Geospatial Foundation Model","created":"2025-12-01","cited_by_count":1,"area":null},{"id":"2512.23903","title":"Scaling Remote Sensing Foundation Models: Data Domain Tradeoffs at the Peta-Scale","created":"2025-12-29","cited_by_count":1,"area":null},{"id":"2601.02315","title":"Prithvi-Complimentary Adaptive Fusion Encoder (CAFE): unlocking full-potential for flood inundation mapping","created":"2026-01-05","cited_by_count":1,"area":null},{"id":"2602.19608","title":"Satellite-Based Detection of Looted Archaeological Sites Using Machine Learning","created":"2026-02-23","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":"2604.16841","title":"When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution","created":"2026-04-18","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":"2605.03175","title":"DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery","created":"2026-05-04","cited_by_count":1,"area":null},{"id":"2605.13743","title":"GHGbench: A Unified Multi-Entity, Multi-Task Benchmark for Carbon Emission Prediction","created":"2026-05-13","cited_by_count":1,"area":null},{"id":"2606.02374","title":"Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models","created":"2026-06-01","cited_by_count":1,"area":null},{"id":"2606.14081","title":"Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection","created":"2026-06-12","cited_by_count":1,"area":null},{"id":"2608.04792","title":"Multi-Modal, Multi-Temporal Geospatial Foundation Models Improve Global Above-Ground Biomass Estimation","created":"2026-08-05","cited_by_count":1,"area":null},{"id":"2608.12663","title":"Evaluating AlphaEarth Foundations Embeddings for Wildfire Susceptibility Mapping","created":"2026-08-12","cited_by_count":1,"area":null},{"id":"2608.16614","title":"Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts","created":"2026-08-17","cited_by_count":1,"area":null},{"id":"2608.21041","title":"CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment","created":"2026-08-21","cited_by_count":1,"area":null},{"id":"2608.27971","title":"GAAT: Geometry-Aware Alignment Transformer for Multimodal UAV Perception","created":"2026-08-28","cited_by_count":1,"area":null},{"id":"2501.10144","title":"A Vision-Language Framework for Multispectral Scene Representation Using Language-Grounded Features","created":"2025-01-17","cited_by_count":0,"area":null},{"id":"2505.18179","title":"GAIA: A Foundation Model for Operational Atmospheric Dynamics","created":"2025-05-15","cited_by_count":0,"area":null},{"id":"2506.15565","title":"Baltimore Atlas: FreqWeaver Adapter for Semi-supervised Ultra-high Spatial Resolution Land Cover Classification","created":"2025-06-18","cited_by_count":0,"area":null},{"id":"2510.10799","title":"Rethinking deep learning: linear regression remains a key benchmark in predicting terrestrial water storage","created":"2025-10-12","cited_by_count":0,"area":null},{"id":"2510.11576","title":"Benchmarking foundation models for hyperspectral image classification: Application to cereal crop type mapping","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2510.12670","title":"TerraCodec: Compressing Optical Earth Observation Data","created":"2025-10-14","cited_by_count":0,"area":null},{"id":"2510.21813","title":"SITS-DECO: A Generative Decoder Is All You Need For Multitask Satellite Image Time Series Modelling","created":"2025-10-21","cited_by_count":0,"area":null},{"id":"2511.00073","title":"Habitat and Land Cover Change Detection in Alpine Protected Areas: A Comparison of AI Architectures","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2601.07392","title":"OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation","created":"2026-01-12","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":"2601.13751","title":"Towards Onboard Continuous Change Detection for Floods","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2601.22841","title":"How Much of a Model Do We Need? Redundancy and Slimmability in Remote Sensing Foundation Models","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.10720","title":"Ecological mapping with geospatial foundation models","created":"2026-02-11","cited_by_count":0,"area":null},{"id":"2602.18765","title":"A high-resolution nationwide urban village mapping product for 342 Chinese cities based on foundation models","created":"2026-02-21","cited_by_count":0,"area":null},{"id":"2603.07246","title":"LEPA: Learning Geometric Equivariance in Satellite Remote Sensing Data with a Predictive Architecture","created":"2026-03-07","cited_by_count":0,"area":null},{"id":"2604.03289","title":"Toward Artificial Intelligence Enabled Earth System Coupling","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2604.03874","title":"Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance","created":"2026-04-04","cited_by_count":0,"area":null},{"id":"2604.05068","title":"Towards Scaling Law Analysis For Spatiotemporal Weather Data","created":"2026-04-06","cited_by_count":0,"area":null},{"id":"2604.16663","title":"A Benchmark Study of Segmentation Models and Adaptation Strategies for Landslide Detection from Satellite Imagery","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2604.26675","title":"Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification","created":"2026-04-29","cited_by_count":0,"area":null},{"id":"2605.08160","title":"WATCH: Wide-Area Archaeological Site Tracking for Change Detection","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2605.14406","title":"GeoViSTA: Geospatial Vision-Tabular Transformer for Multimodal Environment Representation","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.24558","title":"Position: AI for Science Should Treat Measurement-to-Dataset Pipelines as Inference Components","created":"2026-05-23","cited_by_count":0,"area":null},{"id":"2605.30467","title":"Clustering Guided Domain-Specific Pretrained Foundation Model for Very High-Resolution Arctic Remote Sensing","created":"2026-05-28","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":"2606.05368","title":"Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.10468","title":"Geometric Coastline Localization using Vision-Language Models","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.12218","title":"Adapting Prithvi-EO for Fallow Detection for Food-Water Nexus: ViT-Adapter Necks and Parameter-Efficient Backbone tuning of Geospatial Foundation Model","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2606.16580","title":"Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction","created":"2026-06-15","cited_by_count":0,"area":null}],"next_cursor":"100"}