{"total":125,"rows":[{"id":"2511.13655","title":"OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation","created":"2025-11-17","cited_by_count":24,"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":"2511.02101","title":"Measuring the Intrinsic Dimension of Earth Representations","created":"2025-11-03","cited_by_count":10,"area":null},{"id":"2510.17914","title":"NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation","created":"2025-10-19","cited_by_count":7,"area":null},{"id":"2512.20056","title":"Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach","created":"2025-12-23","cited_by_count":7,"area":null},{"id":"2602.10354","title":"Physically Interpretable AlphaEarth Foundation Model Embeddings Enable LLM-Based Land Surface Intelligence","created":"2026-02-10","cited_by_count":6,"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":"2603.27101","title":"PRUE: A Practical Recipe for Field Boundary Segmentation at Scale","created":"2026-03-28","cited_by_count":5,"area":null},{"id":"2605.26036","title":"CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities","created":"2026-05-25","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":"2607.03949","title":"TESSERA v2: Scaling Pixel-wise Earth Foundation Models","created":"2026-07-04","cited_by_count":5,"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":"2601.16900","title":"Embedding -based Crop Type Classification in the Groundnut Basin of Senegal","created":"2026-01-23","cited_by_count":4,"area":null},{"id":"2602.17250","title":"Inferring Height from Earth Embeddings: First insights using Google AlphaEarth","created":"2026-02-19","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":"2603.02080","title":"From Pixels to Patches: Pooling Strategies for Earth Embeddings","created":"2026-03-02","cited_by_count":4,"area":null},{"id":"2603.16911","title":"What on Earth is AlphaEarth? Hierarchical structure and functional interpretability for global land cover","created":"2026-03-08","cited_by_count":4,"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":"2511.01408","title":"Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping","created":"2025-11-03","cited_by_count":3,"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":"2602.00392","title":"Localized, High-resolution Geographic Representations with Slepian Functions","created":"2026-01-30","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":"2605.21804","title":"Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis","created":"2026-05-20","cited_by_count":3,"area":null},{"id":"2509.03749","title":"Mapping on a Budget: Optimizing Spatial Data Collection for ML","created":"2025-09-03","cited_by_count":2,"area":null},{"id":"2509.07852","title":"Deep Learning-Based Burned Area Mapping Using Bi-Temporal Siamese Networks and AlphaEarth Foundation Datasets","created":"2025-09-09","cited_by_count":2,"area":null},{"id":"2510.09894","title":"Beyond AlphaEarth: Toward Human-Centered Geospatial Foundation Models via POI-Guided Contrastive Learning","created":"2025-10-10","cited_by_count":2,"area":null},{"id":"2601.07268","title":"From Landslide Conditioning Factors to Satellite Embeddings: Evaluating the Utilisation of Google AlphaEarth for Landslide Susceptibility Mapping using Deep Learning","created":"2026-01-12","cited_by_count":2,"area":null},{"id":"2602.19863","title":"Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation","created":"2026-02-23","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":"2604.11444","title":"HuiYanEarth-SAR: A Foundation Model for High-Fidelity and Low-Cost Global Remote Sensing Imagery Generation","created":"2026-04-13","cited_by_count":2,"area":null},{"id":"2604.11668","title":"UNIGEOCLIP: Unified Geospatial Contrastive Learning","created":"2026-04-13","cited_by_count":2,"area":null},{"id":"2604.23678","title":"Transferable Human Mobility Network Reconstruction with neuroGravity","created":"2026-04-26","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":"2607.14072","title":"MetaPerch: Learning from metadata for bioacoustics foundation models","created":"2026-07-15","cited_by_count":2,"area":null},{"id":"2608.06612","title":"SLED: Scalable Location Encoding via Distillation","created":"2026-08-06","cited_by_count":2,"area":null},{"id":"2509.02783","title":"The Transparent Earth: A Multimodal Foundation Model for the Earth's Subsurface","created":"2025-09-02","cited_by_count":1,"area":null},{"id":"2509.03497","title":"Invariant Features for Global Crop Type Classification","created":"2025-09-03","cited_by_count":1,"area":null},{"id":"2509.17942","title":"StefaLand: An Efficient Geoscience Foundation Model That Improves Dynamic Land-Surface Predictions","created":"2025-09-22","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":"2510.14493","title":"Grazing Detection using Deep Learning and Sentinel-2 Time Series Data","created":"2025-10-16","cited_by_count":1,"area":null},{"id":"2511.01990","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":"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":"2601.21899","title":"Breaking the Regional Barrier: Inductive Semantic Topology Learning for Worldwide Air Quality Forecasting","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2602.19190","title":"FUSAR-GPT : A Spatiotemporal Feature-Embedded and Two-Stage Decoupled Visual Language Model for SAR Imagery","created":"2026-02-22","cited_by_count":1,"area":null},{"id":"2603.24744","title":"Contrastive Learning Boosts Deterministic and Generative Models for Weather Data","created":"2026-03-25","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":"2604.18881","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":"2605.18667","title":"Better Together: Evaluating the Complementarity of Earth Embedding Models","created":"2026-05-18","cited_by_count":1,"area":null},{"id":"2605.18911","title":"Does Your Wildfire Prediction Model Actually Work, or Just Score Well?","created":"2026-05-14","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":"2606.11510","title":"Continuous biome representations from Earth observation embeddings","created":"2026-06-09","cited_by_count":1,"area":null},{"id":"2606.20034","title":"Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland","created":"2026-06-18","cited_by_count":1,"area":null},{"id":"2606.24997","title":"What's in an Earth Embedding? An Explainability Analysis of Location Encoders","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2606.29134","title":"Beyond Backscatter: AlphaEarth Land-Cover Priors for Rapid SAR Flood Segmentation Across Foundation Backbones","created":"2026-06-28","cited_by_count":1,"area":null},{"id":"2607.24532","title":"From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps","created":"2026-07-27","cited_by_count":1,"area":null},{"id":"2607.27217","title":"Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States","created":"2026-06-25","cited_by_count":1,"area":null},{"id":"2608.02315","title":"GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation","created":"2026-08-03","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":"2608.16614","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":"2608.26088","title":"Planetary Prediction Engine: Autonomous Geospatial Prediction via Intelligent Data Selection and Foundation Model Embeddings","created":"2026-08-26","cited_by_count":1,"area":null},{"id":"2508.13814","title":"Unsupervised Urban Tree Biodiversity Mapping from Street-Level Imagery Using Spatially-Aware Visual Clustering","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2510.02572","title":"Geospatial Machine Learning Libraries","created":"2025-10-02","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":"2511.11706","title":"Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modelling","created":"2025-11-12","cited_by_count":0,"area":null},{"id":"2511.17954","title":"A multi-view contrastive learning framework for spatial embeddings in risk modelling","created":"2025-11-22","cited_by_count":0,"area":null},{"id":"2512.20813","title":"GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface","created":"2025-12-23","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":"2602.23146","title":"Partial recovery of meter-scale surface weather","created":"2026-02-26","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":"2604.03874","title":"Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance","created":"2026-04-04","cited_by_count":0,"area":null},{"id":"2604.21032","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":"2605.00972","title":"Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2605.04510","title":"Predictive and Prescriptive AI toward Optimizing Wildfire Suppression","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.06990","title":"TrajGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.14120","title":"Mini-JEPA Foundation Model Fleet Enables Agentic Hydrologic Intelligence","created":"2026-05-13","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":"2605.15666","title":"ChronoEarth-492K: A Large Scale and Long Horizon Spatiotemporal Hyperspectral Earth Observation Dataset and Benchmark","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.16665","title":"In-context learning enables continental-scale subsurface temperature prediction from sparse local observations","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.19812","title":"FLUXtrapolation: A benchmark on extrapolating ecosystem fluxes","created":"2026-05-19","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":"2606.07725","title":"GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series","created":"2026-06-05","cited_by_count":0,"area":null},{"id":"2606.08046","title":"OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs","created":"2026-06-06","cited_by_count":0,"area":null},{"id":"2606.11534","title":"Urban Heat MiniCubes: An AI-Ready dataset for urban heat research","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},{"id":"2606.20167","title":"Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2607.01082","title":"When Context Compensates for Sparse Event History: AlphaEarth for Spatio-Temporal Point-Process Forecasting","created":"2026-07-01","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":"2607.10231","title":"PhenoEmbed: Self-Supervised Multispectral UAV Time-Series Embeddings for Individual Tree Crown Phenology","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2607.12177","title":"The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.17661","title":"Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers","created":"2026-07-20","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":"2607.19503","title":"Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2607.23908","title":"Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets","created":"2026-07-27","cited_by_count":0,"area":null},{"id":"2607.29527","title":"TerraNova: A Foundation Model for the Anthropocene","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2608.03410","title":"Earth Embeddings","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2608.14372","title":"Catching the Imposter: Self-Supervised Learning of Physical Coherence with Cross-Entity Feature Permutations","created":"2026-08-14","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":"2608.27175","title":"Temporal Sensitivity Analysis of Tessera Embeddings","created":"2026-08-27","cited_by_count":0,"area":null}],"next_cursor":"100"}