{"total":39,"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":"2505.21357","title":"AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mapping","created":"2025-05-27","cited_by_count":9,"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":"2508.00858","title":"Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal","created":"2025-07-16","cited_by_count":6,"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":"2502.15021","title":"Thicker and Quicker: A Jumbo Token for Fast Plain Vision Transformers","created":"2025-02-20","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":"2602.00392","title":"Localized, High-resolution Geographic Representations with Slepian Functions","created":"2026-01-30","cited_by_count":3,"area":null},{"id":"2608.09497","title":"SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping","created":"2026-08-10","cited_by_count":3,"area":null},{"id":"2504.19223","title":"CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis","created":"2025-04-27","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":"2602.19863","title":"Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation","created":"2026-02-23","cited_by_count":2,"area":null},{"id":"2602.21421","title":"ECHOSAT: Estimating Canopy Height Over Space And Time","created":"2026-02-24","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":"2603.23408","title":"GeoSANE: Learning Geospatial Representations from Models, Not Data","created":"2026-03-24","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":"2605.20804","title":"OlmoEarth v1.2: A more efficient family of OlmoEarth models","created":"2026-05-20","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":"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":"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":"2603.01576","title":"Cryo-Bench: Benchmarking Foundation Models for Cryosphere Mapping","created":"2026-03-02","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":"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":"2607.22408","title":"LunarFM: A Shared Multimodal Representation of the Moon's Surface","created":"2026-07-24","cited_by_count":1,"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":"2506.12885","title":"$T^{3}S$: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series","created":"2025-06-15","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":"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":"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":"2603.23669","title":"Estimating Individual Tree Height and Species from UAV Imagery","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2606.23356","title":"Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2606.23615","title":"Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark","created":"2026-06-22","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":"2609.09482","title":"Infra-Bench CLS: A Global, Open-Source Benchmark for Critical Infrastructure Classification with Earth Observation Foundation Models","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2609.13868","title":"Physically Typed and Geometry-Aware Representations for Earth Foundation Models","created":"2026-09-12","cited_by_count":0,"area":null},{"id":"2609.23427","title":"RSPDBench: Benchmarking Vision Foundation Models on Earth Observation Tasks Under Physically Grounded Remote-Sensing Product Degradations","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2609.37784","title":"Planetary Feature Fields are Scalable Earth Representations","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.18845","title":"The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}