{"total":20,"rows":[{"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":"2601.13134","title":"Earth Embeddings as Products: Taxonomy, Ecosystem, and Standardized Access","created":"2026-01-19","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":"2603.02080","title":"From Pixels to Patches: Pooling Strategies for Earth Embeddings","created":"2026-03-02","cited_by_count":4,"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":"2608.06612","title":"SLED: Scalable Location Encoding via Distillation","created":"2026-08-06","cited_by_count":2,"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":"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":"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":"2604.03301","title":"Embedding-Only Uplink for Onboard Retrieval Under Shift in Remote Sensing","created":"2026-03-30","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":"2606.23615","title":"Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark","created":"2026-06-22","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":"2608.12271","title":"Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling","created":"2026-08-12","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":"2608.30895","title":"A Controlled Evaluation of Model Rankings and Input Reliance in Surface Water Segmentation","created":"2026-08-31","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":"2610.18624","title":"MajorTOM:Elliot Pretrain, A Multi-Sensor, Multi-Temporal Pretraining Dataset for Earth Observation","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}