{"id":"W4416052645","doi":"10.1109/iccv51701.2025.00693","title":"TerraMind: Large-Scale Generative Multimodality for Earth Observation","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"Gauss Centre for Supercomputing; European Space Agency","keywords":"Multimodality; Inference; Geospatial analysis; Generative grammar; Security token; Semantics (computer science); Modalities; Multimodal learning; Earth observation","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008852109,0.001195286,0.0006892623,0.0007352417,0.0004606887,0.001102047,0.002545147,0.001340634,0.006968509],"category_scores_gemma":[0.003071974,0.0007828007,0.002092206,0.0007049452,0.0008014407,0.001975338,0.003271561,0.002891117,0.002181282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046633,"about_ca_system_score_gemma":0.0008000695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01281,"about_ca_topic_score_gemma":0.02895783,"domain_scores_codex":[0.9996694,0.00008276752,0.00001158623,0.0001442057,0.0000537561,0.00003834213],"domain_scores_gemma":[0.9994342,0.0002697404,0.00003658775,0.0001589782,0.00005543189,0.00004506585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003938534,0.0002423052,0.00804371,0.0003509912,0.0005520329,0.0003564123,0.0004197066,0.590942,0.01008544,0.03017329,0.03464869,0.3237916],"study_design_scores_gemma":[0.00001517107,0.00003719584,0.0007208869,0.00002537363,0.00002624097,0.00007621536,0.00002809945,0.9748049,0.001724395,0.01704954,0.005470653,0.00002136917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0296018,0.0007785039,0.9460011,0.0009113932,0.0001682811,0.0001324277,0.005558329,0.01167976,0.00516839],"genre_scores_gemma":[0.5722041,0.0007626628,0.3860844,0.001354455,0.0001972571,0.0004799026,0.02132331,0.002237394,0.01535651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01281,"threshold_uncertainty_score":0.02547085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03810939791941986,"score_gpt":0.3313300054043127,"score_spread":0.2932206074848929,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}