{"id":"W4292970091","doi":"10.1109/lgrs.2022.3201489","title":"SatViT: Pretraining Transformers for Earth Observation","year":2022,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Transformer; Artificial intelligence; Training set; Machine learning; Pattern recognition (psychology); Voltage; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004014285,0.0001381335,0.0001285714,0.0001360803,0.0005126861,0.00008280894,0.00007699038,0.00003429596,0.000001447068],"category_scores_gemma":[0.00003018973,0.0001516742,0.00005168316,0.0003341406,0.0001291645,0.0002153145,0.00000962898,0.0001912649,0.000001424554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007596002,"about_ca_system_score_gemma":0.00002099542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003835164,"about_ca_topic_score_gemma":0.000009557567,"domain_scores_codex":[0.9988902,0.00003201476,0.0001946651,0.0002882573,0.0002471877,0.0003476646],"domain_scores_gemma":[0.9996642,0.00007317992,0.00004240701,0.0001301566,0.00002620068,0.00006389028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005660748,0.00000146649,0.000005962864,0.00002460747,0.000005129978,0.000003633624,0.0007279942,0.007073042,0.5821413,0.000001816813,0.000343028,0.4096663],"study_design_scores_gemma":[0.0002312732,0.00003533518,0.001193739,0.00002452827,0.00001328377,0.00006177202,0.0002761545,0.9793032,0.01121971,0.00003877143,0.007386985,0.0002152434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5445679,0.00002231686,0.4528285,0.001277547,0.0008424341,0.0001889412,0.00000519768,0.0001493311,0.0001178934],"genre_scores_gemma":[0.8925212,0.00002248921,0.1054869,0.001634428,0.0001288739,4.19848e-7,0.00001475083,0.00004032622,0.000150637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9722301,"threshold_uncertainty_score":0.6185095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02533367531391046,"score_gpt":0.2219756204092544,"score_spread":0.196641945095344,"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."}}