{"id":"W3127391403","doi":"10.1109/lra.2021.3060397","title":"UAV Localization Using Autoencoded Satellite Images","year":2021,"lang":"en","type":"preprint","venue":"IEEE Robotics and Automation Letters","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Defence Research and Development Canada","keywords":"Artificial intelligence; Computer science; Computer vision; Autoencoder; Satellite; Kernel (algebra); Computation; Representation (politics); Pattern recognition (psychology); Remote sensing; Deep learning; Geography; Algorithm; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001990229,0.0005031344,0.0003002111,0.0005477463,0.0001556211,0.0004354016,0.0005913374,0.0004014543,0.0007948485],"category_scores_gemma":[0.001005778,0.0002583831,0.0003394799,0.0006333056,0.0003647133,0.0008236856,0.0007438953,0.000378707,0.0005588769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000222588,"about_ca_system_score_gemma":0.0003411135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005010902,"about_ca_topic_score_gemma":0.006541267,"domain_scores_codex":[0.9996667,0.00004411229,0.00001015013,0.00009052671,0.0001508963,0.00003763827],"domain_scores_gemma":[0.9996859,0.00005127991,0.00006017362,0.00009186615,0.00009913094,0.00001168248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003431241,0.00006958628,0.00536545,0.0001525861,0.0001432152,0.000435345,0.0003038438,0.3001046,0.2179329,0.003496304,0.002481124,0.469172],"study_design_scores_gemma":[0.00002187002,0.0001222773,0.005250087,0.00002218558,0.00002453519,0.0003208428,0.00008256538,0.9125831,0.07563126,0.001587482,0.004321803,0.00003203278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1541875,0.0002465215,0.8405128,0.0001238661,0.00007297195,0.00003431581,0.0002138234,0.002006258,0.002601939],"genre_scores_gemma":[0.74078,0.0001867786,0.2559716,0.00008770607,0.0000441705,0.00003566912,0.0005279393,0.0001455914,0.002220655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005010902,"threshold_uncertainty_score":0.009963453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620983881900805,"score_gpt":0.2264389787113051,"score_spread":0.210229139892297,"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."}}