{"id":"W4400613682","doi":"10.1016/j.rse.2024.114290","title":"Deep learning for urban land use category classification: A review and experimental assessment","year":2024,"lang":"en","type":"review","venue":"Remote Sensing of Environment","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Remote sensing; Land use; Computer science; Environmental science; Artificial intelligence; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003323175,0.0005686192,0.001409188,0.0001753532,0.00008008358,0.00009160417,0.0001080536,0.0002568552,0.000007701853],"category_scores_gemma":[0.0000464952,0.0005284676,0.0003321022,0.0001284546,0.0001232313,0.00009832922,0.0000753526,0.000498328,0.00003147562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000654084,"about_ca_system_score_gemma":0.00004372691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008275601,"about_ca_topic_score_gemma":8.162046e-7,"domain_scores_codex":[0.9978039,0.0001433782,0.0008825487,0.0006053541,0.0002640641,0.0003007926],"domain_scores_gemma":[0.998769,0.0001968766,0.0003076019,0.0005928982,0.0000214101,0.0001121973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001275552,0.00001442242,4.278298e-7,0.04622068,0.0002449717,0.000008723435,0.00006034979,0.000299399,0.0003141846,0.00001090962,0.0007694592,0.9520552],"study_design_scores_gemma":[0.000119167,0.00005027049,0.0000121209,0.01827288,0.002481422,0.00009935861,0.00002103913,0.1423456,0.00003592716,0.000008634725,0.8360533,0.0005003385],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001169443,0.9677619,0.03012882,0.00003778013,0.000232989,0.001403735,0.000008922492,0.0001585981,0.0002555053],"genre_scores_gemma":[0.000280418,0.9703847,0.02840276,0.00001474783,0.0001195409,0.000003309976,0.0002374519,0.0002099829,0.0003470943],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9515548,"threshold_uncertainty_score":0.9997167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05394558127974756,"score_gpt":0.3101542184369268,"score_spread":0.2562086371571792,"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."}}