{"id":"W4312595763","doi":"10.1109/igarss46834.2022.9884722","title":"An Urban Road Extraction Method Based on Trajectory Clustering","year":2022,"lang":"en","type":"article","venue":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Delaunay triangulation; Cluster analysis; Computer science; Trajectory; Position (finance); Triangulation; Transport engineering; Artificial intelligence; Data mining; Geography; Engineering; Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006890883,0.0002308448,0.0001705909,0.0004547091,0.0006047871,0.0001582119,0.0002774976,0.00007650732,0.00009828121],"category_scores_gemma":[0.00001581682,0.000250129,0.00008191903,0.0003778349,0.00004763887,0.000382871,0.0000490588,0.000567679,0.000006350309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003721955,"about_ca_system_score_gemma":0.0000449949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002322433,"about_ca_topic_score_gemma":0.00002520447,"domain_scores_codex":[0.9979706,0.0001399215,0.0002955086,0.0005197942,0.0007498561,0.0003242721],"domain_scores_gemma":[0.9993944,0.00008375772,0.0001024231,0.0002593939,0.00004724748,0.0001127685],"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":[0.00003697163,0.00003847438,0.00001744319,0.00001219569,0.00001681926,0.00004889183,0.0002721372,0.5335466,0.3861026,0.000009329546,0.000510754,0.07938787],"study_design_scores_gemma":[0.0002634937,0.000140665,0.0007839537,0.00003579949,0.00001678213,0.0002464211,0.0002254563,0.9817258,0.008704369,0.00003192157,0.007530212,0.0002951916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.610365,0.000108221,0.3643678,0.0008740695,0.01775036,0.0003385586,0.00007701472,0.001106432,0.005012523],"genre_scores_gemma":[0.9849091,0.00005312964,0.01316574,0.0004236182,0.0004953883,0.000002323271,0.00005784239,0.00005799511,0.0008348487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4481792,"threshold_uncertainty_score":0.9999951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01128541110923544,"score_gpt":0.2764108710500044,"score_spread":0.265125459940769,"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."}}