{"id":"W7095806747","doi":"","title":"Automatic road extraction from dense urban area by integrated processing of high resolution imagery and LIDAR data","year":2004,"lang":"en","type":"article","venue":"","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Grid; Hough transform; Segmentation; Data set; Urban area; Image segmentation; Set (abstract data type); Image processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001795453,0.0007055263,0.0004652024,0.002629214,0.0002548618,0.0006492516,0.0005114811,0.0004034929,0.001169596],"category_scores_gemma":[0.0004673637,0.0003653602,0.0006484727,0.001604628,0.0001827328,0.000936745,0.000654067,0.0002861886,0.001114446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001499203,"about_ca_system_score_gemma":0.000429408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003087564,"about_ca_topic_score_gemma":0.006937988,"domain_scores_codex":[0.9997674,0.00002761861,0.00001126886,0.00006212682,0.0000947105,0.00003681521],"domain_scores_gemma":[0.9997769,0.00005417041,0.00003101319,0.00005931418,0.00006594685,0.00001274766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001729912,0.000147463,0.009066707,0.0002964737,0.0001188551,0.0005320674,0.00025444,0.03659633,0.2543699,0.001246661,0.002287969,0.6949101],"study_design_scores_gemma":[0.00005505438,0.0002035755,0.05157875,0.00004238026,0.0001613749,0.0009843741,0.0006394169,0.7594731,0.1721303,0.00332024,0.01131274,0.00009864048],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2254484,0.0004689996,0.7636168,0.00008206532,0.00002851148,0.0001972795,0.001014592,0.005870885,0.003272372],"genre_scores_gemma":[0.4815651,0.0004636463,0.5111659,0.00003746008,0.00001984302,0.00008992919,0.004559437,0.0002223092,0.001876318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003087564,"threshold_uncertainty_score":0.006139159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01363652591651294,"score_gpt":0.2333208913209576,"score_spread":0.2196843654044446,"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."}}