{"id":"W4320176508","doi":"10.1016/j.isprsjprs.2023.01.009","title":"Road object detection for HD map: Full-element survey, analysis and perspectives","year":2023,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Object (grammar); Object detection; Road surface; Road map; Construct (python library); Artificial intelligence; Computer vision; Key (lock); Segmentation; Geography; Cartography; Engineering; Computer security","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":[0.0009048296,0.0001385199,0.0002903641,0.0007395567,0.0001590852,0.00008960838,0.00003266946,0.00009424325,0.000001824458],"category_scores_gemma":[0.0000910423,0.0001209684,0.000145468,0.0008868369,0.00003364283,0.0001170299,0.00001304206,0.0002007166,6.766494e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000551603,"about_ca_system_score_gemma":0.00000844117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003766808,"about_ca_topic_score_gemma":0.000573086,"domain_scores_codex":[0.9991259,0.00005849199,0.0003081594,0.0001384515,0.0001522956,0.0002167172],"domain_scores_gemma":[0.9994305,0.0001465445,0.0001365246,0.00007218248,0.0001299354,0.0000843269],"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":[0.00008727627,0.000008440773,0.000194392,0.00007843522,0.0008273239,0.00002259737,0.0006138685,0.00437981,0.1312681,5.253748e-7,0.00006066188,0.8624586],"study_design_scores_gemma":[0.0006603065,0.000227186,0.04097017,0.000102033,0.0006249735,0.0002912953,0.002359193,0.9117051,0.0419589,0.0001108344,0.0007263961,0.0002636056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7385184,0.000730782,0.2601303,0.00002676895,0.0003960101,0.0000817414,0.000005143131,0.00009617508,0.00001475381],"genre_scores_gemma":[0.9943574,0.0008058652,0.004627252,0.000005639637,0.0001607921,5.154385e-8,0.000005143007,0.00002104816,0.00001679467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9073253,"threshold_uncertainty_score":0.493295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01726824336076558,"score_gpt":0.2594019683470514,"score_spread":0.2421337249862859,"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."}}