{"id":"W2464204616","doi":"10.1109/cvpr.2016.393","title":"HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images","year":2016,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Ground truth; Segmentation; Inertial measurement unit; Aerial imagery; Aerial survey; Parsing; Inference; Drone; Image segmentation; Remote sensing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004892146,0.00234235,0.001042235,0.005456334,0.0005027825,0.001670447,0.002427765,0.00139425,0.004434719],"category_scores_gemma":[0.001131376,0.001097174,0.001507414,0.003442999,0.0005497299,0.002207005,0.001806041,0.0013136,0.005360199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005229341,"about_ca_system_score_gemma":0.0007906597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01345603,"about_ca_topic_score_gemma":0.0392867,"domain_scores_codex":[0.9994096,0.0000515376,0.00002207432,0.0002818449,0.0001419385,0.00009315875],"domain_scores_gemma":[0.9993406,0.0001176656,0.00006647994,0.0003080373,0.0001285563,0.00003863706],"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":[0.0004602211,0.0005136313,0.01389927,0.0008481226,0.000688793,0.0005873791,0.0003885628,0.08582249,0.04273631,0.004159209,0.07288148,0.7770146],"study_design_scores_gemma":[0.0001214377,0.0002234025,0.0255004,0.0001448655,0.0003630652,0.0008494264,0.0006072994,0.7955742,0.04550947,0.02319653,0.1077295,0.0001804804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07511006,0.001540515,0.7684104,0.0003796153,0.0002958272,0.0006530764,0.05288555,0.09062491,0.01010002],"genre_scores_gemma":[0.1931347,0.000517931,0.6963242,0.0002796411,0.0001584765,0.000268794,0.1009044,0.002515477,0.005896386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01345603,"threshold_uncertainty_score":0.02675539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007614698424362755,"score_gpt":0.2257034413586504,"score_spread":0.2180887429342877,"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."}}