{"id":"W2780861787","doi":"10.1109/iccv.2017.372","title":"DeepRoadMapper: Extracting Road Topology from Aerial Images","year":2017,"lang":"en","type":"article","venue":"","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":558,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Focus (optics); Motion planning; Topology (electrical circuits); Aerial image; Artificial intelligence; Shortest path problem; Deep learning; State (computer science); Computer vision; Path (computing); Network topology; Image (mathematics); Algorithm; Engineering; Theoretical computer science; Computer network; Robot","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001973929,0.002219618,0.0008545524,0.003630998,0.0005123846,0.001050969,0.001666863,0.001185319,0.005322581],"category_scores_gemma":[0.0007111981,0.0007690801,0.001056335,0.002453733,0.000377461,0.001313029,0.001364033,0.001092587,0.005558279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005641588,"about_ca_system_score_gemma":0.0008658745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01617085,"about_ca_topic_score_gemma":0.05636536,"domain_scores_codex":[0.9996443,0.00002437566,0.00001434606,0.0001429852,0.0001162342,0.00005790851],"domain_scores_gemma":[0.9997757,0.00003782074,0.00002830685,0.00008731031,0.00005402537,0.0000168549],"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":[0.0001946726,0.0002977361,0.006355773,0.0009668967,0.0004015928,0.0007531019,0.0002158266,0.07617197,0.04129239,0.00341096,0.1690006,0.7009384],"study_design_scores_gemma":[0.0001058944,0.0001189457,0.0154108,0.0001480861,0.0001363329,0.001359049,0.0003780036,0.7996026,0.0684853,0.01136953,0.1027665,0.0001190224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.111758,0.002340334,0.6384251,0.0006670952,0.0003624354,0.0008702001,0.1085886,0.1217799,0.01520838],"genre_scores_gemma":[0.2309854,0.001050497,0.5496551,0.0001916427,0.00008632719,0.0003368664,0.208213,0.001792514,0.007688586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01617085,"threshold_uncertainty_score":0.03215349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01295831396303205,"score_gpt":0.256611640676719,"score_spread":0.2436533267136869,"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."}}