{"id":"W2943153115","doi":"10.3390/rs11091017","title":"Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Terrain; Adversarial system; Topology (electrical circuits); Completeness (order theory); Network topology; Artificial intelligence; Generative adversarial network; Shadow (psychology); Quality (philosophy); Data mining; Machine learning; Deep learning; Computer network; Geography; Mathematics","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":[0.0007381787,0.001246268,0.0008051732,0.001032709,0.0002952241,0.0006405788,0.0014162,0.0009939058,0.001608947],"category_scores_gemma":[0.001901803,0.000634546,0.001052788,0.0006170427,0.0007585576,0.001138442,0.001193196,0.001441594,0.0008185328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006576352,"about_ca_system_score_gemma":0.000510792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003447558,"about_ca_topic_score_gemma":0.005795634,"domain_scores_codex":[0.9995346,0.0001231404,0.00001629658,0.0001602943,0.0001076199,0.00005806201],"domain_scores_gemma":[0.9991813,0.0003985317,0.0001222006,0.0001489673,0.0001118127,0.0000370681],"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":[0.000056647,0.00002967996,0.0009029881,0.00004315938,0.00004866029,0.00008812544,0.00003770313,0.9273065,0.004184264,0.002950172,0.00151481,0.06283719],"study_design_scores_gemma":[0.000001888135,0.000006400167,0.0001092611,0.000003297314,0.000004213791,0.00002268416,0.00000365305,0.9973048,0.000983256,0.001322016,0.0002354645,0.000003181104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01984695,0.0001769415,0.9771833,0.0001419024,0.00002594864,0.00004745042,0.0001707682,0.001012475,0.001394221],"genre_scores_gemma":[0.7098418,0.0003240735,0.2816457,0.0003743417,0.00008491529,0.0001642366,0.001814375,0.0003858336,0.005364833],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003447558,"threshold_uncertainty_score":0.006855011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01067714942454933,"score_gpt":0.2366706807346833,"score_spread":0.225993531310134,"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."}}