{"id":"W2969510143","doi":"10.1016/j.isprsjprs.2019.08.010","title":"Recovery of urban 3D road boundary via multi-source data","year":2019,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Boundary (topology); Global Positioning System; Trajectory; Computer vision; Artificial intelligence; Road surface; Point (geometry); Remote sensing; Geography; Engineering; Mathematics; Geometry","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":[0.0004928808,0.000162736,0.0003490571,0.0002575288,0.00006212368,0.00005723943,0.0001584027,0.0001456301,0.00001040532],"category_scores_gemma":[0.00005308961,0.0001419314,0.00008989979,0.0002389944,0.00005078474,0.0003562685,0.00005563281,0.0004316045,0.000004328687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003651278,"about_ca_system_score_gemma":0.00002530303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002190506,"about_ca_topic_score_gemma":0.00001496646,"domain_scores_codex":[0.9988922,0.00004422011,0.0004857635,0.0001509355,0.0002101644,0.0002167547],"domain_scores_gemma":[0.9991693,0.00007301723,0.0002756278,0.00030657,0.00008185179,0.00009356974],"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":[0.00004512658,0.00001855973,0.0001413037,0.0001289333,0.0001292206,0.00002925392,0.0001418452,0.002697575,0.198205,9.412408e-8,0.000248271,0.7982148],"study_design_scores_gemma":[0.0008405561,0.0001420164,0.001126744,0.0006206968,0.0001099977,0.001455527,0.0002092618,0.9484562,0.02849499,0.00002322904,0.01826915,0.0002516534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7288349,0.002193434,0.2673016,0.0000116196,0.001297322,0.00007835284,0.000005237101,0.00006255427,0.0002149481],"genre_scores_gemma":[0.9539145,0.0004770014,0.04527267,0.0000263656,0.0001960144,3.179587e-9,0.000007083726,0.00003636491,0.00007005435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9457586,"threshold_uncertainty_score":0.5787796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338903981515576,"score_gpt":0.2406437063556982,"score_spread":0.2272546665405424,"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."}}