{"id":"W2893333163","doi":"10.3390/rs10101531","title":"Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review","year":2018,"lang":"en","type":"review","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":225,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Calgary; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Point cloud; Mobile mapping; Road surface; Software; Point (geometry); Computer vision; Artificial intelligence; Engineering","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":[0.001392621,0.00126349,0.001370341,0.004945965,0.0003881842,0.001453078,0.00175347,0.001244989,0.002684229],"category_scores_gemma":[0.002734213,0.0007530011,0.00133406,0.006306348,0.0004910452,0.002523812,0.0007397833,0.0008414836,0.002314603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005366668,"about_ca_system_score_gemma":0.001915338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003948792,"about_ca_topic_score_gemma":0.003971685,"domain_scores_codex":[0.999198,0.0001054287,0.000112697,0.0001788572,0.0003582475,0.00004667848],"domain_scores_gemma":[0.997895,0.001141414,0.0001891293,0.00006866761,0.0006648626,0.00004084937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004549896,0.0000659661,0.001143348,0.03493176,0.0002227307,0.0001692153,0.0001389503,0.001793896,0.00304391,0.002631045,0.01595895,0.9398548],"study_design_scores_gemma":[0.00001598474,0.0002359467,0.005402238,0.01246839,0.0008917301,0.001816822,0.0003820752,0.004538761,0.005746583,0.003274175,0.96505,0.0001773443],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009144048,0.9892262,0.006553355,0.0003225557,0.0002650216,0.00005000543,0.0002421602,0.00009181491,0.002334513],"genre_scores_gemma":[0.004450533,0.9881706,0.005991279,0.000169231,0.0002094984,0.00004184269,0.000342959,0.00001980318,0.000604195],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004945965,"threshold_uncertainty_score":0.008979678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02523861043269165,"score_gpt":0.3130555511552838,"score_spread":0.2878169407225922,"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."}}