{"id":"W2135148444","doi":"10.5194/isprsarchives-xxxviii-5-w12-109-2011","title":"DETECTION OF ROAD CURB FROM MOBILE TERRESTRIAL LASER SCANNER POINT CLOUD","year":2012,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Computer science; Computer vision; Laser scanning; Segmentation; Pipeline (software); Point (geometry); Road surface; Scanner; Artificial intelligence; Remote sensing; Triangulation; Mobile mapping; Elevation (ballistics); Geography; Engineering; Laser; Mathematics; Cartography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001306713,0.0004412752,0.0005412215,0.002641478,0.0003145807,0.0004822276,0.0005012885,0.000610512,0.001198666],"category_scores_gemma":[0.0005187584,0.0003104222,0.0003433843,0.001243018,0.0001875913,0.0004159695,0.0005795927,0.0003394197,0.001003101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002681015,"about_ca_system_score_gemma":0.0005112236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005060054,"about_ca_topic_score_gemma":0.0100295,"domain_scores_codex":[0.9996098,0.00002200878,0.00001019819,0.00005574298,0.0002483987,0.00005378222],"domain_scores_gemma":[0.9996119,0.00004406523,0.00006131875,0.00004915165,0.0001944455,0.00003906786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004160738,0.0001832153,0.05353263,0.0003711088,0.0001430147,0.001676929,0.0003008706,0.02859113,0.5984471,0.0008748367,0.00540032,0.3100628],"study_design_scores_gemma":[0.00005977683,0.0003524137,0.1736745,0.0001015863,0.00006493121,0.001733912,0.0005708399,0.6240005,0.1918394,0.001123152,0.006391343,0.00008759325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7112592,0.0004203873,0.2773168,0.0001222126,0.00007342177,0.0002458818,0.002208384,0.003871357,0.004482319],"genre_scores_gemma":[0.9011627,0.0002036534,0.09551719,0.00003766735,0.00001781964,0.00009116477,0.001792398,0.00006373705,0.001113735],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005060054,"threshold_uncertainty_score":0.0100612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01281810414829508,"score_gpt":0.244178369805308,"score_spread":0.231360265657013,"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."}}