{"id":"W4389658633","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-7-2023","title":"DEVELOPING COMPLETE URBAN DIGITAL TWINS IN BUSY ENVIRONMENTS: A FRAMEWORK FOR FACILITATING 3D MODEL GENERATION FROM MULTI-SOURCE POINT CLOUD DATA","year":2023,"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":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Computer science; Cloud computing; Photogrammetry; Lidar; Data science; Data mining; Software; Point (geometry); Systems engineering; Remote sensing; Artificial intelligence; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002126342,0.001061336,0.00073934,0.003119621,0.0009918819,0.002770987,0.002703257,0.000868801,0.003778113],"category_scores_gemma":[0.00509692,0.0008555277,0.001989373,0.002428591,0.001047469,0.002766948,0.006090757,0.001543079,0.001986253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008418253,"about_ca_system_score_gemma":0.001772966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078231,"about_ca_topic_score_gemma":0.01897597,"domain_scores_codex":[0.9987185,0.0002226032,0.0001266686,0.000234519,0.000597983,0.00009967999],"domain_scores_gemma":[0.9985867,0.0003192549,0.0001192515,0.0004742684,0.0003845489,0.0001160623],"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":[0.0003017996,0.0005439725,0.008446929,0.0007638067,0.0003144646,0.001457102,0.001865083,0.3479013,0.02901358,0.09880663,0.02975725,0.480828],"study_design_scores_gemma":[0.00003831134,0.00005969033,0.0014179,0.00008316372,0.00003141397,0.0002181109,0.0004503001,0.9246282,0.01391754,0.02323986,0.03583848,0.00007701854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003353955,0.00004473731,0.9884517,0.0000647701,0.00001954211,0.0001918202,0.0007233132,0.006266512,0.0008836819],"genre_scores_gemma":[0.05757258,0.000141606,0.9348203,0.00004006258,0.00001310108,0.0003692984,0.005359323,0.0009585831,0.0007251505],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01078231,"threshold_uncertainty_score":0.02143908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0722144247631094,"score_gpt":0.2774624999479975,"score_spread":0.2052480751848881,"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."}}