{"id":"W4414314774","doi":"10.5194/isprs-archives-xlviii-4-w15-2025-71-2025","title":"Mobile LiDAR Data and Imagery for Digital Twin Generation","year":2025,"lang":"en","type":"article","venue":"˜The œinternational 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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Bundle adjustment; Lidar; Photogrammetry; Ranging; Mobile mapping; Perspective (graphical); Visualization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001750831,0.0003652002,0.0001816116,0.001222164,0.0002450391,0.0003779316,0.0004284587,0.000426036,0.007628956],"category_scores_gemma":[0.0006441262,0.0001290242,0.000361208,0.001366377,0.000110535,0.0005811038,0.000696243,0.0003242089,0.002321332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002645835,"about_ca_system_score_gemma":0.0004608079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005706962,"about_ca_topic_score_gemma":0.008536558,"domain_scores_codex":[0.9997657,0.00002299138,0.00001089549,0.00004939418,0.0001134729,0.00003757855],"domain_scores_gemma":[0.999692,0.00001747926,0.00002119022,0.00007987518,0.000162946,0.00002656641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001120487,0.0004134976,0.04774876,0.0006134042,0.0001684861,0.001273655,0.0004825536,0.0688233,0.1134139,0.003949498,0.0346549,0.7273375],"study_design_scores_gemma":[0.0002582808,0.0006136622,0.1842564,0.0001133213,0.000147902,0.001413957,0.001564576,0.6098907,0.1128845,0.004902068,0.08379285,0.0001618177],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8431367,0.0004703649,0.102894,0.0004704104,0.000374883,0.000357152,0.02693483,0.003951178,0.02141044],"genre_scores_gemma":[0.8967826,0.0001171789,0.07579548,0.00008582132,0.00003089497,0.0001045808,0.02264478,0.0001378098,0.004300852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007628956,"threshold_uncertainty_score":0.0255214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0267645774517076,"score_gpt":0.2636980972418568,"score_spread":0.2369335197901492,"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."}}