{"id":"W1808410813","doi":"10.5194/isprsarchives-xl-1-w4-369-2015","title":"ALIGNMENT OF POINT CLOUD DSMs FROM TLS AND UAV PLATFORMS","year":2015,"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":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Photogrammetry; Computer science; Computer vision; Artificial intelligence; Computer graphics; Transformation (genetics); Iterative closest point; Matching (statistics); Computer graphics (images); Point (geometry); Point set registration; Remote sensing; Geography; Mathematics","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":[0.0004071163,0.0005304939,0.0003928941,0.002481332,0.0003678023,0.0007163467,0.0005643191,0.000491448,0.001601535],"category_scores_gemma":[0.001771788,0.000383873,0.0006668459,0.002579876,0.0002562738,0.0005112703,0.0009794146,0.0004494787,0.001004838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006191662,"about_ca_system_score_gemma":0.000946414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008358647,"about_ca_topic_score_gemma":0.01200437,"domain_scores_codex":[0.9994169,0.00006938144,0.00003991387,0.00009533282,0.0002901433,0.00008830662],"domain_scores_gemma":[0.9992841,0.00005811132,0.00008880537,0.0001887004,0.0003307301,0.00004965332],"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":[0.001103009,0.0005676514,0.06038639,0.0006449366,0.0004154912,0.001120131,0.0007619843,0.2282748,0.2042698,0.003467193,0.009587431,0.489401],"study_design_scores_gemma":[0.0001015786,0.0002457593,0.105132,0.00007667831,0.00005893523,0.0004231952,0.0009867272,0.8113673,0.07158905,0.002340856,0.007595042,0.00008293638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7716576,0.0003395308,0.2132011,0.0001915116,0.0002743882,0.0004184927,0.005628072,0.003713614,0.004575653],"genre_scores_gemma":[0.8826684,0.00008814166,0.1098782,0.00002919157,0.00001347855,0.00008072212,0.006614222,0.00009425105,0.0005334147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008358647,"threshold_uncertainty_score":0.01661998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01952107594277251,"score_gpt":0.2364593797007774,"score_spread":0.2169383037580048,"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."}}