{"id":"W3081466886","doi":"10.5194/isprs-archives-xliii-b3-2020-611-2020","title":"SEMI-AUTOMATIC APPROACH FOR OPTICAL AND LIDAR DATA INTEGRATION USING PHASE CONGRUENCY MODEL AT MULTIPLE RESOLUTIONS","year":2020,"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":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Hong Kong Polytechnic University","keywords":"Lidar; Computer science; Artificial intelligence; Affine transformation; Context (archaeology); Computer vision; Filter (signal processing); Consistency (knowledge bases); Transformation (genetics); Data set; Remote sensing; Pattern recognition (psychology); Mathematics; Geography","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":[0.000971162,0.0007185654,0.0009265495,0.001804897,0.0005096503,0.001180694,0.001158464,0.0006969039,0.002222349],"category_scores_gemma":[0.001538901,0.0006505508,0.001431674,0.001524562,0.0003813704,0.0009996425,0.001223537,0.0009578267,0.001163178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005599438,"about_ca_system_score_gemma":0.001461677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006136175,"about_ca_topic_score_gemma":0.006539881,"domain_scores_codex":[0.999222,0.0001250301,0.00004863552,0.0002411081,0.0002815432,0.00008160608],"domain_scores_gemma":[0.9994286,0.0001361441,0.00009315679,0.00009304944,0.0002198731,0.00002918188],"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":[0.0003818249,0.000267599,0.003791626,0.0002489741,0.0001935914,0.0002063292,0.0002716444,0.3141741,0.07332826,0.004902089,0.00262899,0.599605],"study_design_scores_gemma":[0.000008755424,0.00003837474,0.0009090866,0.000005297434,0.00001557337,0.00003273737,0.00002818254,0.9921354,0.004875148,0.0008269098,0.001111037,0.00001351656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01606849,0.00006888904,0.9820322,0.0000386252,0.00002088738,0.00006962209,0.00008849839,0.001153206,0.0004595211],"genre_scores_gemma":[0.3511397,0.0001144741,0.645829,0.00007111845,0.00003160814,0.0002770586,0.0007420741,0.000229492,0.001565513],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006136175,"threshold_uncertainty_score":0.01220095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04754399250594371,"score_gpt":0.2907314164484067,"score_spread":0.243187423942463,"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."}}