{"id":"W2988795680","doi":"10.1109/igarss.2019.8898612","title":"Robust Building-Based Registration of Airborne Lidar Data and Optical Imagery on Urban Scenes","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre de Géomatique du Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Région Bretagne; Université Laval","keywords":"Lidar; Point cloud; Computer science; Artificial intelligence; Computer vision; Remote sensing; Segmentation; Matching (statistics); Point set registration; Image registration; Sensor fusion; Transformation (genetics); Process (computing); Point (geometry); Geography; Image (mathematics); 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":[0.0004491418,0.0005367236,0.0005253862,0.001924792,0.0002175525,0.000728134,0.0006206899,0.0005285527,0.0009759853],"category_scores_gemma":[0.001308439,0.0003881873,0.0006166908,0.002707804,0.0004962819,0.0008111391,0.001315204,0.0004614679,0.0008326141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000227702,"about_ca_system_score_gemma":0.0004505253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002008972,"about_ca_topic_score_gemma":0.002941148,"domain_scores_codex":[0.9992065,0.0001606501,0.00003415317,0.0001920176,0.0003122879,0.00009439224],"domain_scores_gemma":[0.9996452,0.00007133956,0.00006485359,0.0001311452,0.00006952447,0.00001791725],"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":[0.0003996457,0.0001901877,0.004299594,0.0003468959,0.0002315717,0.0003687727,0.0005053974,0.2305886,0.2586679,0.007807243,0.002410175,0.4941839],"study_design_scores_gemma":[0.0000330265,0.0002128382,0.01615888,0.00003366196,0.00006947708,0.0005285455,0.0003552802,0.8535761,0.1122705,0.009327372,0.007377414,0.00005682442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1521178,0.0003249533,0.8427969,0.00007752414,0.00004186463,0.0001111238,0.0004245309,0.0020201,0.002085171],"genre_scores_gemma":[0.613048,0.0002860446,0.3831947,0.00003880285,0.00004215193,0.00009136513,0.001537182,0.0003631421,0.001398568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002008972,"threshold_uncertainty_score":0.003994584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03940847690774195,"score_gpt":0.2666458521551618,"score_spread":0.2272373752474198,"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."}}