{"id":"W2145648239","doi":"10.5194/isprsarchives-xxxix-b3-285-2012","title":"AUTOMATIC 3D BUILDING MODEL GENERATION FROM LIDAR AND IMAGE DATA USING SEQUENTIAL MINIMUM BOUNDING RECTANGLE","year":2012,"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":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Calgary","funders":"","keywords":"Computer science; Bounding overwatch; Lidar; Process (computing); Aerial image; Data mining; Rectangle; Data model (GIS); Set (abstract data type); Ranging; Component (thermodynamics); Artificial intelligence; Image (mathematics); Remote sensing; Geography; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007557778,0.001236924,0.001053945,0.00182811,0.0003762752,0.0009501377,0.001287545,0.0006750929,0.002642279],"category_scores_gemma":[0.001695599,0.0008605172,0.001447453,0.001759289,0.0003315737,0.001144642,0.001141151,0.0006849843,0.001721721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003279599,"about_ca_system_score_gemma":0.000885229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004721994,"about_ca_topic_score_gemma":0.004955161,"domain_scores_codex":[0.9990913,0.0001400724,0.00005966994,0.0002719253,0.0003666794,0.00007037889],"domain_scores_gemma":[0.9992641,0.0002279285,0.0000841951,0.0001888736,0.0002028174,0.00003201267],"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":[0.0002825814,0.0001875352,0.003383409,0.0004069105,0.0001602175,0.0004855598,0.0003146399,0.2605366,0.08650825,0.003895734,0.006276049,0.6375626],"study_design_scores_gemma":[0.00001776226,0.00004133211,0.0008399938,0.00001699006,0.00001869047,0.0001495679,0.00004913794,0.9769229,0.01755587,0.001102379,0.003262415,0.00002290013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01623113,0.0001379873,0.9782996,0.00003804294,0.00002219931,0.0001142942,0.0002800066,0.00403908,0.0008376397],"genre_scores_gemma":[0.1512406,0.0001871996,0.8450208,0.00003616751,0.00001062044,0.0001851668,0.002104989,0.0004807267,0.0007337509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004721994,"threshold_uncertainty_score":0.009389043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03912327730903112,"score_gpt":0.2872192382814194,"score_spread":0.2480959609723883,"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."}}