{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001739013,0.0004145684,0.000384906,0.0005372475,0.001834702,0.0009148292,0.001587043,0.0001032008,0.00001612329],"category_scores_gemma":[0.0005898327,0.0002829038,0.0002016819,0.0008218184,0.003064687,0.001119983,0.001899645,0.0003943881,0.000004639225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008807951,"about_ca_system_score_gemma":0.0001356486,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7766697,"about_ca_topic_score_gemma":0.09378488,"domain_scores_codex":[0.9956876,0.0003107129,0.001278761,0.0004981738,0.001645093,0.000579675],"domain_scores_gemma":[0.9968626,0.0006804523,0.001436667,0.0007065397,0.0001071807,0.0002065563],"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":[0.00004342907,0.00002103095,0.0004123257,0.00001692279,0.00006297458,1.483831e-7,0.003577784,0.005554748,0.02930655,0.000006753305,0.00006824235,0.9609291],"study_design_scores_gemma":[0.000473279,0.00004560459,0.001931096,0.0001935648,0.00008315318,0.0001210672,0.001011388,0.9826755,0.008001127,0.002818758,0.002321796,0.0003236581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1448916,0.00003263989,0.8478961,0.001726804,0.001156359,0.0005127451,0.0001503043,0.00004485,0.003588598],"genre_scores_gemma":[0.9529384,0.0000923245,0.04589271,0.0006907175,0.0002363658,2.248541e-7,0.00009440378,0.0000153836,0.0000394332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9771208,"threshold_uncertainty_score":0.9999623,"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."}}