{"id":"W4402474673","doi":"10.5194/isprs-archives-xlviii-m-4-2024-55-2024","title":"Reconstruction of Building LoD2 Wireframe Models Using Semantic Segmentation","year":2024,"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":"Power Systems and Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Segmentation; Computer science; Computer vision; Artificial intelligence; Natural language processing; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004024806,0.001639683,0.0008470293,0.003331426,0.0003724825,0.001735414,0.001071915,0.0008885835,0.002457694],"category_scores_gemma":[0.0009712763,0.0007977798,0.001861178,0.002335675,0.0004163703,0.0007725404,0.0014369,0.0007146591,0.00190783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007094615,"about_ca_system_score_gemma":0.0009426519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01478405,"about_ca_topic_score_gemma":0.02192921,"domain_scores_codex":[0.999479,0.00004790962,0.00002076738,0.0001451346,0.0002198188,0.00008743779],"domain_scores_gemma":[0.9996229,0.00007692419,0.00004045954,0.0001031063,0.0001287954,0.00002768141],"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":[0.0004808074,0.0002266605,0.01190139,0.0004705163,0.0002917824,0.0009787446,0.0007327794,0.4181055,0.05790772,0.007191712,0.01283684,0.4888756],"study_design_scores_gemma":[0.0000132624,0.0000341552,0.002144401,0.00002436815,0.00002322452,0.0001768859,0.0001523361,0.9773268,0.01305876,0.002099465,0.004922154,0.00002409558],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09549584,0.0002553197,0.8893703,0.0001042668,0.00006643488,0.000184245,0.002861676,0.00916228,0.002499547],"genre_scores_gemma":[0.4695828,0.0004080947,0.5126479,0.0000598962,0.00002628053,0.0001389837,0.01418307,0.001068497,0.001884507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01478405,"threshold_uncertainty_score":0.029396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971818335203979,"score_gpt":0.25240779386968,"score_spread":0.2326896105176402,"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."}}