{"id":"W3160829204","doi":"10.3390/rs13101946","title":"Reconstruction of Complex Roof Semantic Structures from 3D Point Clouds Using Local Convexity and Consistency","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing Advanced Innovation Center for Future Urban Design; China Postdoctoral Science Foundation; National Natural Science Foundation of China; York University; Beijing University of Civil Engineering and Architecture; Center for Urban Science and Progress","keywords":"Computer science; Point cloud; Roof; Photogrammetry; Lidar; Consistency (knowledge bases); Ranging; Benchmark (surveying); Data mining; Graph; Representation (politics); Segmentation; Parametric statistics; Cut; Level of detail; Algorithm; Artificial intelligence; Theoretical computer science; Remote sensing; Image segmentation; Geology; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001213304,0.000146218,0.0002720341,0.00002666524,0.0002096899,0.00003365764,0.00004515028,0.00009565517,0.0001466905],"category_scores_gemma":[0.00005418036,0.0001517497,0.00005958078,0.0002012233,0.0006242425,0.00006987102,0.0001116859,0.0001459325,0.00001034278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000998238,"about_ca_system_score_gemma":0.00003681957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004472351,"about_ca_topic_score_gemma":0.0004126769,"domain_scores_codex":[0.9987664,0.00012276,0.0003281037,0.0003883511,0.0002008081,0.0001935544],"domain_scores_gemma":[0.9992655,0.00009129351,0.00016042,0.0003526835,0.00004003685,0.0000900907],"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":[0.000009482369,0.000007763214,0.0004445231,0.00001481963,0.00002506444,0.0000169575,0.0003176777,0.001624952,0.4293843,0.00003305617,0.00002754777,0.5680939],"study_design_scores_gemma":[0.0003470464,0.00001450133,0.01578521,0.0001085971,0.00008833208,0.001026512,0.0008832349,0.9037114,0.06643274,0.01103489,0.0003156503,0.000251886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8674873,0.00008076445,0.1290619,0.0001523848,0.0001337874,0.0000847789,0.00000762742,0.00003130623,0.002960186],"genre_scores_gemma":[0.8279166,0.00001681653,0.1718906,0.00009063209,0.00004289392,1.356162e-9,0.0000119812,0.000013108,0.00001742764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9020864,"threshold_uncertainty_score":0.6760886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02441841236845568,"score_gpt":0.2479079644594283,"score_spread":0.2234895520909727,"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."}}