{"id":"W4390121805","doi":"10.1016/j.jag.2023.103623","title":"Shape-preserving mesh decimation for 3D building modeling","year":2023,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing; Qinglan Project of Jiangsu Province of China; Wuhan University; Anhui Provincial Department of Education; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China","keywords":"Polygon mesh; Computer science; Leverage (statistics); Decimation; Process (computing); Segmentation; Point cloud; Semantics (computer science); Building model; Theoretical computer science; Algorithm; Topology (electrical circuits); Artificial intelligence; Mathematics; Computer vision; Computer graphics (images); Programming language","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":[0.0004976865,0.0007880631,0.0006695508,0.001062101,0.0004243942,0.001039931,0.001310589,0.0007500934,0.002037665],"category_scores_gemma":[0.001720028,0.0005832082,0.001180473,0.0008085469,0.0006412477,0.001222469,0.00133397,0.001432016,0.0009679349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004079854,"about_ca_system_score_gemma":0.0005466286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001593011,"about_ca_topic_score_gemma":0.002439892,"domain_scores_codex":[0.9995085,0.00006350208,0.00002038754,0.00008325599,0.0002775207,0.00004688266],"domain_scores_gemma":[0.9994135,0.0001920372,0.00005776134,0.0002222175,0.00008955172,0.0000248373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001385449,0.00008401209,0.001324922,0.0002388354,0.00009304189,0.0002804162,0.000436993,0.500551,0.08083913,0.02968609,0.003160116,0.3831669],"study_design_scores_gemma":[0.000007339411,0.00003082578,0.0002792976,0.00001108437,0.00001410107,0.000216167,0.00004528881,0.9615168,0.02356356,0.007993679,0.006304319,0.00001741835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006555877,0.00007604054,0.9917331,0.00003787838,0.00001611787,0.00002261153,0.00003431173,0.0006404351,0.0008836568],"genre_scores_gemma":[0.1949929,0.0003282265,0.8009124,0.000108613,0.00004035529,0.00008032913,0.0004392347,0.0006149042,0.00248304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002037665,"threshold_uncertainty_score":0.006816685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02725161671053004,"score_gpt":0.2667715725783929,"score_spread":0.2395199558678629,"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."}}