{"id":"W4410328221","doi":"10.20944/preprints202505.0798.v1","title":"Large-Scale Point Cloud Semantic Segmentation with Density-Based Grid Decimation","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China University of Geosciences; National Natural Science Foundation of China","keywords":"Decimation; Point cloud; Scale (ratio); Segmentation; Computer science; Grid; Cloud computing; Point (geometry); Artificial intelligence; Data mining; Geography; Computer vision; Cartography; Mathematics; Geodesy; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004528469,0.001260705,0.001414757,0.002595854,0.000680893,0.001689303,0.001995396,0.0008171448,0.001880947],"category_scores_gemma":[0.001625262,0.0006128428,0.001373448,0.002527296,0.0006043231,0.00173701,0.002177668,0.001158479,0.001757952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182138,"about_ca_system_score_gemma":0.00134496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01804222,"about_ca_topic_score_gemma":0.0314994,"domain_scores_codex":[0.9993792,0.00004427301,0.00003268665,0.0002034378,0.0002422314,0.00009828141],"domain_scores_gemma":[0.9995024,0.0000952214,0.00004850246,0.0001471996,0.0001720372,0.000034638],"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":[0.0004943752,0.0002604559,0.006760995,0.0003073833,0.0001835477,0.0002584744,0.0003765444,0.2323458,0.02888196,0.007104614,0.01514072,0.7078851],"study_design_scores_gemma":[0.00002711921,0.00003852374,0.002208996,0.00001520323,0.00002099185,0.0001570416,0.0001007601,0.9741189,0.01275264,0.006227653,0.004308456,0.0000238262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05198475,0.000461176,0.9285256,0.0001982167,0.00009213734,0.0001964088,0.001271307,0.01457841,0.002692027],"genre_scores_gemma":[0.3752321,0.0003986025,0.6119138,0.0002019193,0.00007295729,0.0001931925,0.008636435,0.001095791,0.002255222],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01804222,"threshold_uncertainty_score":0.03587443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03172102983349417,"score_gpt":0.2941898139903013,"score_spread":0.2624687841568071,"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."}}