{"id":"W3081634424","doi":"10.3390/rs12172849","title":"Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Lidar; Computer science; Remote sensing; Photogrammetry; Intersection (aeronautics); Consistency (knowledge bases); Point cloud; Constraint (computer-aided design); Correctness; Point (geometry); Artificial intelligence; Feature extraction; Triangulated irregular network; Cluster analysis; Pattern recognition (psychology); Mathematics; Geography; Algorithm; Cartography; Geometry","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":[0.0005815388,0.001145095,0.001266086,0.002842712,0.0004694159,0.001149039,0.001201708,0.0006930649,0.001818494],"category_scores_gemma":[0.00149501,0.0004980988,0.00142188,0.002797895,0.000343965,0.001349959,0.001181249,0.001016238,0.001535991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000243788,"about_ca_system_score_gemma":0.0007579052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003709614,"about_ca_topic_score_gemma":0.006489243,"domain_scores_codex":[0.9989401,0.00006569676,0.00007630442,0.0002260353,0.0005501316,0.0001416804],"domain_scores_gemma":[0.9989294,0.0001594538,0.00008958922,0.0002063558,0.0005855086,0.00002969046],"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":[0.0002998009,0.0001886169,0.004924944,0.0004372122,0.0001187748,0.0002687514,0.0002158387,0.02895931,0.1637958,0.001269846,0.004460256,0.7950608],"study_design_scores_gemma":[0.00006010825,0.0003341837,0.02745452,0.00005711231,0.0001188871,0.001060358,0.0004678011,0.7282172,0.2235525,0.003681826,0.01485839,0.0001371753],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09436312,0.0005122911,0.8963043,0.0001066043,0.00008552447,0.0002223676,0.001611582,0.005222201,0.001571969],"genre_scores_gemma":[0.1921882,0.0003578462,0.797746,0.00006627423,0.00004147142,0.0002389077,0.007856563,0.0003204236,0.001184274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003709614,"threshold_uncertainty_score":0.007376015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02381728064876228,"score_gpt":0.2656771740942539,"score_spread":0.2418598934454916,"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."}}