{"id":"W2077264955","doi":"10.1016/j.isprsjprs.2013.10.011","title":"Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification","year":2013,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":118,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"York University","keywords":"Point cloud; Computer science; Artificial intelligence; Segmentation; Markov random field; Silhouette; Voxel; Pattern recognition (psychology); Change detection; Remote sensing; Random forest; Computer vision; Image segmentation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005171854,0.0006569295,0.0007020747,0.002774118,0.0005629153,0.001130248,0.0007528924,0.0007006177,0.002513133],"category_scores_gemma":[0.0009492636,0.0004317031,0.0008262958,0.002585788,0.0002967044,0.001419697,0.001106533,0.0007041388,0.00167332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005138487,"about_ca_system_score_gemma":0.001733783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01027936,"about_ca_topic_score_gemma":0.02482443,"domain_scores_codex":[0.9992866,0.00006315705,0.00003850594,0.0001295472,0.0003549125,0.0001273823],"domain_scores_gemma":[0.9992141,0.00008223425,0.00007068728,0.0001844114,0.0004193533,0.00002924035],"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":[0.0002825596,0.0004395104,0.006270906,0.0001697238,0.0001435065,0.0001054103,0.0001741998,0.03238249,0.1117124,0.001695899,0.008282417,0.8383411],"study_design_scores_gemma":[0.00007331119,0.0002322714,0.04848994,0.00008071448,0.0002232566,0.0002295728,0.0005183188,0.8191507,0.1106145,0.00595155,0.01433164,0.0001041861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1973926,0.000846579,0.7835081,0.0004227862,0.0003027745,0.000276279,0.003035919,0.007404322,0.006810521],"genre_scores_gemma":[0.6471708,0.0006162576,0.3404956,0.0001242949,0.0001048489,0.0001918665,0.006645422,0.0002361742,0.004414815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01027936,"threshold_uncertainty_score":0.02043903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01421897985073103,"score_gpt":0.2432656750339406,"score_spread":0.2290466951832096,"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."}}