{"id":"W3177197229","doi":"10.5194/isprs-archives-xliii-b3-2021-61-2021","title":"INDIVIDUAL TREE CROWN DELINEATION FROM HIGH SPATIAL RESOLUTION IMAGERY USING U-NET","year":2021,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Deciduous; Crown (dentistry); Tree (set theory); Net (polyhedron); Residual; Artificial intelligence; Boundary (topology); Computer science; Pixel; Forestry; Mathematics; Geography; Algorithm; Ecology; Geometry; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000437993,0.0004244944,0.0002569204,0.001773245,0.0002823039,0.0007468794,0.0005284421,0.0003837982,0.001125822],"category_scores_gemma":[0.0007283621,0.0001504856,0.0002565041,0.0008829206,0.0001808714,0.0007241043,0.0004756738,0.0003102695,0.0004620769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006081632,"about_ca_system_score_gemma":0.0004717923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02249666,"about_ca_topic_score_gemma":0.06229889,"domain_scores_codex":[0.9998779,0.00001362365,0.000005546568,0.00003835539,0.00003601245,0.0000284557],"domain_scores_gemma":[0.9996822,0.00005717399,0.00004301408,0.00003515884,0.0001580358,0.00002441045],"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":[0.0009063652,0.0002789862,0.08996672,0.0002416058,0.0002138546,0.0004003167,0.0003157113,0.2132328,0.06578759,0.001864084,0.006393331,0.6203986],"study_design_scores_gemma":[0.000008923219,0.00002808138,0.03064681,0.00003051439,0.00003102811,0.00007765683,0.0001234803,0.9534537,0.01354057,0.0006650883,0.001376021,0.00001818386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8291082,0.0007842231,0.1564967,0.0001142956,0.00005733279,0.00009597975,0.001823377,0.003306874,0.008212999],"genre_scores_gemma":[0.9171593,0.0001069969,0.07981102,0.00004683783,0.000009659763,0.00001918195,0.001436516,0.00006558079,0.001345056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02249666,"threshold_uncertainty_score":0.04473144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01951874505599682,"score_gpt":0.2527743515563505,"score_spread":0.2332556065003537,"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."}}