{"id":"W4225899013","doi":"10.1016/j.jag.2022.102764","title":"Detecting and mapping tree crowns based on convolutional neural network and Google Earth images","year":2022,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Guangxi Key Research and Development Program; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Convolutional neural network; Cartography; Geography; Tree (set theory); Artificial intelligence; Remote sensing; Computer science; Pattern recognition (psychology); Forestry; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002114775,0.0006603015,0.0002295584,0.001479905,0.0001822463,0.000378296,0.0003989947,0.0003146098,0.0006422413],"category_scores_gemma":[0.0003739508,0.0001622575,0.000407021,0.000755645,0.0001614737,0.0007186846,0.0004717478,0.0002507965,0.0002648606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000362266,"about_ca_system_score_gemma":0.000419479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02414897,"about_ca_topic_score_gemma":0.06176024,"domain_scores_codex":[0.9998183,0.00001207825,0.00000749634,0.00005299744,0.00006329923,0.00004586861],"domain_scores_gemma":[0.9998742,0.00001585046,0.00002023017,0.00001773156,0.00006037731,0.00001142575],"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":[0.0003797831,0.0003940966,0.1364747,0.0003437811,0.0003416625,0.0007608092,0.0002683868,0.1273113,0.1175124,0.00228426,0.007522804,0.606406],"study_design_scores_gemma":[0.000008236972,0.00006730793,0.08518945,0.0000250838,0.00007323534,0.0001668127,0.0001475599,0.8927609,0.01891558,0.0007313711,0.001885306,0.00002916909],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.84785,0.0006178839,0.1414591,0.0001550936,0.00009202692,0.0001233341,0.002240575,0.002536094,0.004925738],"genre_scores_gemma":[0.9458326,0.0002631267,0.05019012,0.00004532271,0.00001436299,0.00002597839,0.002127053,0.00003590468,0.001465619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02414897,"threshold_uncertainty_score":0.04801685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01252429748475339,"score_gpt":0.2085291887075412,"score_spread":0.1960048912227878,"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."}}