{"id":"W3197083592","doi":"10.3390/rs13173448","title":"High-Resolution Terrain Modeling Using Airborne LiDAR Data with Transfer Learning","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Basic Public Welfare Research Program of Zhejiang Province; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Digital elevation model; Lidar; Terrain; Artificial intelligence; Point cloud; Remote sensing; Mean squared error; Transfer of learning; Elevation (ballistics); Convolutional neural network; Feature (linguistics); Workflow; Interpolation (computer graphics); Pattern recognition (psychology); Digital surface; Feature extraction; Computer vision; Image (mathematics); Geology; Mathematics; Cartography; Geography; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000329744,0.0002008378,0.0002123676,0.00004012231,0.000497919,0.00009048622,0.0001654764,0.00009605553,0.00005057027],"category_scores_gemma":[0.00005439677,0.0001945397,0.00004274255,0.0004679678,0.0001069422,0.0002490885,0.0001898168,0.0003514695,0.00007122943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001757018,"about_ca_system_score_gemma":0.00005356017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002971171,"about_ca_topic_score_gemma":0.0005447079,"domain_scores_codex":[0.9981084,0.000152115,0.0002583711,0.0007111619,0.0003699118,0.0004000403],"domain_scores_gemma":[0.9989222,0.00003882243,0.00004457295,0.0008457787,0.00003177374,0.0001168535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001498491,0.0000142303,0.00001977401,0.000007227864,0.00001941607,0.00005658875,0.0004407949,0.7094945,0.1480083,0.00001053063,0.00001687559,0.1418968],"study_design_scores_gemma":[0.0002594477,0.00001607929,0.00008182388,0.0001056467,0.00005943233,0.0003088198,0.0003240154,0.9919254,0.004673709,0.0001308471,0.001851803,0.0002630115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5311241,0.00003068015,0.4671758,0.0002719414,0.00004179659,0.00006906926,0.000002289757,0.00008232069,0.001202061],"genre_scores_gemma":[0.7956839,0.00001548418,0.2038037,0.0001054349,0.0001063803,3.373146e-9,0.00008082006,0.00004073882,0.0001635055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2824309,"threshold_uncertainty_score":0.7933098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984382139231386,"score_gpt":0.250808908381963,"score_spread":0.2209650869896491,"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."}}