{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003296894,0.0006756512,0.0003299891,0.0009893036,0.0002903559,0.0005569657,0.0009784059,0.000529945,0.001262108],"category_scores_gemma":[0.001042731,0.0003860006,0.0008041177,0.0009271989,0.0002584491,0.001096557,0.0007794912,0.0006253959,0.0005866295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005969953,"about_ca_system_score_gemma":0.0007296872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01058542,"about_ca_topic_score_gemma":0.01291198,"domain_scores_codex":[0.9997879,0.0000228049,0.000009625463,0.00006297259,0.00008881897,0.00002796319],"domain_scores_gemma":[0.9997752,0.00004471181,0.00003030365,0.0000685446,0.0000691499,0.00001213545],"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":[0.00005792288,0.00007828531,0.003903087,0.00008275969,0.00006923574,0.0001954276,0.0001048275,0.694128,0.01692544,0.002045413,0.001032191,0.2813773],"study_design_scores_gemma":[0.000002051112,0.00000783487,0.0004668917,0.000003155573,0.000003410943,0.00002284039,0.00001277172,0.996134,0.002165961,0.0007444362,0.0004325393,0.000004088505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09290057,0.0001701186,0.9010538,0.0001241313,0.00003860039,0.00007590542,0.0002965427,0.003079309,0.002260966],"genre_scores_gemma":[0.6971008,0.0001611043,0.3003319,0.00004687039,0.00002190272,0.00007577788,0.0007878127,0.000153993,0.001319855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01058542,"threshold_uncertainty_score":0.02104765,"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."}}