{"id":"W4281776681","doi":"10.5194/isprs-archives-xliii-b3-2022-983-2022","title":"IMPROVING THE BINARY CLASSIFICATION OF PEAT LOCALITIES FROM MULTI-SOURCE REMOTELY-SENSED DATA USING CNN","year":2022,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Japan Aerospace Exploration Agency; Ontario Ministry of Agriculture, Food and Rural Affairs; Natural Resources Canada; Ministry of Agriculture, Food and Rural Affairs; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Natural Resources and Forestry","keywords":"Peat; Support vector machine; Convolutional neural network; Random forest; Computer science; Remote sensing; Multispectral image; Binary classification; Artificial neural network; Artificial intelligence; Pixel; Sampling (signal processing); Taiga; Pattern recognition (psychology); Environmental science; Geography; Forestry; Computer vision","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":[0.0005618666,0.0007536896,0.0003306053,0.001045009,0.000205045,0.0006580898,0.0005155061,0.0003976338,0.0009197477],"category_scores_gemma":[0.001185213,0.0002091894,0.0003549139,0.0006166482,0.0001865266,0.0007332458,0.0005048183,0.0003490341,0.0004235949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005912827,"about_ca_system_score_gemma":0.00042427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0215813,"about_ca_topic_score_gemma":0.02862519,"domain_scores_codex":[0.9998401,0.00002122392,0.000009968879,0.00004660066,0.00003707817,0.00004492916],"domain_scores_gemma":[0.9995012,0.0001525072,0.0000734392,0.00004533809,0.0002034879,0.00002406721],"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":[0.0005750422,0.0002766494,0.05041615,0.000198815,0.0001935442,0.0002950539,0.0001426751,0.2581415,0.04892625,0.0009532347,0.003184235,0.6366969],"study_design_scores_gemma":[0.000005634916,0.00003101522,0.01106199,0.00001759536,0.00002792879,0.00002991045,0.00005806554,0.9805822,0.00740981,0.000401846,0.0003656289,0.000008354372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7914433,0.0004864568,0.2013952,0.0002154507,0.00009317852,0.00008264722,0.0007588386,0.001290965,0.004233843],"genre_scores_gemma":[0.9645237,0.00009254932,0.03336582,0.00004214133,0.00001941384,0.00001918322,0.0006355058,0.00001993503,0.001281667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0215813,"threshold_uncertainty_score":0.04291135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03773879518492747,"score_gpt":0.2603793389810541,"score_spread":0.2226405437961267,"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."}}