{"id":"W4407867799","doi":"10.23977/acss.2025.090102","title":"A Comparative Study of Deep Learning-Based Semantic Segmentation Methods for High-Resolution Remote Sensing Imagery","year":2025,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"High resolution; Segmentation; Computer science; Deep learning; Remote sensing; Artificial intelligence; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001859165,0.001103807,0.0006991582,0.002604171,0.0004423384,0.001330159,0.001147545,0.001248261,0.002226416],"category_scores_gemma":[0.003423329,0.000323022,0.0009289905,0.001598835,0.0004975167,0.002457783,0.0007324489,0.0007843426,0.0008342699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001102986,"about_ca_system_score_gemma":0.0011506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009571208,"about_ca_topic_score_gemma":0.01359606,"domain_scores_codex":[0.999263,0.0001105914,0.00005420249,0.0001806603,0.0002874885,0.0001041263],"domain_scores_gemma":[0.9988756,0.0004150344,0.0000819842,0.0001509848,0.0004103078,0.00006606358],"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":[0.0008972269,0.0002991051,0.006347431,0.0005258626,0.0003078941,0.0001107265,0.0001424084,0.1496654,0.01576502,0.004625455,0.007310776,0.8140026],"study_design_scores_gemma":[0.00001773673,0.0001676334,0.004785862,0.00005790064,0.00006160236,0.00009605818,0.00009693731,0.9779084,0.01063233,0.002265105,0.003881996,0.0000284871],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.368818,0.01576215,0.5796188,0.001378146,0.0006319156,0.0002590874,0.001824597,0.005977535,0.02572973],"genre_scores_gemma":[0.7473887,0.004170764,0.2335911,0.0003615647,0.0001261295,0.00009480333,0.005215845,0.0004595344,0.008591511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009571208,"threshold_uncertainty_score":0.01903099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02707748939167514,"score_gpt":0.3417005966670892,"score_spread":0.3146231072754141,"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."}}