{"id":"W4391594024","doi":"10.1109/jstars.2024.3363160","title":"LS-YOLO: A Novel Model for Detecting Multiscale Landslides With Remote Sensing Images","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Ministry of Natural Resources of the People's Republic of China; Ministry of Natural Resources","keywords":"Landslide; Computer science; Robustness (evolution); Remote sensing; Scale (ratio); Artificial intelligence; Feature extraction; Convolution (computer science); Object detection; Pooling; Pattern recognition (psychology); Data mining; Computer vision; Artificial neural network; Geology; Cartography; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0003732302,0.0009416367,0.0006109317,0.0006890098,0.000220736,0.0006604723,0.00189866,0.0008049334,0.001830808],"category_scores_gemma":[0.0008474123,0.000422473,0.0009566106,0.0004189374,0.0003388361,0.0009753216,0.0008375267,0.0008958734,0.0008533669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005813133,"about_ca_system_score_gemma":0.0007981795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01372585,"about_ca_topic_score_gemma":0.02067609,"domain_scores_codex":[0.9998479,0.00001568747,0.000006310168,0.00007191653,0.00002707407,0.00003114955],"domain_scores_gemma":[0.9998199,0.00005087592,0.00002821248,0.00002494422,0.00005849163,0.00001750979],"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":[0.0005969825,0.0002561804,0.008248768,0.0002823435,0.0003385601,0.0002993214,0.0001828637,0.4654498,0.05419572,0.004862183,0.01295534,0.4523318],"study_design_scores_gemma":[0.000004961431,0.00002614829,0.000624783,0.000006004735,0.00001802986,0.00002545825,0.00000806905,0.9957799,0.002035457,0.0006360139,0.0008278099,0.000007295531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05494114,0.001117996,0.9347942,0.0003956237,0.0001644628,0.0001330967,0.0009167786,0.00521723,0.002319432],"genre_scores_gemma":[0.6763908,0.001209408,0.3019848,0.001084189,0.0002631237,0.000397306,0.003359822,0.0006276431,0.01468287],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01372585,"threshold_uncertainty_score":0.02729189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02131195793178067,"score_gpt":0.2363936990325479,"score_spread":0.2150817411007672,"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."}}