{"id":"W4389656768","doi":"10.5194/isprs-archives-xlviii-1-w2-2023-171-2023","title":"MONOCULAR DEPTH ESTIMATION FOR NIGHT-TIME IMAGES","year":2023,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Calgary","keywords":"Computer science; Task (project management); Estimation; Monocular; Artificial intelligence; Synthetic data; Image (mathematics); Translation (biology); Daytime; Computer vision; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003288738,0.0006876922,0.0003304862,0.0005109573,0.0001988552,0.0004935034,0.0007732294,0.0005669057,0.00309264],"category_scores_gemma":[0.00158523,0.0002226415,0.0004324824,0.0004789214,0.0002610957,0.0006144819,0.0005930105,0.0007049349,0.0007529635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005209593,"about_ca_system_score_gemma":0.0006019486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007102062,"about_ca_topic_score_gemma":0.01207381,"domain_scores_codex":[0.9997054,0.00004844356,0.000007691553,0.0001142416,0.0000772471,0.00004689011],"domain_scores_gemma":[0.9995711,0.000101256,0.00004789183,0.0001258523,0.0001221174,0.00003172335],"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":[0.001080339,0.0003842459,0.01045921,0.0004789676,0.0002159654,0.0003119508,0.0001873812,0.2827682,0.1181055,0.001894214,0.01338852,0.5707255],"study_design_scores_gemma":[0.0000255453,0.0001411041,0.0107149,0.00003125199,0.00002014402,0.000213482,0.00006650908,0.9369024,0.04719037,0.001162227,0.00350924,0.00002278971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.497025,0.001091791,0.481144,0.0004100837,0.000311241,0.0002296237,0.004672935,0.006230392,0.008884941],"genre_scores_gemma":[0.8899578,0.0002218253,0.1032138,0.0001112648,0.00003621047,0.00004498209,0.0035337,0.0001774648,0.002703043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007102062,"threshold_uncertainty_score":0.01412147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781346467797845,"score_gpt":0.2779797368820581,"score_spread":0.2601662722040796,"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."}}