{"id":"W2982815917","doi":"10.1109/igarss.2019.8899016","title":"Experiment on the Impact of Spatial Resolution on Building Extraction Accuracy","year":2019,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Image resolution; Computer science; Context (archaeology); Extraction (chemistry); Spatial contextual awareness; Ranging; Remote sensing; Resolution (logic); Identification (biology); Scale (ratio); Process (computing); Artificial intelligence; Geography; Cartography; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001473096,0.0003102701,0.0002563787,0.0004317797,0.0003961088,0.0004754116,0.0003408574,0.0004219412,0.001115505],"category_scores_gemma":[0.006887818,0.0002173792,0.0002535056,0.0006376673,0.0003768755,0.0005963166,0.000456639,0.000274367,0.0002746403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004229699,"about_ca_system_score_gemma":0.0003166189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02029251,"about_ca_topic_score_gemma":0.02231948,"domain_scores_codex":[0.9991546,0.0001913291,0.00008580503,0.0001789521,0.0002872937,0.0001021118],"domain_scores_gemma":[0.9932379,0.00449801,0.0002530498,0.000535298,0.001401643,0.00007418701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.008183996,0.0009145454,0.1494277,0.0004426047,0.0003500113,0.0008763725,0.001041046,0.07784174,0.6133118,0.0005623722,0.0008293253,0.1462185],"study_design_scores_gemma":[0.0001267241,0.002178416,0.3991346,0.00004729124,0.0002844391,0.0007950296,0.0008242996,0.1352281,0.4588803,0.0003698817,0.002045146,0.0000855351],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930251,0.00009138765,0.005594167,0.00002359109,0.000006403442,0.00003145624,0.0002308764,0.00009879646,0.0008982635],"genre_scores_gemma":[0.9821132,0.00004942096,0.01692385,0.00002787134,0.000003580619,0.00001911368,0.0003724655,0.00002888313,0.0004616229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02029251,"threshold_uncertainty_score":0.04034883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01863161844681947,"score_gpt":0.3105287429053295,"score_spread":0.29189712445851,"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."}}