{"id":"W4390638490","doi":"10.1111/phor.12476","title":"Building extraction from oblique photogrammetry point clouds based on <scp>PointNet</scp>++ with attention mechanism","year":2024,"lang":"en","type":"article","venue":"The Photogrammetric Record","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Point cloud; Computer science; Artificial intelligence; Oblique case; Photogrammetry; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000836311,0.0003473577,0.0002496821,0.0003745926,0.0003605574,0.0003689033,0.0003970719,0.0001623026,0.000318121],"category_scores_gemma":[0.000152973,0.0002383013,0.0002207274,0.003597788,0.0001425724,0.0001639244,0.00008245963,0.0006415403,0.0008390797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003644562,"about_ca_system_score_gemma":0.0000340943,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02452166,"about_ca_topic_score_gemma":0.0007710031,"domain_scores_codex":[0.9974282,0.000192133,0.0003687015,0.0008068961,0.0006733116,0.0005308065],"domain_scores_gemma":[0.9976421,0.001174821,0.0001643972,0.0008070968,0.00002277399,0.0001888141],"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":[0.00007131133,0.0004459495,0.00121873,0.00002655401,0.0001276974,0.00004769673,0.0002715884,0.001510593,0.139386,0.0007868447,0.0073243,0.8487828],"study_design_scores_gemma":[0.001105188,0.001121543,0.01800153,0.00033174,0.0004167418,0.0001113485,0.0007403286,0.6315519,0.1056855,0.02649712,0.2138062,0.0006308751],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5477937,0.00005091031,0.4410689,0.0004103188,0.0005663138,0.000720721,0.0000195515,0.000413905,0.008955587],"genre_scores_gemma":[0.9752507,0.00005412412,0.02322066,0.0004644513,0.0002133614,0.00005830825,0.00004158816,0.00007721173,0.0006195659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8481519,"threshold_uncertainty_score":0.9999389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079848968408282,"score_gpt":0.2428074014696195,"score_spread":0.2320089117855366,"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."}}