{"id":"W4407456984","doi":"10.1109/tgrs.2025.3536169","title":"Enhanced 3-D Urban Scene Reconstruction and Point Cloud Densification Using Gaussian Splatting and Google Earth Imagery","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Point cloud; Cloud computing; Gaussian; Remote sensing; Artificial intelligence; Computer vision; Computer graphics (images); Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002866044,0.0001663829,0.0001572524,0.000146489,0.001096725,0.0001440084,0.00004589199,0.00008967439,0.000006560324],"category_scores_gemma":[0.00001277564,0.0001615317,0.00003254465,0.0004902176,0.0006484058,0.0002725391,0.000007043256,0.0002064988,0.00000751695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006506329,"about_ca_system_score_gemma":0.00002495886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008704622,"about_ca_topic_score_gemma":0.0001807282,"domain_scores_codex":[0.9987171,0.00006038798,0.0002405491,0.0005630139,0.0001531324,0.0002658502],"domain_scores_gemma":[0.9994789,0.00007378324,0.00008540153,0.0002297558,0.00002009109,0.0001120981],"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":[0.000009605291,0.000007492715,0.00001950996,0.000008751512,0.000004241873,0.000001114177,0.0003500647,0.0001888637,0.2496195,0.000006901846,0.000004786657,0.7497792],"study_design_scores_gemma":[0.000446242,0.00005576425,0.008762361,0.0003914181,0.00008864819,0.0004670161,0.0009306843,0.7856723,0.2016101,0.0007921745,0.0003577777,0.000425458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5284513,0.0000267553,0.4700007,0.00025061,0.0002025739,0.0001173325,0.000001248141,0.00003620672,0.0009133173],"genre_scores_gemma":[0.8846989,0.0001340313,0.1144611,0.0001477078,0.00002869655,7.765446e-8,4.553252e-7,0.000009444362,0.0005195299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7854835,"threshold_uncertainty_score":0.8435232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002820026210475,"score_gpt":0.2332029308195895,"score_spread":0.2231747305574848,"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."}}