Large-Scale Urban Scene Reconstruction Using 3D Gaussian Splatting and Satellite Imagery
Bibliographic record
Abstract
With the rapid development of deep learning techniques, the construction of digital twin cities has become an important direction for developing digital earth. Thus, in this paper, we perform scene reconstruction using the 3D Gaussian Splatting (3DGS) method and satellite imagery in complex and large-scale urban downtown areas. Experimental results show that 3DGS significantly outperformed Neural Radiance Fields (NeRF) in both reconstruction accuracy and efficiency, improving approximately 10–13 dB in PSNR, 0.3–0.4 in SSIM, and a reduction of 0.1 in LPIPS. Moreover, 3DGS exhibited high robustness under multi-period lighting conditions, with most quantitative metric fluctuations remaining below 2%. This paper highlights the potential of 3DGS in panoramic urban modeling and multi-scene analysis for digital twins, providing efficient and reliable technical support for future urban planning and management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".