Digital Reconstruction of Historical Cultural Landscapes Based on Image Recognition Technology
Bibliographic record
Abstract
With the advancement of modern society and technology, the preservation and inheritance of historical cultural landscapes have become increasingly significant.These landscapes not only testify to the development of human civilization but are also an essential part of cultural heritage.However, the ravages of time, natural disasters, and human activities continually threaten these valuable cultural assets.To better preserve and pass on these landscapes, digital reconstruction using technological means has become a crucial method.The rapid development of image recognition technology offers new possibilities and solutions for the digital reconstruction of historical cultural landscapes.Although current digital reconstruction methods have improved in automation, they still require enhancements in recognition accuracy and three-dimensional reconstruction effects in complex scenes.Furthermore, the performance of existing methods in handling multi-scale and multiperspective issues is not satisfactory.Therefore, this paper proposes a digital reconstruction method for historical cultural landscapes based on image recognition technology, comprising two main parts: historical cultural landscape target recognition based on Multi-Scale Dilated Convolution YOLOv3 (MSDC-YOLOv3) and three-dimensional reconstruction of historical cultural landscapes based on pyramid feature attention Pixel2Mesh.The MSDC-YOLOv3 technique enables more precise recognition of objects within historical cultural landscapes against complex backgrounds, while the pyramid feature attention Pixel2Mesh method achieves more efficient and accurate 3D reconstruction, providing detailed three-dimensional models.This research not only achieves technical breakthroughs, enhancing the precision and efficiency of recognition and reconstruction, but also holds significant value in the protection and inheritance of cultural heritage, offering new ideas and methods for future research in related fields.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".