Optimising Territorial Spatial Planning Using Image Recognition Technology and Monitoring the Direction of Coordinated Development of Urbanisation
Bibliographic record
Abstract
With the accelerating process of urbanization development, it is urgent to optimize the national land spatial planning to promote the coordinated development of urbanization.Based on the image recognition technology, this study uses the kernel density gradient algorithm to segment the image samples of the national spatial layout and the GWO-SVM classi ication model to classify the land use types of the national spatial layout, and inally combines the Markov-FLUS model to predict the future planning of the existing national spatial layout.The research analysis found that the segmentation and classi ication accuracy of the kernel density gradient algorithm and the GWO-SVM classi ication model for the homeland spatial layout samples both reached more than 90%.The classi ication accuracy using the GWO-SVM classi ication model is improved to a greater extent than that of SVM, GA-SVM, etc.The Markov-FLUS model also maintains an accuracy of more than 80% for the prediction of future territorial spatial planning.In terms of land use types, the Markov-FLUS model shows that the proportion of residential land and industrial land will decrease after 10 years compared with 5 years, while the proportion of public facilities land will increase by about 8% after 10 years compared with 5 years.The optimization of national spatial layout is of great signi icance to the development of urbanization in China, and the research in this paper will promote the development of national spatial layout planning in a more reasonable direction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
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".