Integration of GIS and Artificial Intelligence Algorithms in Rural Landscape Protection and Planning
Bibliographic record
Abstract
This article explored the application of the integration technology of GIS and artificial intelligence (AI) algorithms in rural landscape protection and planning. By analyzing the problems existing in traditional methods, the article elaborated on the necessity and feasibility of combining GIS (Geographic Information System) and CNN (Convolutional Neural Network) algorithms to improve data processing capabilities and strengthen comprehensive analysis capabilities. Through case studies and empirical analysis, the article demonstrated the practical application effect and potential of this fusion technology, providing a new perspective and method for the scientific planning and effective protection of rural landscapes. In the experimental stage, four experiments were designed to evaluate the performance of GIS and CNN fusion. In the first landscape basic feature extraction experiment, the CNN algorithm achieved an accuracy of 95% in extracting features from rural landscape images; the Multi-layer Perceptron algorithm achieved 85%; the RF (Random Forest) achieved an accuracy of 80%; the Support Vector Machine (SVM) achieved 82%. Although the CNN algorithm achieved a processing time of 2 seconds, it had a high accuracy advantage. In the second landscape diversity assessment experiment, the method of integrating GIS and CNN improved species richness by 15%, landscape heterogeneity by 20%, and landscape connectivity by 25%. In landscape change detection experiments, the fusion technology of GIS and CNN has significant advantages in capturing subtle landscape changes. In the experimental data conclusion, the fusion technology of GIS and CNN had a high performance advantage in improving rural planning and management processes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".