Research and Application of Boundary Optimization Algorithm of Forest Resource Vector Data Based on Convolutional Neural Network
Bibliographic record
Abstract
With the deepening of the research and application of GIS (Geographic Information System), the boundary quality of GIS data is becoming more and more important for production and application, and the high accuracy of vector data is the trend of future development. The second-class survey results have the characteristics of accurate and reliable survey data, rich contents and diverse expressions, and can establish forest resources archives for local areas. Therefore, this study established a boundary optimization algorithm for vector data of forest resources based on CNN (Convolutional Neural Network). The network structure proposed in this paper consists of encoder and decoder, and the input dimension of source data is 64×64. The encoder consists of CL(Convolution layer), active layer and PL(Pool layer). The dimension of CL is 24×64×256, which respectively represents the width of convolution kernel, the number of input data columns and the number of convolution kernels. The results show that the vector data processed by the algorithm basically keeps the original shape unchanged, and reduces the redundancy of vector data. At the same time, from the test results, CNN has achieved the best results. Compared with GA (Genetic Algorithm), the accuracy of this method is 3.6% higher. The results verify the reliability of this algorithm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".