Study on extraction methods of land utilization information based on SPOT5
Bibliographic record
Abstract
【Objective】 The study explored the effect of extracting approach for information of land utilization based on high resolution remote sensing image to provide evidence for studying land utilization and cover dynamic variation.【Method】 This paper extracted the information of land utilization focused on Changjiaoba town,using SVM classification of texture feature and object-oriented classification of multi-resolution segmentation.The classification result was compared with maximum likelihood classification.Then the classification result was analyzed.【Result】 The overall classification accuracy of object-oriented was 90.67%,which increased by 8.34% compared with SVM classification of texture feature and increased by 20.32% compared with maximum likelihood classification.This kind of classification not only can get over the disadvantages of other classifications,e.g.Spectral Similar and Ground-object Fragmentations,etc.but also acquire good effectiveness.【Conclusion】 Using the classification of object-oriented can realize the purpose of extracting the land utilization information,and this method is accurate and fast.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".