Application of Satellite Images and Artificial Intelligence to Monitor Land Cover Changes in Hanoi Area During 2013-2023 Period
Bibliographic record
Abstract
Artificial intelligence (AI) and remote sensing technology have now increasingly improved their efficiency and reliability in monitoring the changes in land cover. With the amendment of the Vietnamese Law on Land in 2013 and the administrative boundary expansion of Hanoi, Hanoi experiences significant changes in land use and land cover for the last ten years. To monitor the actual land use changes in the area, this study used the Random Forest (RF) machine learning algorithm to classify the basic land covers, monitor, and analyze the spatial variation of land use and land cover in the 2013 to 2023 period. The study findings indicate a relatively high rate of expansion of construction zone area and a decrease in land cover related to water bodies and vegetated area. Water bodies decrease by an average of 0.8% annually, whereas the construction zone area increased by 7% of the total area.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".