A Temporal Correlation Networks Based on Interactive Modelling for Remote Sensing Images Change Detection
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Change detection identifies dynamic changes in surface cover and feature status by comparing remote sensing images at different points in time, which is of wide application value in the fields of disaster early warning, urban management and ecological monitoring. Mainstream datasets are dominated by long‐term datasets; to support short‐term change detection, we collected a new dataset, HNU‐CD, which contains some small and hard‐to‐identify change regions. A time correlation network (TCNet) is also proposed to address these challenges. First, foreground information is enhanced by interactively modelling foreground relations, while background noise is smoothed. Secondly, the temporal correlation between bit‐time images is utilised to refine the feature representation and minimise false alarms due to irrelevant changes. Finally, a U‐Net inspired architecture is adapted for dense upsampling to preserve details. TCNet demonstrates excellent performance on both the HNU‐CD (Hainan University change detection dataset) dataset and three widely used public datasets, indicating that its generalisation capabilities have been enhanced. The ablation experiments provide a good demonstration of the ability to reduce the impact caused by pseudo‐variation through temporal correlation modelling.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it