Bibliographic record
Abstract
Change detection plays a crucial role in identifying differences between multi-temporal images captured over the same geographical area, with applications spanning various fields including urban planning, environmental monitoring, and disaster assessment. However, challenges persist in handling bitemporal images with viewpoint difference, affecting the performance of traditional change detection models. To address these challenges, this paper proposes a novel end-to-end optical flow alignment change detection (PFCD) model. The PFCD model integrates optical flow estimation technology, enabling direct change detection in images with viewpoint differences without the need for a separate image registration model. Through end-to-end training, the model achieves higher detection accuracy and faster processing speeds. Experimental results on the scattered garbage regions change detection dataset (SGRCD-VD) and the building change detection dataset (WHUCD-VD) validate the effectiveness of the model. On the SGRCD-VD test set, the PFCD model achieves an F1 score of 91.00%, while on the WHUCD-VD test set, it reaches 94.82%, demonstrating excellent performance in handling images with viewpoint differences. Additionally, the model exhibits advantages in processing speed and model parameter.
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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) | 0.000 | 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".