Scale- and Shape-Aware Network With Prediction Decoupling for Building Fine-Grained Change Detection
Why this work is in the frame
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Bibliographic record
Abstract
Building change detection (BCD) is a hot topic in geoscience and remote sensing (RS) with widespread applications. However, most existing BCD methods only focus on areas where changes have occurred, but ignore the change statuses. To address this problem, a building fine-grained change detection (BFCD) task is further explored in this work, which aims to judge the time-related “disappeared”, “appeared”, and “rebuilt” change types of buildings. Meanwhile, a scale- and shape-aware network (S2Net) with prediction decoupling is designed. Firstly, a prediction decoupling framework with dual decoders is built to ensure the prediction consistency with the temporal order of bi-temporal images. Secondly, considering the rebuilt type is the changes between building instances, which are often reflected in the scale and shape differences of the buildings. Thereby, a scale-aware module (ScAM) and a shape-aware module (ShAM) are designed. These two modules help extract the discriminative features of buildings with different scales and shapes for subsequent change detection (CD). In addition, two BCD datasets widely used, LEVIR-CD+ and WHU-CD, are relabeled in this work to support the study of BFCD. Experimental results show that S2Net achieves competitive performance, and its effectiveness is confirmed. The code and datasets will be publicly available at https://github.com/ptdoge/S2Net.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it