Building Change Detection in Off-Nadir Images Using Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
Recently developed deep learning networks along with advances in remotely sensed data have considerably broadened change detection applications. While tracking changes in urban areas manually is a laborious and time-consuming procedure, the recent improvements in deep learning have enabled researchers to use base and target images and update building footprint layers automatically with high accuracy. However, combining off-nadir satellite and airborne images for automatic change detection is still an ongoing issue in the literature. In this research, we used Patch-wise Co-registration (PWCR) and Mask R-CNN to implement building change detection over off-nadir very high-resolution satellite images taken in 2011 and 2013 from Fredericton, NB, Canada. Then, the new/demolished constructions were detected. The results showed that the model was able to detect buildings with nearly 85% overall accuracy compared to ground truth data.
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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 | 0.000 |
| 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 it