Box-Supervised Instance Segmentation For Building Rooftop Delineation From High Spatial Resolution Remotely Sensed Imagery
Bibliographic record
Abstract
In this work, we propose Box2Boundary for box-supervised instance segmentation in building rooftop delineation. The proposed Box2Boundary is developed based on the state-of-the-art(SOTA) bounding box-supervised instance segmentation method Box2Mask. We incorporate InternImage as the backbone for better feature extraction and employ Dynamic Scale Training (DST) during model training. InternImage and DST are integrated into Box2Boundary to address scale variance issues and the complexity of building boundaries in deep learning-based methods. Experiments on the WuHan University building dataset (WHU) demonstrate SOTA performance in box-supervised building rooftop delineation, with an Average Precision (AP) value of 48.7%, surpassing the previous SOTA method Box2Mask by 2%. While it achieves poor performance in building damage assessment using the xBD dataset, we provide a preliminary test of box-supervised instance segmentation for this task. Thus, the proposed Box2Boundary is validated as a practical method for building rooftop delineation, especially when high-quality building annotations are unavailable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".