A Method for Fully Automatic Building Footprint Extraction From Remote Sensing Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatically mapping building footprints has a wide range of applications in many fields. In recent years, the automatic building extraction methods based on deep learning show an absolute advantage over the traditional image segmentation methods due to its high speed and high precision. However, the building footprint extracted by deep learning is just an irregular building mask. There is still much work to be done to transform the building mask into a vector building footprint in the usual sense. One of the most important tasks is to determine the orientation of each side of the building. Most of the current methods are based on the building mask to determine the orientation of each side of the building. The biggest disadvantage of this method is that it completely relies on the building mask which is often unsatisfactory. In this case, the article proposes a method to determine the orientation of each side of the building based on the building mask and line segments, thereby effectively avoiding the danger of relying on the building mask. Experiments show that the proposed method can achieve high-speed and high-precision automatic extraction of building footprints from remote sensing images, saving costs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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