CornerRegNet: Building Segmentation from Overhead Imagery Using Oriented Corners in Deep Networks
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
The increasing demand for high-resolution maps across various applications has driven the need for building segmentation vectors from overhead imagery. Nevertheless, current deep neural networks commonly output raster data, necessitating extensive post-processing and often compromising the fidelity, regularity, and simplicity of buildings. This paper proposes a novel deep convolutional neural network, CornerRegNet, which directly extracts delineated building polygons from images. Specifically, a deep model is designed to predict building footprint masks, corners, and corresponding orientation vectors pointing to adjacent corners. Then, an initial polygon is reconstructed using the predictions, and a graph convolution network repeatedly refines it using both semantic and geometric features. Initialized by the predicted corners, the output polygon is inherently simple, and the geometric information in oriented corners leads to more regular and accurate results. Compared to state-of-the-art methods, our approach demonstrates a highly competitive performance on SpaceNet Vegas and CrowdAI datasets.
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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