A supervised training and learning method for building identification in remotely sensed imaging
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 paper investigates a novel approach for building identification in aerial images, that combines a classical segmentation algorithm, the region growing algorithm, a user guided training approach and a supervised learning solution based on support-vector machines. The user is guiding the training procedure by choosing points on the surface of objects of interest, e.g. buildings, as well as points over objects that are of no interest for the application, e.g. streets or vegetation. A local region growing algorithm is applied at each location chosen by the user. The system then prompts the user to label the type of object he/she selected. At the same time, a global region-growing algorithm is applied at uniformly spread seeds over the image and the resulting regions are combined. A series of features based on shape are then built for each region and a support-vector machine is trained to classify between objects of interest versus objects of no interest. The proposed solution obtains results in line in terms of recall and better in terms of precision than those reported in the remote sensing literature.
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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 | 0.001 | 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