Toward the Use of Deep Learning for Topographic Feature Extraction from High Resolution Optical Satellite Imagery
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
This paper introduces the exploitation of a convolutional neural network for the extraction of topographic features from high-resolution optical satellite imagery. A UNET based model was trained for seven feature classes of roads, buildings, waterbodies using two 3-band (RGB) images for a study site in Kingston (Canada). The trained model's accuracy was evaluated on eight tiles of 8000×8000 pixels using a confusion matrix, the overall accuracy and kappa. The results show overall accuracy varying between 90 % and 99 % and kappa varying between 0.48 and 0.98, with five of the eight tiles being over 0.85. The model generally produced accurate predictions, except for commercial and industrial buildings and for unpaved roads, which were under represented in the training data. The project provided perspective for the development of a training database for topographic feature extraction using deep learning and for expansion to the national scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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