Road detection using Deep Neural Network in high spatial resolution images
Bibliographic record
Abstract
Object detection is one of the mandatory steps in transferring imagery data into land cover information. Deep networks in machine learning have shown capabilities in automatic object detection and generated promising results. The patch-based Deep Neural Network (DNN) is one of the architectures that is designed for a pixel based object detection in aerial images. The network was designed for the images with 1.2 m spatial resolution, thus, it was unable to generate promising results for a large orthophoto aerial data set obtained over Fredericton city with 0.15 m spatial resolution. In this paper, the patched-based deep neural network is further improved for detecting roads in Fredericton data set. The network is redesigned based on our data and then, trained and applied to the data set. Results are evaluated qualitatively and quantitatively using Precision and Recall (P-R) method, and are compared with the result of Support Vector Machines (SVMs) method. Results of the adapted Deep Neural Network method show 0.89 accuracy, more than SVMs (0.78), making it applicable for road detection in large-scale data sets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".