Deep Learning Neural Networks for Land Use Land Cover Mapping
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 importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing. This research examines the application of deep learning neural networks (DLNN) to the analysis of satellite imagery with specific focus on the production of land use/land cover maps. DLNN have made considerable strides in pattern recognition and machine learning over the last several years. However, their application to remote sensing is less well developed as the technology was originally designed for simple photographs and not satellite imagery. This research presents the results of an experimental study conducted that developed a DLNN to generate land use/land cover maps of the southern agricultural region of Manitoba, Canada. The results of this approach demonstrate a clear advantage in processing time once the DLNN is properly trained when compared to human based semi-automated process.
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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