SPATIAL RESOLUTION ENHANCEMENT OF LAND COVER MAPPING USING DEEP CONVOLUTIONAL NETS
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
Abstract. Multispectral satellite imagery is the primary data source for monitoring land cover change and characterizing land cover at the global scale. However, the accuracy of land cover classification is often constrained by the spatial and temporal resolutions of the acquired satellite images. This paper proposes a novel spatiotemporal fusion method based on deep convolutional neural networks under the application background of massive remote sensing data, as well as the large spatial resolution gaps between MODIS and Sentinel images. The training was taken on the public SEN12MS dataset, while the validation and testing were conducted using ground truth data from the 2020 IEEE GRSS data fusion contest. As a result of data fusion, the synthesized land cover map was more accurate than the corresponding MODIS-derived land cover map, with an enhanced spatial resolution of 10 meters. The ensemble approach can be implemented for improving data quality when generating a global land cover product from coarse satellite imageries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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