Satellite Image Parcel Segmentation and Extraction Based on U-Net Convolution Neural Network Model
Why this work is in the frame
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Bibliographic record
Abstract
Satellite image parcel segmentation is a specific task of satellite image interpretation. Good satellite image parcel segmentation results can provide guidance for environmental protection, agricultural production and town construction. In this paper, a U-Net convolution neural network model based on Tensorflow framework is built. During the training process, a data enhancement strategy is specially designed for the satellite image parcel segmentation task, so as to enhance the generalization ability of the model. The experimental results select the intersection ratio (Iou), recall rate (Recall), and Kappa coefficient as evaluation indexes, and the final model can achieve a Kappa coefficient of 0.9342, which is significantly better than the random forest and convolution neural network methods commonly used for satellite image segmentation. The regions of some segmented images are not complete enough, and the connectivity of segmented images needs to be further improved. The satellite image parcel segmentation method proposed in this paper can realize the fine segmentation of high-resolution satellite images and provide a reference for the research of satellite image segmentation.
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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