A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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