Tropical Agrarian Landscape Classification using high-resolution GeoEYE data and segmentationbased approach
Why this work is in the frame
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Bibliographic record
Abstract
We examine the use of high spatial resolution ‘GeoEYE’ imagery for land use classification in a tropical landscape. Image objects (I-Os) derived from features identification provide a basis for segmentation process and the Geographic Object Based Image Analysis (GEOBIA) framework. eCognition software with I-Os as classification unit and maximum likelihood algorithm facilitated the process. Supervised classification approaches (SCA) and rule set classification approach (RSCA) were used and performance and transferability of two approaches compared. Main conclusions: (a) high degree of details in GeoEYE data enables delineation of diverse land use zones, and (b) segmentation based analysis is more effective to tackle spatial intermixing.
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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 | 0.000 |
| 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