Identification of land cover alterations in the Alta Murgia National Park (Italy) with VHR satellite imagery
Bibliographic record
Abstract
Land cover exerts a great influence on many basic environmental processes and consequently any transformation in it may have a marked impact on the environment from the local to the global scales.In multidisciplinary research contexts, satellite remote sensing offers opportunities both to evaluate the effects of these processes and to provide one of the information layers needed for designing national strategies oriented to protection and sustainable use of our resources.The advent of recent satellite imagery has increased the possibility to investigate large-scale areas in great detail.Together with an increase in spatial and radiometric resolution, there is, usually, an increase in the variability within land parcels, generating a decrease in the accuracy of land use classification on a per-pixel basis.In order to avoid such negative impacts, an object-oriented classification methodology on IKONOS multispectral data has been implemented on the test area of the Alta Murgia National Park, in the Apulia region (Italy), where soil adaptation for agricultural practices, through rock breaking, has taken place over the last 20 years.The analysis has been conducted with a classification strategy that is able to distinguish land use functions on the basis of differences in spatial distribution and pattern of land cover forms.It consists of two phases: segmentation of the image into meaningful multipixel objects of various sizes, based on both spectral and spatial characteristics of groups of pixels; then, assignment of the segments (objects) to classes using fuzzy logic and a hierarchical decision key.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".