Investigation of indices for the automated quantification of landscape qualitative characteristics using digital ground photographs
Bibliographic record
Abstract
This study aims to investigate indices for the automatic evaluation and classification of landscape quality using digital ground photographs.Research efforts to date are scarce on automated extraction of qualitative information based on photographs and therefore this study contributes in this respect, i.e. the automated quantification of landscape qualitative characteristics.Based on the texture indices that are commonly used in landscape analysis, eight quantitative indices are selected and the results from the application of these indices to a sample of ground photographs are described in this paper.These indices are richness, fragmentation, diversity, dominance, grouping and complexity.Furthermore, we investigate the effectiveness of the indices selected as to the classification of the landscape's qualitative characteristics, such as relief morphology, visibility, water existence, vegetation patterns, etc.The results are compared to the results derived from a research programme of the National Technical University of Athens, in which the qualitative characteristics of the landscapes depicted in the same samples of ground photographs have been manually assessed based on the scientific opinion of seven experts.Comments and suggestions are presented based on the comparison for further investigation.The main conclusion of the investigation is that the texture measurement indices are sensitised in the landscape's qualitative characteristics, a fact that is positive and encouraging enough in order to pursue further research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".