Remote Sensing and Geographic Information System Applications for Land Use and Land-Suitability Mapping: Case Study—Irbid District, Jordan
Bibliographic record
Abstract
Information on the spatial distribution of land-use and land-suitability categories and the pattern of their changes is a prerequisite for land-resources management—particularly for agricultural and land-use planning. It is well established that remote sensing and Geographic Information System (GIS) have the potential to make the most significant contribution for land-use mapping and land-suitability analysis. The study was conducted as a pilot project to demonstrate the utility of these tools for land-use and agricultural planning.In this paper, SPOT imagery and aerial photographs were used to analyze the utilization of land through a tree structural classification, which consists of subdividing the classification into levels of analysis, proceeding from the lower level to the next higher one. Then climatic, topographic, soil, and land-use data were georeferenced to the same coordinate system and combined together to create land-suitability maps.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".