Identifying urban boundaries: application of remote sensing and geographic information system technologies
Bibliographic record
Abstract
This paper focuses on a new definition of urbanization trends by investigating the concept of a fuzzy urban boundary (UB) that assigns different membership levels to urbanized aggregates based on a proposed composite index. The research work builds on this logic to investigate a new approach in defining urbanized areas by compounding the characteristics of the fuzzy density of an urban agglomeration with land use variation and intensity of economic activity. Spatial overlaying capabilities of geographic information system (GIS) are used to model the urbanization trend in the case study of Greater Beirut. The UB is defined using a multispectral high resolution visible (HRV) Satellite Pour L'observation de la Terre (SPOT) satellite image. The challenges of urban modelling using satellite images are addressed through an investigative approach in cartographic feature extraction and delineation of the urban agglomeration. This entails image treatment of the spot HRV image, defining internal characteristics of the urban agglomeration and constructing spatially continuous socio-economic data sets that can be combined with the digital remotely sensed image.Key words: fuzzy logic, GIS, remote sensing, spatial urban modelling, urban boundary, urbanization trends.
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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 | 0.000 | 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 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".