Automated Process for Classifying Built-up Areas Using Geospatial and Census Data, Applied to an Agglomeration of the Algerian Coast
Bibliographic record
Abstract
The semi-urban, semi-rural nature of many cities around the world often leads to conflicts due to their unclear boundaries. This study aims to classify geospatial data into peri-urban, urban, and rural areas using a spatial analysis and geoprocessing process based on socio-economic indicators such as population, distance to urban areas, urban isolation, and road network density and availability. The process utilized satellite images and OpenStreetMap data to distinguish three types of settlements: urban, rural, and peri-urban, and subcategories within each type such as urban, semi-urban, suburban, metropolis, douar, rural settlement, sparse rural, and isolated habitat. Results showed a correlation of over 98% between the estimated population of the generated settlement classes and census data, indicating the effectiveness of this approach, which can be replicated in other settlements, particularly in North Africa.
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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 | 0.001 |
| 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".