From Palm Groves to Urban Zones: Patterns and Past Trends of Urban Sprawl and Land Use Efficiency in Semi-Arid Context, Case of Biskra, Algeria
Bibliographic record
Abstract
Cities worldwide are experiencing rapid expansion, often leading to urban sprawl that encroaches on adjacent agricultural land. This phenomenon represents a significant challenge for sustainable urban development. In this context, the present study investigates land use efficiency by assessing the spatiotemporal dynamics of urban sprawl in the Biskra urban area, Algeria, over a twenty-year period. Using a retrospective, integrated remote sensing (RS) and GIS approach, multi-temporal Landsat imagery was processed via supervised classification using the maximum likelihood algorithm. Land use land cover changes were further analyzed using post-classification comparison of image pairs spanning two decades to determine the magnitude, direction, and nature of transformations. Findings reveal that prevailing urban policies have contributed to inefficient land management practices, leading to sprawling urban patterns and significant land-use changes. During the first decade of the 2000s, urban expansion accelerated sharply, with the built-up area increasing at a rate nearly four times higher than population growth. As a result, three previously distinct municipalities merged into a single, continuous urban zone, accompanied by extensive conversion of palm groves into built-up areas. By revealing how current planning practices have contributed to unsustainable land-use patterns, the findings highlight the need to align local urban management strategies with broader global sustainability frameworks that align with the objectives of the Sustainable Development Goals (SDGs).
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".