Use of Geospatial Techniques in Monitoring Urban Expansion and Land Use Change Analysis: A Case of Lahore, Pakistan
Bibliographic record
Abstract
Rapid urban expansion and resultant temporal land use changes have a profound effect on the city’s environment and its surroundings. Due to its significance, it is essential to evaluate the urban expansion patterns and land use change analysis of mega cities of the world. For land use change detection, multi-source & multi-temporal satellite images along with GIS & remote sensing (RS) techniques are significant aspects in analyzing urban expansion all over the world. In present study, two image data sets of the Landsat system in 7/ETM+ and 8/OLI modes, along with ground truthing data were utilized to examine the spatio-temporal dynamics of land use changes and assess the spatial patterns of urban expansion in Lahore, Pakistan from the year 2000 & 2014. Supervised classification using maximum likelihood algorithm has been carried out for land use classification andPost classification change detection technique was used to produce change detection map of the study area. The output land use and change detection map revealed that the areal expansion has been attributed due to loss of agricultural land and urban sprawl while major change in land use has taken place in built-up and agricultural areas. The results indicated that 40.81% of built-up area increased, while agricultural land has decline by -12.98% during the study period (2000-2014). Due to this the observed expansion of the city has been toward the South-east, South and South-west along with major roads. The results infer can provide better understanding and information about the past and current spatial dynamics of land use change in Lahore, Pakistan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".