Assessing Land Cover Transformation for Urban Environmental Sustainability through Satellite Sensing
Bibliographic record
Abstract
Rapid urbanization in Pakistan is triggering regulated and unregulated land cover changes in planned and semiplanned urban areas. The key objective of this study is to assess the spatial–temporal fluctuations in the land use/land cover (LULC) transformations in planned (Islamabad) and semiplanned (Rawalpindi) urban areas over the last forty years (1976–2016). The study focuses on the orientation of LULC modifications and analyzes concomitant impacts on urban environmental sustainability. Therefore, remotely sensed data were retrieved and processed through Google Earth Engine (GEE) by applying supervised classifier algorithms on each of the five chosen Landsat images. The trajectory of LULC changes for each of the four periods 1976–1988, 1988–1995, 1995–2006 and 2006–2016 was critically scrutinized. The observations revealed massive physical expansions and LULC convergences during these timeframes. The proportionate share of built-up surfaces in this contextual setting substantially stretched from 0.83% in 1976 to 23.23% in 2016, while the shares of cropland and shrubberies significantly reduced. The orientation and magnitude of such changes were observed asymmetrically in the adjoining urban settlements. The assessments formulate that availability of land for urban growth, urban planning and regulatory control significantly determines the speed, scale and orientation of urbanization in planned and semiplanned areas. The study substantiates the notions that the efficient use of cost-effective remotely sensed data offers a pragmatic and reliable tool for assessing, evaluating and monitoring urban land resources. The inferences and insights are relevant for urban and regional planners as well as for other scientific communities.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".