A geospatial approach to assessing land change in the built-up landscape of Wa Municipality of Ghana
Bibliographic record
Abstract
Urban landscapes are changing in response to changes in socio-economic conditions. Land change scientists seek to understand these land dynamics in the coupled human-environment system of urban landscapes. This study assessed land change in the built-up area of Wa Municipality between 1986 and 2016 using Landsat images. We used the Support Vector Machine algorithm for classifying the images. We recorded image classification accuracies of 97%, 95%, 92% and 96% for the 1986, 1996, 2006 and 2016 classified images, respectively. Our study finds that over the 1986–2016 period, agricultural land and bare land transitioned to build-up land by 9.23% and 3.79%, respectively, as compared to 2.79% for vegetation and 0.05% for water. Our in-municipal level analysis thus shows that urban landscapes could expand more sustainably by targeting other dominant land categories instead of the vegetation cover. The findings in this paper could serve as a spatial model for planning and reducing the unintended socio-ecological impacts of expansion in the built-up area.
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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 | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".