An Assessment of the Growth of Ile-Ife, Osun State Nigeria, Using Multi-Temporal Imageries
Bibliographic record
Abstract
This paper examined the use of GIS and Remote Sensing in monitoring the growth and development pattern of Ile-Ife, Osun State, Nigeria over a period of 21 years with a view to predicting its direction of growth. In effect, the study sought to identify and explain the rate and extent of changes in the study area between 1986 and 2007; measure the rate of urban growth in the study area between 1986 and 2007; assess the impact of urban growth on land use patterns; and predict the trend of urban growth in the study area. Data for the study were generated from both primary and secondary sources. Remote Sensing Imagery of Landsat TM 1986, Landsat ETM 2002 and ALOS 2007 were used to measure the extent of growth and to show the effects of this growth on other Land use/Land cover types. Multi-temporal approach was adopted for the study to detect the changes in the imageries. Pixel analysis was employed to identify and compare the type, nature, trend and magnitude of change that occurred in the study area within the slated dates. The observed land use/land cover and population were projected to the next 15 years. The results showed the growth of Ile-Ife and its effects on other land use classes. Pixel analysis revealed that changes occurred in the magnitude and rate of urbanization in the study area between 1986 and 2007. The results were discussed mainly focusing on the trend of urban growth expansion and its effect to the Environment natural resources, farmland and food security and its contribution to climate change. Its implications to urban planning were also discussed and the recommendations made.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".