Urban Change Detection and Population Prediction Modeling Using Multitemporal Landsat TM Images
Bibliographic record
Abstract
Urban environments belong to the most dynamic system on the earth's surface. Urban areas contain nearly half of the world's population. Understanding the growth and change brought on by urbanization is critical for urban planning, environmental studies, and resource management. This study is an attempt to present a satellite-based approach to modelling urban population growth from multitemporal and multispectral Landsat image data. The focus is placed on two aspects: detection of urban land cover changes and population prediction modeling associated with the urban expansion. The study consists of an experimental set-up to generate the land cover maps and to recognize the vegetation-impervious surface-soil (V-I-S) patterns followed by integrating population census data and remote sensing data at the city planning district level. This is done in conjunction with geographic information systems (GIS) in order to model population growth from 1996 to 2001 in the City of Mississauga, Ontario. The main findings of this research show that a total of 81.6 km² of built-up areas have been added with Mississauga's boundaries between 1985 and 2002. This accounts for 25.5% of the total area of Mississauga at the expense of non-built and water covered areas. The results show an increase of 6.5% in built-up areas in the last three years (1999-2002), which results in an average growth rate of 7 km²/year. The previous 14 years (1985-1999) have shown an increase of 19.0% in development, which equals 4.3 km²/year. The investigation also shows that a linear equation adequately describes the relationship between the population counts and the built-up area, or "C-442" area, of V-I-S components.
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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 | 0.000 | 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".