Remote Sensing and Geographic Information System for Inferring Land Cover and Land Use Change in Wuhan (China), 1987-2006
Bibliographic record
Abstract
This study evaluates land use /land cover changes (LULCC) in Wuhan city, China, between 1987-2006 using satellite imagery data. Spatial and temporal dynamics of LULCC were quantified using three landsat TM images (1987, 1994 and 2006). The maximum likelihood supervised classification algorithm and post classification Change detection technique in GIS were also used. The analysis revealed that forest and urban growth over the study period changed by 15.57% and 8.66% respectively, resulting in a significant decrease in the area of cultivated land (16.88%) and water (7.35%). For the three main towns that make up Wuhan city, Wuchang increased in water, urban and cultivated land, and a decrease in forest cover; Hanyang increased in urban area and decreases in cultivated land, water and forest, while in Hankou, cultivated land and forest increased, urban and water covers decreased. The overall accuracy of the derived LULCC maps ranged from 88% to 92%. The outcomes of this research will benefit society through the creation of reliable land cover information for better decision making. However, to identify how information diffusion and spatial externalities could affect the spatial pattern and composition of land cover over time, agent-based techniques could be more helpful.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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".