Land use change analysis of Beykoz-Istanbul by means of satellite images and GIS
Bibliographic record
Abstract
Management and planning of the natural environment requires spatially accurate and timely information on land use patterns. With repetitive satellite coverage, the rapid evolution of computer technology and the integration of satellite and spatial data, the development of land use applications have become ubiquitous. The integration of Remote Sensing (RS) and Geographic Information Systems (GIS) has been widely applied and recognized as a powerful and effective tool in detecting land use change in urban areas. This paper presents the land use change analysis of the Beykoz region, which is the second largest administrative district of Istanbul. Land use changes and their impacts are monitored using Landsat (MSS - TM) and Spot 5 satellite data in the period of 1975-2001. The independent classification of each satellite image was used as a change analysis method and the resulting images were analyzed with GIS techniques. The results showed that forest area of Beykoz decreased from 80.55% to 70.5% between 1975 and 1984 and during the 1984-2001 periods, the forested area decreased from 70.5% to 68.86% and the urban growth rate was 4.65%.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".