A neural network based approach to detecting urban land cover changes using Landsat TM and IKONOS imagery
Bibliographic record
Abstract
Much attention has been drawn to the new applications and opportunities afforded by high-resolution satellite imagery, such as IKONOS and QuickBird. The purpose of this paper is to examine the extent to which high-resolution change detection can be performed using a combination of high and medium-resolution satellite imagery. This combination is important for detecting changes during the time before and after the high-resolution satellite imagery was made available. In particular, the analysis is oriented towards smaller cities and municipalities. Many change detection algorithms and methods have been evaluated. The post-classification change detection algorithm was deemed to be the most suitable technique for this project. Landsat 5 TM and IKONOS MS images of Fredericton, New Brunswick, Canada, were used as source data for the change detection. The results tend to suggest that it is possible to extract reliable change detection information pertaining to small streets, and new rows of residential housing with a medium-resolution benchmark. However, the detection of change in individual houses and small buildings proved to be beyond the capabilities of this procedure.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".