A neural network based approach to detecting urban land cover changes using Landsat TM and IKONOS imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it