A METHOD FOR CONTINUOUS EXTRACTION OF MULTISPECTRALLY CLASSIFIED URBAN RIVERS
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
The continuous extraction of linear objects, such as rivers, roads, or boundaries, from digital images can hardly be achieved using automatic methods. Line extraction algorithms as well as multispectral classification usually break down linear objects into segments with significant noise. In urban areas it is especially hard to continuously extract small rivers because of mixed pixels and other disturbing elements (bridges, ships, shadows, etc.). Therefore, methods for connecting broken linear segments and eliminating noise are important. For mapping urban water areas, in addition to connecting broken river segments, additional operations have to be carried out. In this study, a simple and effective automatic method was developed to connect river segments and eliminate noise. By applying this method, discontinuous river segments can be connected, small water areas can be separated from noise, and noise in large water areas can be eliminated. The method was tested using Landsat m/l images in the urban area of Shanghai, China. The visible water bodies in the TM image were extracted. The result shows that the accuracy of urban water area extraction is increased up to over 95 percent. Rivers and canals broader than one image pixel can be completely
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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 | 0.001 | 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) | 0.000 | 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