DIFFERENTIAL APPROACH FOR MAP REVISION FROM NEW MULTI-RESOLUTION SATELLITE IMAGERY AND EXISTING TOPOGRAPHIC DATA
Bibliographic record
Abstract
The Centre for Topographic Information (CTI), Geomatics Canada, NRCan is responsible for the National Topographic Database (NTDB) and the National Topographic Series (NTS) Maps at scales 1:50000 and 1:250000. One of the major tasks is the updating of the topographic information and the map revision operations. This paper addresses two aspects of revision. In the first one, a differential approach is proposed where emphasis is put into detecting the changes and integrating these changes with the existing topographic data to generate the updated maps, leaving untouched the unchanged cartographic data. In the second, the existing topographic data is used as ‘ a-priori knowledge’ to facilitate change detection, extraction, and validation using multi-resolution satellite imagery. The Indian Remote Sensing satellite images (IRS) from both the 5.8m high resolution PAN and the 23.5m medium resolution LISS 3 sensors were fused and integrated with the existing topographic vectors, vegetation in this case, for a semi-automatic updating process. The paper discusses the methodology and the initial results obtained and demonstrates the potential of the approach for rapid map revision operations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".