Machine learning applied to geoscience: Geo-referenced character recognition
Bibliographic record
Abstract
Significant quantities of non-digital geoscience data exists on maps. In many cases this information has been scanned and is available in a raster format, but remains irretrievable for digital operations. This information may be in both a text and symbol format and it is also necessary to capture the georeferenced location. In many cases this data may also consist of handwritten characters, which have much greater variability than typed characters. An example of such a dataset is handwritten depth soundings that are a common aspect of Canadian Hydrographic Service (CHS) field sheets. CHS maintains a collection of scanned and georeferenced digital image files with handwritten depth soundings recorded directly on lake maps. To make use of this data for digital 3-D modelling, it needed to be converted to geo-referenced vector data. To avoid the time-consuming process of entering thousands of data points, a machine-learning algorithm was applied to automate the digitization process using open-source software. Robust machine-learning libraries available in Python were integrated within a custom work environment for this application. This is an example of how analogue geoscience datasets can be captured in a cost effective, timely and reliable manner.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".