Towards a landform geodatabase : the automatic identification of landforms
Bibliographic record
Abstract
If a geomorphologist is able to identify landforms from an aerial photograph or a Digital Terrain Model, then it should be possible for a computer to mimic the same process. The Landform Classification System (LCS) was created to allow for the automated identification of landforms from a Digital Terrain Model. The system uses a combination of a Network-Integrated Triangulated Irregular Network (NetTLN), a Fuzzy ARTMap Artificial Neural Network (ANN), and custom programming to produce a classification based on 22 morphometric variables, which describe the shape of the land surface. The ANN allows the system to "see" patterns in the morphometric variables. Once it has been trained with examples of different landform types, the ANN can perform a classification based on what it has learned. The LCS requires sufficient examples to produce high classification accuracies. Within the LCS, Kappa Analysis is used as the primary method for assessing classification accuracy. Kappa analysis takes into account the fact that even a random distribution of classified triangles may result in a few correct matches, so it is used as the primary measure of accuracy in this thesis. The K statistic produced by the Kappa Analysis decreases as we move from drumlins (8911 triangles) to eskers (193 triangles) and kames (11 triangles). The results for drumlins were best, with an Overall Accuracy value of 74.78% and a K accuracy value of 26.36%. For eskers, the values were 95.85% and 3.99% respectively. It should be noted that in spite of the low K values for eskers, the system has identified six potential eskers that were previously unidentified. For kames, the Overall Accuracy value was 98.47% and the K value was 0.00%, although this latter value is a reflection of the fact that no kames are known to exist on the map sheets that were classified. The Landform Classification System is reasonably fast at performing classifications. The ANN is currendy an external program; with some additional work, it can be incorporated directly into the LCS. Once this is done, the LCS should be fast enough to allow large areas to be classified. If the accuracy of the classifications can be improved somewhat, the Landform Classification System can then be used to produce a "Landform Geodatabase," which is a Geographical Information System (GIS) layer containing the type and extent of all landforms over a broad area. A short paper summarizing some of the results of this project to date was recendy presented at the Geotec 2005 conference in Vancouver. Entided "Development of the Landform Classification System," this paper summarizes some of the successes and problems that have surfaced in this project.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".