Towards a landform geodatabase : the automatic identification of landforms
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».