A Salience-Based Framework for Terrain Modelling: From the Surface Network to Topo-Contexts
Bibliographic record
Abstract
Twenty years after Mark and Smith’s seminal paper, a Science of Topography, we revisit some of their fundamental questions about how landforms are recognised by people and how they can be automatically extracted or delimited from representations of topographic surfaces. Many approaches and tools, essentially based on GeoOBIA, can extract objects associated with landforms from image data. But, they cannot relate these objects to the topology and topography of the terrain. Yet, geo-scientists can easily recognise landforms, considering terrain characteristics and other factors composing the context of appearance of those landforms. Revisiting Gestalt Theory, we propose a salience-based approach fostering a holistic view of the terrain which fits with the geoscientists' ability to recognise landforms using the topographic and hydrologic contexts. The terrain is represented as an extended surface network (ESN), a graph composed of elementary saliences (peaks, pits, saddles, thalweg and ridge networks) and obtained from raster data. The ESN combines both the surface and the drainage networks in a sound topological representation of the terrain. A skeletonisation technique of the ESN’s thalweg and ridge networks is proposed to geometrically and topologically characterise landforms, as well as ensembles of landforms. On this basis and to represent the context of appearance of landforms, geo/topo-contexts are introduced as structures grounded in the properties of the ESN and using the skeletonisation technique. We give an illustration of how a geomorphologist can apply our approach and tools, using the depressions and drainage basins as examples of useful geo/topo-contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".