Data Structures for 2D Representation of Terrain Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This entry gives an overview of the main data structures and approaches used for a two-dimensional representation of the terrain surface using a digital elevation model (DEM). A DEM represents the elevation of the earth surface from a set of points. It is used for terrain analysis, visualisation and interpretation. DEMs are most commonly defined as a grid where an elevation is assigned to each grid cell. Due to its simplicity, the square grid structure is the most common DEM structure. However, it is less adaptive and shows limitations for more complex processing and reasoning. Hence, the triangulated irregular network is a more adaptive structure and explicitly stores the relationships between the points. Other topological structures (contour graphs, contour trees) have been developed to study terrain morphology. Topological relationships are captured in another structure, the surface network (SN), composed of critical points (peaks, pits, saddles) and critical lines (thalweg, ridge lines). The SN can be computed using either a TIN or a grid. The Morse Theory provides a mathematical approach to studying the topology of surfaces, which is applied to the SN. It has been used for terrain simplification, multi-resolution modelling, terrain segmentation and landform identification. The extended surface network (ESN) extends the classical SN by integrating both the surface and the drainage networks. The ESN can itself be extended for the cognitive representation of the terrain based on saliences (typical points, lines and regions) and skeleton lines (linking critical points), while capturing the context of the appearance of landforms using 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it