The use of eye tracking for analysing the perception of landscape composition and vertical objects
Bibliographic record
Abstract
The European Landscape Convention states the need for public participation in landscape planning and management. Therefore, it is important to know how landscapes are observed by people, how they perceive different landscape features, to include this knowledge into landscape planning and design. This can be measured objectively using eye tracking technology, a system measuring the speed and direction of eye movements (saccades) and fixations while looking at images. Although the eye tracking system is used in the field of experimental psychology, it is also introduced in geography, landscape science and cartography and is emerging as an innovative tool for assessing people’s perception of images (e.g. landscape photographs, maps). The general aim of this paper is to assess the effect of the horizontal and vertical view angle of landscape photographs on the perception of landscapes. Also, the influence of the type of landscape on perception is tested. Therefore, a group of 20 observers are asked to observe 72 landscape photographs. The photographs represent 18 different landscapes in Flanders, ranging from urban and multifunctional suburban landscapes to more rural landscapes, differing in degree of urbanisation, openness, heterogeneity and topography. For each site, four pictures are taken: a panoramic photograph (70° horizontal view angle), standard photograph (31°), a detail photograph (< 31°) and a wide angle photograph to determine the influence of the view angle on the perception of landscape composition. To avoid effects of transparency by vegetation, all photographs are taken in the same season. The respondents are graduate geographers. The measurements are done using an Eye Link 1000 device from SR Research (Ontario). The results are visualised in density maps, showing the intensity of eye fixations on different features in the landscape. Also, saccades are visualised and their length is measured to determine the influence of the complexity of landscapes on the number and length of eye movements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".