From God’s Eye to Ground Level: Aerial LiDAR as an Avenue to a Volumetric Understanding of Urban Spaces
Bibliographic record
Abstract
Recent advances in high resolution, aerial LiDAR data collection can facilitate a more thorough understanding of three dimensional (3D) urban space across a range of viewpoints from the God’s eye view to the ground level. Using an extremely high resolution aerial LiDAR dataset collected over a 1.5km2 area of central Dublin, Ireland as a case study, this work pursues new vertical and volumetric understandings of the controversial Spire of Dublin. Viewing this structure in a fully elaborated, 3D environment with the capacity to experience the space from a range of perspectives enables a clearer understanding of the monument’s relative proportion to the space of the built environment both in terms of verticality and volume. Arguably, this in turn provides insight into relationships of power, modernity, tradition, and enclosure that inform a richer understanding of the arguments of both supporters and detractors of this piece of modern, public sculpture. This essay concludes with a suggestion of potential future work in high-resolution, aerial LiDAR collection to aid in developing resources in urban studies more broadly.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".