LiDAR—A Technology to Assist with Smart Cities and Climate Change Resilience: A Case Study in an Urban Metropolis
Bibliographic record
Abstract
In this paper, we demonstrate three unique use cases of LiDAR data and processing, which can be implemented in an urban metropolis to determine the challenges that are associated with climate change. LiDAR data for the City of Toronto were collected in April 2015 with a density of 10 points/m2. We utilized both a digital terrain model and a bare earth digital elevation model in this work. The first case study estimated storm water, in which we compared flow accumulation values and catchment areas generated with a 20-m DEM and a 1-m LiDAR DEM. The finer resolution DEM demonstrated that the urban street features play a significant role in flow accumulation by directing flows. Urban catchment areas were found to occur on spatial scales that were smaller than the 20-m DEM cell size. For the second case study, the solar potential in the City of Toronto was calculated based on the slope and aspect of each land parcel. According to area, 56% of the city was found to have high solar potential, with 33% and 11% having medium and low solar potential. For the third case study, we calculated the building heights for 16,715 high-rise buildings in Toronto, which were combined with ambulance and fire emergency response times required to reach the base of the building. All buildings that had more than 17 stories were within a 5-min response time for both fire and ambulance services. Only 79% and 88% of these buildings were within a 3-min response time for ambulance and fire emergencies, respectively. LiDAR data provides a highly detailed record of the built urban environment and can provide support in the planning and assessment of climate change resilience activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".