Mapping urban vegetation cover using WorldView-2 imagery
Bibliographic record
Abstract
There are clear indications that densification of built-up areas within cities and new developments in their outskirts, in conjunction with urban population activities, are at the origin of climate changes at the local level and have a direct impact on air and water quality. Densification of the vegetation cover is often mentioned as one of the most important means to mitigate the impacts of climate changes and to improve the quality of the urban environment. Decision making on vegetation cover densification presupposes that urban planners and managers know exactly the actual situation in terms of vegetation location, types and biomass. However, in many cities, inventories of vegetation cover are usually absent. This study examines the feasibility of an automatic system for vegetation cover inventory and mapping in urban areas based on WorldView-2 imagery. The city of Laval, Canada, was chosen as the experimental site. The principal conclusions are as follows: a) conversion of digital counts to ground reflectances is a crucial step in order to fully exploit the potential of WV-2 multispectral images for mapping vegetation cover and recognizing vegetation classes; b) the combined use of NDVIs computed using the three infrared available bands and the red band provides an accurate means of differentiating vegetation cover from other land covers; and c) it is possible to separate trees from other vegetation types and to identify tree species even in dense urban areas using spectral signature characteristics and segmentation algorithms.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".