Classification of urban blue green structures with aerial measurements
Bibliographic record
Abstract
The development of climate-responsive design has social and environmental impacts, as the adverse effects of climate change are particularly relevant for urban areas.Green and blue infrastructure has been identified as best practice for achieving greater urban sustainability and resilience.The climatic improvements from use of blue-green infrastructure are generally related to the ability to moderate the impacts of extreme precipitation and temperature.However, the challenges and barriers to implementation of climate adaptation plans focusing on the use of blue-green spaces have not been analysed extensively to date.The present work describes a novel methodology to measure and classify urban surface parameters, which are important for the understanding and simulation of urban flooding.An aerial survey with multispectral sensors in VIS/NIR (Visible and Near Infrared) and IR (Infrared) wavelengths on a UAV (Unmanned Airborne Vehicle) has been carried out at the campus of the Norwegian University of Life Sciences in Ås, Norway.The area covers various types of surface such as asphalt, concrete, gravel, vegetation and water.The Normalized Difference Vegetation Index (NDVI) derived from the VIS/ NIR images have been used to study the spatial distribution and physical characteristics of the vegetation.Multivariate statistical tools have further been utilized to classify the different terrain materials according to their reflectance spectral properties from the multispectral VIS/NIR/IR data cubes.These materials have been linked to roughness and infiltration properties that are commonly used in water analysis simulation tools.Photogrammetry was applied to compute the Digital Surface Map (DSM), which was used to determine drainage lines and water accumulation areas in the surveyed area.The applied method provides data with high spatial resolution that can simplify and improve simulation of urban flooding.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".