Material identification using fuzzy-classification of high resolution hyperspectral imagery of an urban area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Remote sensing of urban materials is crucial for urban planning and management. The use of current data on surface materials is fundamental to many aspects of urban planning such as public health monitoring, natural disaster risk management, energy balancing, and more. Often, this type of information is acquired through field surveys; however, this method of data acquisition can be time consuming, tedious, and costly. With advances in remote sensing, especially high-resolution imagery, this information is now increasingly accessible [1]. Our project uses CASI (compact airborne spectrographic imager), an airborne hyperspectral sensor that measures radiation in up to 288 contiguous bands in a spectral range between 365 nm and 1050 nm. As it is an airborne sensor, it also has the advantage of having high spatial resolution as small as 25cm. The CASI data acquired in the summer of 2016 of the island of Montreal (Quebec, Canada), had a resolution of 1m, and contained 96 bands.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it