Urban planning using data fusion of satellite and aerial photo images
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
Urban planning using data fusion of different satellite and aerial photo images can be very useful. However, multisource data fusion requires geometric and radiometric processing, adapted to the nature and characteristics of the data. In this way the best information available from each image is preserved in the composite image. With the increased resolution of satellite and aerial photo images (5 m and less), the off-nadir viewing angle of the satellite sensor (greater than 20 degrees), and the multi-source data available (such as SPOT, RADARSAT, and IRS), a general and accurate photogrammetric method which can deal with different satellite images and an accurate photogrammetric method for aerial photos are needed. For satellite images, a rigorous method developed at the Canada Centre for Remote Sensing (CCRS), Natural Resources Canada, which takes into account the nature of the data can be used. For aerial photos, the method of space resection by collinearity can be used. This paper presents data fusion results using SPOT, RADARSAT, IRS satellite images and an aerial photo. The results are sharp and precise, which enables a better and easier interpretation for urban planning.
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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 | 0.000 |
| 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