The Frequent Image Frames Enhanced Digital Orthorectified Mapping (FIFEDOM) Camera for Acquiring Multiangular Reflectance From the Land Surface
Bibliographic record
Abstract
The Frequent Image Frames Enhanced Digital Orthorectified Mapping (FIFEDOM) camera was designed to provide a cost-effective remote-sensing method for accurate acquisition of forest information, such as spatial distributions of individual tree species and tree structures for forest monitoring and management. Compared with existing regular digital cameras, the FIFEDOM camera has several unique features as follows: (1) it can collect data not only in the visible bands (550 and 670 nm) but also in the near-infrared band (800 nm); (2) it has a frame rate of up to 3 frames/s with a frame size of 3500 times 2300; and (3) it has a wide angular field view with 150deg along track and 78.8deg across track. Its high frame rate and wide angular field view allow it to obtain a sequence of images that oversample ground target areas. The multiangle database and bidirectional reflectance signatures of forest canopies can be generated from the oversampled image data, which can be used to identify forest species and estimate tree structures. In addition, the multiframe highly overlapped FIFEDOM data can also be used to generate a very dense, high-quality, and reliable digital surface model. Effective methods for radiometric and geometric calibration of the FIFEDOM camera were developed in this paper. A data-acquisition campaign was carried out in 2004 over the Algoma boreal forest, Ontario, Canada. The FIFEDOM data were validated using the data acquired by the Compact Airborne Spectrographic Imager instrument, which was flown together with the FIFEDOM camera.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".