REVIEW OF PHOTOGRAMMETRY, REMOTE SENSING, AND GEOSPATIAL SCIENCES REQUIREMENTS BY VARIOUS ACCREDITATION BODIES
Bibliographic record
Abstract
Abstract. Technological advancements in the fields of Photogrammetry, Remote Sensing, and Geospatial Sciences continuously alter the way we collect, process, and interpret datasets. Accreditation bodies and institutions often need to respond to those changes and make timely modifications to stay current with technology changes and industry needs. However, it can be generally stated that the industry can adapt and adopt technological changes faster than accreditation organizations and institutions, because the process of modernizing curricula can be time consuming and slow based on organization / university / college policies. In addition to industry demands, institutions must adhere to and follow accreditation requirements. This paper reviews several accreditation requirements related to Photogrammetry, Remote Sensing, and Geospatial Sciences. The accreditation bodies that are reviewed focus on organizations in Northern America and Europe due to ease of access to information. The review provides insights about their curricula criteria, their level of detail, if they can be considered current based on industry needs, and if they provide enough flexibility for modernizing curricula without violation of accreditation policies and criteria.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".