FAST ORTHOPHOTO PRODUCTION USING THE DIGITAL SENSOR SYSTEM
Why this work is in the frame
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Bibliographic record
Abstract
The Digital Sensor System (DSS) is a fully integrated fully digital ruggedized system for airborne image acquisition, georeferencing, and map production. The DSS consists of a 4K x 4K digital camera, a GPS-aided INS direct georeferencing system, and a flight management system. The DSS digital camera component uses a CCD chip with a 9 µm pixel size which allows digital image acquisition with a Ground Sample Distance that ranges from 0.05 m to 1.0 m using its 35 mm and 55 mm lenses. The embedded POS AV direct georeferencing system provides the exterior orientation parameters in both real-time and post-mission modes. The DSS is used primarily to generate high-resolution color and color infrared digital orthophotos and orthomosaics. The DSS data interfaces directly and seamlessly with commercial off-the-shelf photogrammetric software to allow for fast map production. Orthophotos are created using the DSS-derived directly georeferenced digital images and a Digital Elevation Model (DEM). The orthophotos and/or orthomosaics can then be used for many different mapping, GIS and remote sensing applications. Examples of these are updating and maintaining cadastral GIS databases, classifying and mapping pervious and impervious surface areas, identifying wetland areas, updating land use maps, estimating crop yields and health, preparing timber stand inventories, planning for new construction sites, verifying areas for licensing and permitting. Many of these applications involve small localized areas, corridors, or irregular spot shots, which make the DSS the suitable tool for such projects. In this paper, an overview of the DSS system design, calibration, and performance is presented, while DEM extraction and orthophoto generation using the DSS is discussed in some detail using real mapping missions.
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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