Unmanned aerial image dataset: Ready for 3D reconstruction
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
Unmanned aerial vehicles (UAVs) have become popular platforms for collecting various types of geospatial data for various mapping, monitoring and modelling applications. With the advancement of imaging and computing technologies, a vast variety of photogrammetric, computer-vision and, nowadays, end-to-end learning workflows are introduced to produce three-dimensional (3D) information in form of digital surface and terrain models, textured meshes, rectified mosaics, CAD models, etc. These 3D products might be used in applications where accuracy and precision play a vital role, e.g. structural health monitoring. Therefore, extensive tests against data with relevant characteristics and reliable ground-truth are required to assess and ensure the performance of 3D modelling workflows. This article describes the images collected by a customized unmanned aerial vehicle (UAV) system from an open-pit gravel mine accompanied with additional data that will allow implementing and evaluating any structure-from-motion or photogrammetric approach for sparse or dense 3D reconstruction. This dataset includes total of 158 high-quality images captured with more than 80% endlap and spatial resolution higher than 1.5 cm, the 3D coordinates of 109 ground control points and checkpoints, 2D coordinates of more than 40K corresponding points among the images, a subset of 25 multi-view stereo images selected from an area of approximately 30 m × 40 m within the scene accompanied with a dense point cloud measured by a terrestrial laser scanner.
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) | 0.002 | 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 it