Multi-scale colour 3D satellite imagery and global 3D Web mapping
Bibliographic record
Abstract
The two dimensional (2D) satellite image on-line mapping by Google Maps has been a great success in bringing remote sensing into the daily life of general public. The development of online mapping technologies is intensely towards 3D mapping of urban areas or structures of interest. The mainstream technology used for 3D mapping is draping images on 3D models. However, this technology is time consuming and costly. Usually, only major buildings or structures are built in 3D, while small houses and other above-ground objects are still in 2D, resulting in an artificial effect. This paper presents an automatic system for generating multi-scale colour 3D satellite images and dynamic visualization of 3D images through the Internet. This system provides an alternative technology for on-line visualization of real 3D scene of urban environments in a fast and low-cost fashion. Medium-resolution satellite images such as Landsat 7 and high-resolution satellite images such as Ikonos or QuickBird are the data sources for the multi-scale 3D images. New image fusion, colour enhancement and stereoscopy algorithms are developed for the generation of multi-scale colour 3D satellite images. Latest Internet technologies are integrated into the system for fast and dynamic visualization of colour 3D images at different scales. State-of-the-art 3D screens are a great alternative for 3D visualization of the colour satellite images without using 3D glasses. The concept of the 3D satellite image generation and the on-line dynamic visualization are presented. Since Landsat imagery has a global coverage and the Ikonos and QuickBird images can be acquired quickly for the areas of interest, the generation and online visualization of global colour 3D satellite images at different scales is possible with this new system. Different 3D objects on the Earth's surface from mountain ranges, such as Rocky Mountains, to individual family houses and trees can be dynamically visualized and analyzed through the Internet across the world. This new system has a great potential to upgrade or complement the state-of-the-art on-line mapping technologies such as Goolge Maps and/or MapQuest for global colour 3D satellite image visualization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".