Bibliographic record
Abstract
In recent decades, Remote sensing data becomes one of the basic information for generating of base maps and different applications in geomatics.In fact, it is providing very useful for a board range of environmental applications such as surveying, agriculture, geography, meteorology, hydrology, transportation, urban planning, control analysis, landscape planning and etc. Especially in order to generation base maps, the Satellites data has a great role and it is now widely applied on collecting and processing data.For reach to this purpose, we had been used Indian satellite imageries such as the IRS-P5 and the IRS-P6 satellite data which have been belonged to Indian Space Research Organization (ISRO).The P5 (Cartosat-I) satellite was launched on May 5, 2005 into circular sun synchronous orbit which it is equipped with two panchromatic cameras capable of simultaneous acquiring images of 2.5 meters spatial resolution.Also the IRS-P6 (Resourcesat-I) was launched on October 17, 2003 which has three sensor includes LISS III, LISS IV and AWIFS.The LISS IV sensor of this satellite has the spatial resolution 5.8 m with enhanced spectral resolution.It consists of three spectral bands in the green, red and near infrared regions of the electromagnetic spectrum.In this investigation we had been developed a method for generating of base maps in middle scale, such as 1:15000 ratio scale and an attempt has been made to evaluate the information content available with merging data consist of the IRS-P5 and MX mode image from IRS-P6 satellite imageries.The results have shown its capability in solving of generation base maps with IRS satellite data and we found that merging these data is very suitable for identification all of the features in the base maps in different categories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.949 | 0.969 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".