AIRPORT PLAN TOPOGRAPHIC EXAMINATION: ACCURACY ANALYSIS BY DEMNAS AND ASTER GDEM METHOD IN TERRESTRIAL SURVEYS
Bibliographic record
Abstract
An airport feasibility study is an important thing that must be completed to propose a new airport as a condition for issuing an airport location determination. The most critical indicator is the technical construction, which examines the topographic conditions of the new airport location. The topographic conditions using a terrestrial survey are highly accurate because they are carried out directly on the analyzed object. However, terrestrial surveys require time, energy, and money. This study aims to examine the topographic conditions using DEMNAS and ASTER GDEM, which can provide the same data as terrestrial surveys with a spatial resolution of 8 meters and 30 meters for free. The results of the comparative analysis show that the average elevation difference between DEMNAS and ASTER GDEM against terrestrial survey in the new airport location plan in Mahakam Ulu Regency is 2.04 meters and 8.89 meters, respectively. The validity and accuracy test of DEMNAS against terrestrial survey resulted in R2 0.963, RMSE 2.417 meters, NSE 0.941, and LE90 3.897 meters. ASTER GDEM against terrestrial survey resulted in R2 0.674, RMSE 6.244 meters, NSE -0.666 and LE90 10.3 meters. The analysis results show that DEMNAS data is better than ASTER GDEM. The conclusion is that DEMNAS data has a good level of accuracy that can be used to determine and analyze the topographic conditions of the new airport land plan so that it can be an alternative for the initiator in preparing the airport feasibility study.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".