Evaluating IFSAR and LIDAR Technologies Using ArcInfo: Red River Pilot Study
Bibliographic record
Abstract
The 1997 Red River flood resulted in catastrophic damage to residential, commercial, industrial, agricultural, and public properties in large portions of the Red River Valley in Minnesota and North Dakota, and in the Province of Manitoba, Canada. In the aftermath of the flood, the U.S. and Canadian governments asked the International Joint Commission (IJC) to analyze the cause and effects and to recommend ways to reduce the impact of future floods. In support of the IJC study, the U.S. Army Engineer District, Saint Paul, requested assistance from the U.S. Army Engineer Research and Development Center (ERDC), Topographic Engineering Center (TEC) to evaluate emerging airborne remote-sensing technologies for application to crisis management support. A pilot study was conducted using both Interferometric Synthetic Aperture Radar (IFSAR) and LIght Detection and Ranging (LIDAR) collection systems to determine the correct mix of technologies required. The major objectives of the study were to develop and implement a data fusion technique to merge IFSAR and LIDAR DEMs and to test the hydrological flow of water over each respective DEM. The results of this study will provide the Red River task force with a cost comparison for each of the technologies tested during this project and a list of recommendations for performing the remainder of the basin collection.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".