Ressub-Net: Residual Subtraction Network For DTM Extraction From DSM
Bibliographic record
Abstract
Digital terrain model (DTM) is of paramount importance in various applications, such as infrastructure planning. However, this model is not directly generated from remote sensing sensors. DTMs are generally generated by filtering digital surface models (DSMs) as a product of these sensors. In this regard, deep learning (DL) techniques have been successfully used compared to traditional ones in recent years. DL-based DTM extraction methods, the performance of which is dependent on training losses, are usually followed by ad-hoc post-processors, which makes the efficiency of the DL part distorted. Accordingly, this article presents an independent novel network, a residual subtraction network (ResSub-NET), where different loss functions and their combinations are also assessed. The proposed method is a simple version of residual-based networks, the skip connections of which play a subtraction role. This is attributed to the nature of the problem, which is filtering. The experimental results in three different datasets, related to urban, hilly, and mountainous show that the proposed method outperforms common traditional methods as well as the current state-of-the-art one, named DeepTerRa.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".