A Comparative Study of Deep Learning-Based Semantic Segmentation Methods for High-Resolution Remote Sensing Imagery
Bibliographic record
Abstract
Remote sensing image information extraction plays a crucial role in land use planning, environmental monitoring, and natural disaster assessment. However, traditional machine learning-based methods often face challenges such as high computational complexity and limited feature representation ability when processing large-scale remote sensing data, leading to difficulties in meeting both efficiency and accuracy requirements. With the rapid development of deep learning, its application to remote sensing data processing has become a powerful solution. This paper uses the standard Potsdam dataset provided by ISPRS and tests and compares the accuracy of several commonly used deep learning convolutional networks, including SegNet, PspNet, Unet, UNet++, DeepLab V3+, SegFormer, and SegVit, in remote sensing image information extraction. Experimental results show that SegVit performs exceptionally well in accuracy, detail preservation, and edge clarity, achieving higher precision compared to other networks. This finding provides an effective solution for remote sensing image information extraction and offers strong support for research and applications in related fields. It is worth noting that although SegVit excels in accuracy, it may require more computational resources and time during training and inference. Therefore, in practical applications, it is necessary to balance efficiency and accuracy and choose a network model that suits the specific task requirements.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".