Urban green space extraction from BJ-2 remote sensing image based SegFormer semantic segmentation model
Bibliographic record
Abstract
Accurate extraction of urban green spaces is of great significance for promoting urban planning and management, and promoting sustainable urban development. Given the characteristic spatial distribution of urban green spaces in high-resolution remote sensing imagery, a framework for automated extraction of urban green spaces using the SegFormer semantic segmentation network is proposed. The framework leverages self-attention mechanisms to capture global information and enhance the extraction of green spaces. We have constructed an urban green space dataset that includes four study areas: Ruyuan, Tongcheng, Jianli, and Hanchuan. Experimental results demonstrate that the proposed architecture achieves superior performance compared with other models, with a mean intersection over union ratio of 89.26% and an accuracy rate of 95.34% (pixel accuracy). The visualization results exhibit exceptional performance in extracting green spaces with clear spatial relationships, such as roadside and community greening while effectively distinguishing between urban green spaces and farmland. Furthermore, we conduct experiments on generalization ability on four different areas. The results show that the generalization ability of SegFormer outperforms other models. Lastly, the experiments verify the impact of different data augmentation multiples on model accuracy, providing valuable insights to guide data augmentation strategies in practical engineering.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".