RingFormer-Seg: A Scalable and Context-Preserving Vision Transformer Framework for Semantic Segmentation of Ultra-High-Resolution Remote Sensing Imagery
- DOI
- 10.3390/rs17173064
- Published
- 2025-09-03
- Container
- Remote Sensing
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/rs17173064,
title = {RingFormer-Seg: A Scalable and Context-Preserving Vision Transformer Framework for Semantic Segmentation of Ultra-High-Resolution Remote Sensing Imagery},
author = {Zhan Zhang and Daoyu Shu and Guihe Gu and Wenkai Hu and Ru Wang and Xiaoling Chen and Bingnan Yang},
year = {2025},
journal = {Remote Sensing},
doi = {10.3390/rs17173064},
url = {https://doi.org/10.3390/rs17173064}
}RIS
TY - JOUR TI - RingFormer-Seg: A Scalable and Context-Preserving Vision Transformer Framework for Semantic Segmentation of Ultra-High-Resolution Remote Sensing Imagery AU - Zhan Zhang AU - Daoyu Shu AU - Guihe Gu AU - Wenkai Hu AU - Ru Wang AU - Xiaoling Chen AU - Bingnan Yang PY - 2025 JO - Remote Sensing DO - 10.3390/rs17173064 UR - https://doi.org/10.3390/rs17173064 ER -
APA
Zhang, Z., Shu, D., Gu, G., Hu, W., Wang, R., Chen, X., & Yang, B. (2025). RingFormer-Seg: A Scalable and Context-Preserving Vision Transformer Framework for Semantic Segmentation of Ultra-High-Resolution Remote Sensing Imagery. Remote Sensing. https://doi.org/10.3390/rs17173064
Source records
- crossref · retrieved 2026-11-04T20:28:40.889Z