Reproduce “DB SwinT: A Dual-Branch Swin Transformer Network for Road Extraction in Optical Remote Sensing Imagery” on OpenResearch
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About this paper
Zongyang He, Xiangli Yang, Xian Gao, Zhiguo Wang
DB SwinT extracts road masks from overhead imagery by combining two Swin Transformer branches: patch size 4 captures fine detail, while patch size 8 supplies broader context. Attentional Feature Fusion blends their features before a U-shaped decoder produces predictions. On DeepGlobe, it achieves 79.35% IoU versus 75.37% for SwinT+U-Net, with gains reported on Massachusetts; results are limited to benchmark datasets.
Summary by alphaXiv.
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