DualStrip-Net: A Strip-Based Unified Framework for Weakly- and Semi-Supervised Road Segmentation From Satellite Images

Automated road segmentation from remote sensing imagery remains a fundamental challenge in Earth observation systems. The primary bottleneck lies in acquiring dense pixel-wise annotations, which is both labor-intensive and time-prohibitive. This article presents DualStrip-Net, a novel deep learning framework for weakly supervised and semi-supervised road segmentation that effectively handles both sparse annotations and limited labeled data. Unlike conventional convolutional neural network (CNN)-based segmentation methods that lack explicit road topology modeling, DualStrip-Net exploits the inherent linear topology of road networks through a dual-stream architecture that combines patch-level annotation strategy and strip-based feature learning. The framework captures road characteristics through orthogonal strip processing in horizontal and vertical orientations. The proposed DualStrip Learning mechanism enables robust feature representation of road structures through complementary views. Extensive evaluations on the DeepGlobe, Massachusetts, and CHN6-CUG benchmark datasets demonstrate that DualStrip-Net achieves superior performance in both weakly supervised and semi-supervised settings. Notably, with only 20% of labeled training data, our method outperforms the supervised-only baselines on both Massachusetts and CHN6-CUG datasets. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jasonnhu/DualStrip-Net/</uri>.