MAIG-Net: Unsupervised Remote Sensing Road Extraction Combining Multi-Layer Adversarial Learning and Intermediate Domain Guidance

In unsupervised cross-domain remote sensing road extraction, severe data distribution shifts and heterogeneous background interference significantly constrain model performance. In addition to appearance shift, source and target images acquired by different sensors often present roads at unequal physical scales. To address this, MAIG-Net combines a target-label-free ground-sampling-distance (GSD) rule that matches the physical field of view of the two domains, an intermediate domain constructed by Fourier domain adaptation (FDA) that transfers only low-frequency target appearance onto labeled source images while preserving the complete source phase and road labels, and Domain-Invariant Feature Alignment (DIFA) modules that perform reliability-weighted, topology-conditioned adversarial alignment at three encoder depths. An exponential-moving-average (EMA) teacher supplies detached reliability and topology conditions for the domain discriminators; no target prediction is used as a direct segmentation label. Under a matched protocol with a fixed 30-epoch budget and three seeds, MAIG-Net improves the mean road IoU from 0.327 to 0.376 on SpaceNet→DeepGlobe and from 0.450 to 0.458 on SpaceNet→Massachusetts relative to source-only training, with consistent per-seed gains in both directions; the smaller Massachusetts effect is directionally reproduced on a previously untouched validation holdout. Reverse adaptation, reliability perturbations, computational cost, and failure cases are further reported to delimit the applicability of the method.