DPCDO: Dynamic-Coordinated Learning for Multitask Domain Adaptation in Remote Sensing Imagery
Abstract
Unified architectures that jointly perform semantic segmentation and monocular height estimation offer improved computational efficiency through shared representations, but face challenges in remote sensing applications: scarce annotations, intertask interference, and cross-regional domain shifts. These issues often lead to severe performance degradation, as models struggle with domain discrepancies and conflicting gradients. Moreover, existing domain adaptation methods remain constrained, suffering from low pseudolabel confidence, feature misalignment, and negative transfer. To address these, we propose DPCDO, a dynamic-coordinated learning approach for unsupervised multitask domain adaptation, which jointly models the multivariate conditional generation process of task outputs, domain alignment, and pseudolabel consistency. DPCDO integrates three synergistic modules: 1) dynamic patchwise prototype matching dynamically aligns local feature distributions to accommodate topographic diversity, promoting domain-invariant representation learning and cross-task information sharing; 2) multistream multiscale consistency constraint performs multiscale perturbations and enforces cross-stream consistency to enhance pseudolabel reliability; and 3) decoupled optimization for multitask heads to resolve intertask gradient conflicts. Extensive experiments show DPCDO consistently outperforms state-of-the-art methods in both semantic segmentation and monocular height estimation, validating its effectiveness in disentangling domain shifts and enhancing cross-task synergy.