  Large-scale high-resolution (HR) land-cover mapping is a vital task to survey
the Earth's surface and resolve many challenges facing humanity. However, it is
still a non-trivial task hindered by complex ground details, various landforms,
and the scarcity of accurate training labels over a wide-span geographic area.
In this paper, we propose an efficient, weakly supervised framework
(Paraformer) to guide large-scale HR land-cover mapping with easy-access
historical land-cover data of low resolution (LR). Specifically, existing
land-cover mapping approaches reveal the dominance of CNNs in preserving local
ground details but still suffer from insufficient global modeling in various
landforms. Therefore, we design a parallel CNN-Transformer feature extractor in
Paraformer, consisting of a downsampling-free CNN branch and a Transformer
branch, to jointly capture local and global contextual information. Besides,
facing the spatial mismatch of training data, a pseudo-label-assisted training
(PLAT) module is adopted to reasonably refine LR labels for weakly supervised
semantic segmentation of HR images. Experiments on two large-scale datasets
demonstrate the superiority of Paraformer over other state-of-the-art methods
for automatically updating HR land-cover maps from LR historical labels.
