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), a.k.a. Low-to-High Network (L2HNet) V2, 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.

翻译：大规模高分辨率土地覆盖制图是调查地球表面并应对人类面临诸多挑战的关键任务。然而，由于复杂的地表细节、多样的地形以及广域地理空间上精确训练标签的稀缺性，该任务仍具挑战性。本文提出一种高效的弱监督框架（Paraformer），即低分辨率到高分辨率网络（L2HNet）V2，利用易于获取的低分辨率历史土地覆盖数据，引导大规模高分辨率土地覆盖制图。具体而言，现有土地覆盖制图方法虽展现了卷积神经网络（CNN）在保留局部地表细节方面的优势，但在地形多样性下仍存在全局建模不足的问题。为此，我们在Paraformer中设计了一种并行CNN-Transformer特征提取器，包含无下采样CNN分支和Transformer分支，以联合捕获局部与全局上下文信息。此外，针对训练数据的空间不匹配问题，我们采用伪标签辅助训练（PLAT）模块，合理优化低分辨率标签，用于高分辨率图像的弱监督语义分割。在两个大规模数据集上的实验表明，Paraformer在基于低分辨率历史标签自动更新高分辨率土地覆盖图方面，优于其他现有最优方法。