High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the complex land cover patterns, the costly training sample collections, and the severe distribution shifts of satellite imageries, few studies have applied high-resolution images to land cover mapping in detailed categories at large scale. To fill this gap, we present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. In addition, we propose a deep-learning-based unsupervised domain adaptation approach that can transfer classification models trained on labeled dataset (referred to as the source domain) to unlabeled data (referred to as the target domain) for large-scale land cover mapping. Specifically, we introduce an end-to-end Siamese network employing dynamic pseudo-label assignment and class balancing strategy to perform adaptive domain joint learning. To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images. Over a total study area of 60,000 square kilometers, the experiments show promising results even though the input images are entirely unlabeled. The proposed approach, trained with the Five-Billion-Pixels dataset, enables high-quality and detailed land cover mapping across the whole country of China and some other Asian countries at meter-resolution.

翻译：高分辨率卫星影像能够提供丰富、精细的空间信息用于土地覆盖分类，这对于研究复杂的建成环境尤为重要。然而，由于土地覆盖格局复杂、训练样本采集成本高昂以及卫星影像存在严重的分布偏移问题，目前鲜有研究将高分辨率影像应用于大范围、详细类别的土地覆盖制图。为填补这一空白，我们提出了一个大规模土地覆盖数据集——Five-Billion-Pixels。该数据集包含150幅高分二号（4米分辨率）高分辨率卫星影像中超过50亿个标注像素，采用涵盖人工建造、农业和自然类别的24类分类体系进行标注。此外，我们提出了一种基于深度学习的无监督域适应方法，该方法可将标注数据集（称为源域）上训练的分类模型迁移至无标注数据（称为目标域），用于大范围土地覆盖制图。具体而言，我们引入了一种采用动态伪标签分配和类别平衡策略的端到端孪生网络，以实现自适应域联合学习。为验证本数据集及所提方法在不同传感器和不同地理区域间的泛化能力，我们分别利用PlanetScope（3米）、高分一号（8米）和哨兵二号（10米）卫星影像对中国五大城市群以及其他五个亚洲国家的六个城市进行了土地覆盖制图。在总计6万平方公里的研究区域内，尽管输入影像完全无标注，但实验结果仍显示出良好的应用前景。所提出的方法基于Five-Billion-Pixels数据集进行训练，能够实现中国全境及其他亚洲国家米级分辨率的高质量、精细化土地覆盖制图。