Despite its success, deep learning in land cover mapping requires a massive amount of pixel-wise labeled images. It typically assumes that the training and test scenes are similar in data distribution. The performance of models trained on any particular dataset could degrade significantly on a new dataset due to the domain shift or domain gap across datasets, resulting in new training data requiring labor-intensive manual pixel-wise labeling. This paper proposes a land cover mapping framework combining Feature Pyramid Network (FPN) and self-training. In the FPN, we integrate ConvNeXt with a Pyramid Pooling Module (PPM). Combining the FPN and the PPM improves the segmentation performance, which benefits from the multiscale aggregation of pyramid features. To fully exploit pseudo-labels, we design an Unsupervised Domain Adaptation (UDA) land cover mapping scheme with self-training using weighted pseudo-labels of the target samples. The proposed land cover mapping framework could benefit from multiscale aggregation of pyramid features and the full use of the pseudo-labels. Comparison results on the LoveDA dataset, the latest large-scale unsupervised domain adaptation dataset for land cover mapping, empirically demonstrated that our land cover mapping approach significantly outperforms the baselines in both UDA scenarios, i.e., Urban $\to$ Rural and Rural $\to$ Urban. The models of this paper are now publicly available on GitHub.¹

尽管取得了成功，但土地覆盖映射中的深度学习需要大量的像素级标记图像。它通常假设训练和测试场景在数据分布上是相似的。由于数据集之间的域转移或域间隙，在任何特定数据集上训练的模型的性能可能会在新数据集上显着降低，从而导致新的训练数据需要劳动密集型的手动逐像素标记。本文提出了一种结合特征金字塔网络（FPN）和自训练的土地覆盖映射框架。在 FPN 中，我们将 ConvNeXt 与 Pyramid Pooling Module (PPM) 集成在一起。结合 FPN 和 PPM 提高了分割性能，这得益于金字塔特征的多尺度聚合。为了充分利用伪标签，我们设计了一个无监督域适应（UDA）土地覆盖映射方案，使用目标样本的加权伪标签进行自我训练。所提出的土地覆盖制图框架可以受益于金字塔特征的多尺度聚合和伪标签的充分利用。LoveDA 数据集（用于土地覆盖制图的最新大规模无监督域适应数据集）的比较结果经验证明，我们的土地覆盖制图方法在两种 UDA 场景中都显着优于基线，即城市的 $\to$ 农村和农村 $\to$ 城市。本文的模型现已在 GitHub 上公开提供。¹