Accurate and up-to-date maps of built-up areas are crucial to support sustainable urban development. Earth Observation (EO) is a valuable data source to cover this demand. In particular, Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions offer new opportunities to map built-up areas on a global scale. Using Sentinel-2 images, recent urban mapping efforts achieved promising results by training Convolutional Neural Networks (CNNs) on available built-up data. However, these results strongly depend on the availability of local reference data for fully supervised training or assume that the application of CNNs to unseen areas (i.e. across-region generalization) produces satisfactory results. To alleviate these shortcomings, it is desirable to leverage Semi-Supervised Learning (SSL) algorithms that can take advantage of unlabeled data, especially because satellite data is plentiful. In this paper, we propose a novel Domain Adaptation (DA) approach using SSL that jointly exploits Sentinel-1 SAR and Sentinel-2 MSI to improve across-region generalization for built-up area mapping. Specifically, two identical sub-networks are incorporated into the proposed model to perform built-up area segmentation from SAR and optical images separately. Assuming that consistent built-up area segmentation should be obtained across data modality, we design an unsupervised loss for unlabeled data that penalizes inconsistent segmentation from the two sub-networks. Therefore, we propose to use complementary data modalities as real-world perturbations for consistency regularization. For the final prediction, the model takes both data modalities into account. Experiments conducted on a test set comprised of sixty representative sites across the world showed that the proposed DA approach achieves strong improvements (F1 score 0.694) over fully supervised learning from Sentinel-1 SAR data (F1 score 0.574), Sentinel-2 MSI data (F1 score 0.580) and their input-level fusion (F1 score 0.651). To demonstrate the effectiveness of DA, we also performed a comparison with two state-of-the-art products, namely GHS-BUILT-S2 and WSF 2019, on the test set. The comparison showed that our model is capable of producing built-up area maps with comparable or even better quality than the state-of-the-art global human settlement maps. Therefore, the multi-modal DA offers great potential to be adapted to produce easily updateable human settlements maps at a global scale.

准确和最新的建成区地图对于支持可持续城市发展至关重要。地球观测 (EO) 是满足这一需求的宝贵数据源。特别是，Sentinel-1 合成孔径雷达 (SAR) 和 Sentinel-2 多光谱仪器 (MSI) 任务提供了在全球范围内绘制建成区地图的新机会。使用 Sentinel-2 图像，最近的城市测绘工作通过在可用的构建数据上训练卷积神经网络 (CNN) 取得了可喜的成果。然而，这些结果在很大程度上取决于用于完全监督训练的本地参考数据的可用性，或者假设将 CNN 应用于看不见的区域（即跨区域泛化）会产生令人满意的结果。为了缓解这些缺点，最好利用可以利用未标记数据的半监督学习 (SSL) 算法，特别是因为卫星数据非常丰富。在本文中，我们提出了一种使用 SSL 的新型域适应 (DA) 方法，该方法联合利用 Sentinel-1 SAR 和 Sentinel-2 MSI 来改进建成区映射的跨区域泛化。具体来说，将两个相同的子网络合并到所提出的模型中，以分别从 SAR 和光学图像执行构建区域分割。假设应该跨数据模态获得一致的构建区域分割，我们为未标记数据设计了一个无监督损失，以惩罚来自两个子网络的不一致分割。因此，我们建议使用互补的数据模式作为真实世界的扰动来进行一致性正则化。对于最终预测，该模型将两种数据模式都考虑在内。在由全球 60 个代表性站点组成的测试集上进行的实验表明，与从 Sentinel-1 SAR 数据（F1 分数 0.574）、Sentinel-2 MSI 数据（ F1 得分 0.580）及其输入级融合（F1 得分 0.651）。为了证明 DA 的有效性，我们还在测试集上与两个最先进的产品，即 GHS-BUILT-S2 和 WSF 2019 进行了比较。比较表明，我们的模型能够生成与最先进的全球人类住区地图相当甚至更好质量的建成区地图。所以，