Unsupervised domain adaptation (UDA) is a challenging open problem in land cover mapping. Previous studies show encouraging progress in addressing cross-domain distribution shifts on remote sensing benchmarks for land cover mapping. The existing works are mainly built on large neural network architectures, which makes them resource-hungry systems, limiting their practical impact for many real-world applications in resource-constrained environments. Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts. This is achieved by integrating Markov random field neural architecture search (MRF-NAS) into a self-training UDA framework to search for efficient and effective networks under a limited computation budget. This is the first attempt to combine NAS with self-training UDA as a single framework for land cover mapping. We also investigate two different pseudo-labelling approaches (confidence-based and energy-based) in self-training scheme. Experimental results on two recent datasets (OpenEarthMap & FLAIR #1) for remote sensing UDA demonstrate a satisfactory performance. With only less than 2M parameters and 30.16 GFLOPs, the best-discovered lightweight network reaches state-of-the-art performance on the regional target domain of OpenEarthMap (59.38% mIoU) and the considered target domain of FLAIR #1 (51.19% mIoU). The code is at https://github.com/cliffbb/UDA-NAS}{https://github.com/cliffbb/UDA-NAS.

翻译：无监督域适应（UDA）是土地覆盖制图中一个具有挑战性的开放问题。以往的研究在应对遥感基准数据中跨域分布偏移方面取得了令人鼓舞的进展。现有工作主要依赖于大型神经网络架构，这使得系统资源消耗巨大，限制了其在资源受限环境下的实际应用。为此，我们提出了一种简单而有效的框架，能够在域偏移条件下自动搜索轻量级神经网络以完成土地覆盖制图任务。该框架通过将马尔可夫随机场神经架构搜索（MRF-NAS）集成到自训练UDA框架中实现，从而在有限的计算预算下搜索高效且有效的网络。这是首次将NAS与自训练UDA作为统一框架应用于土地覆盖制图。我们还研究了自训练方案中两种不同的伪标签方法（基于置信度和基于能量）。在两个最新遥感UDA数据集（OpenEarthMap 和 FLAIR #1）上的实验结果表明，该方法取得了令人满意的性能。最佳发现的轻量级网络参数不足2M，计算量为30.16 GFLOPs，在OpenEarthMap的区域目标域上达到了59.38% mIoU的先进性能，在FLAIR #1的目标域上达到了51.19% mIoU。代码地址为https://github.com/cliffbb/UDA-NAS。