  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.
