  Deep learning approaches have shown promising results in remote sensing high
spatial resolution (HSR) land-cover mapping. However, urban and rural scenes
can show completely different geographical landscapes, and the inadequate
generalizability of these algorithms hinders city-level or national-level
mapping. Most of the existing HSR land-cover datasets mainly promote the
research of learning semantic representation, thereby ignoring the model
transferability. In this paper, we introduce the Land-cOVEr Domain Adaptive
semantic segmentation (LoveDA) dataset to advance semantic and transferable
learning. The LoveDA dataset contains 5987 HSR images with 166768 annotated
objects from three different cities. Compared to the existing datasets, the
LoveDA dataset encompasses two domains (urban and rural), which brings
considerable challenges due to the: 1) multi-scale objects; 2) complex
background samples; and 3) inconsistent class distributions. The LoveDA dataset
is suitable for both land-cover semantic segmentation and unsupervised domain
adaptation (UDA) tasks. Accordingly, we benchmarked the LoveDA dataset on
eleven semantic segmentation methods and eight UDA methods. Some exploratory
studies including multi-scale architectures and strategies, additional
background supervision, and pseudo-label analysis were also carried out to
address these challenges. The code and data are available at
https://github.com/Junjue-Wang/LoveDA.
