  This paper proposes a novel domain adaptation algorithm to handle the
challenges posed by the satellite and aerial imagery, and demonstrates its
effectiveness on the built-up region segmentation problem. Built-up area
estimation is an important component in understanding the human impact on the
environment, the effect of public policy, and general urban population
analysis. The diverse nature of aerial and satellite imagery and lack of
labeled data covering this diversity makes machine learning algorithms
difficult to generalize for such tasks, especially across multiple domains. On
the other hand, due to the lack of strong spatial context and structure, in
comparison to the ground imagery, the application of existing unsupervised
domain adaptation methods results in the sub-optimal adaptation. We thoroughly
study the limitations of existing domain adaptation methods and propose a
weakly-supervised adaptation strategy where we assume image-level labels are
available for the target domain. More specifically, we design a built-up area
segmentation network (as encoder-decoder), with an image classification head
added to guide the adaptation. The devised system is able to address the
problem of visual differences in multiple satellite and aerial imagery
datasets, ranging from high resolution (HR) to very high resolution (VHR). A
realistic and challenging HR dataset is created by hand-tagging the 73.4 sq-km
of Rwanda, capturing a variety of build-up structures over different terrain.
The developed dataset is spatially rich compared to existing datasets and
covers diverse built-up scenarios including built-up areas in forests and
deserts, mud houses, tin, and colored rooftops. Extensive experiments are
performed by adapting from the single-source domain, to segment out the target
domain. We achieve high gains ranging 11.6%-52% in IoU over the existing
state-of-the-art methods.
