  As an important application in remote sensing, landcover classification
remains one of the most challenging tasks in very-high-resolution (VHR) image
analysis. As the rapidly increasing number of Deep Learning (DL) based
landcover methods and training strategies are claimed to be the
state-of-the-art, the already fragmented technical landscape of landcover
mapping methods has been further complicated. Although there exists a plethora
of literature review work attempting to guide researchers in making an informed
choice of landcover mapping methods, the articles either focus on the review of
applications in a specific area or revolve around general deep learning models,
which lack a systematic view of the ever advancing landcover mapping methods.
In addition, issues related to training samples and model transferability have
become more critical than ever in an era dominated by data-driven approaches,
but these issues were addressed to a lesser extent in previous review articles
regarding remote sensing classification. Therefore, in this paper, we present a
systematic overview of existing methods by starting from learning methods and
varying basic analysis units for landcover mapping tasks, to challenges and
solutions on three aspects of scalability and transferability with a remote
sensing classification focus including (1) sparsity and imbalance of data; (2)
domain gaps across different geographical regions; and (3) multi-source and
multi-view fusion. We discuss in detail each of these categorical methods and
draw concluding remarks in these developments and recommend potential
directions for the continued endeavor.
