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As an important application in remote sensing, landcover classificationremains one of the most challenging tasks in very-high-resolution (VHR) imageanalysis. As the rapidly increasing number of Deep Learning (DL) basedlandcover methods and training strategies are claimed to be thestate-of-the-art, the already fragmented technical landscape of landcovermapping methods has been further complicated. Although there exists a plethoraof literature review work attempting to guide researchers in making an informedchoice of landcover mapping methods, the articles either focus on the review ofapplications 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 havebecome more critical than ever in an era dominated by data-driven approaches,but these issues were addressed to a lesser extent in previous review articlesregarding remote sensing classification. Therefore, in this paper, we present asystematic overview of existing methods by starting from learning methods andvarying basic analysis units for landcover mapping tasks, to challenges andsolutions on three aspects of scalability and transferability with a remotesensing classification focus including (1) sparsity and imbalance of data; (2)domain gaps across different geographical regions; and (3) multi-source andmulti-view fusion. We discuss in detail each of these categorical methods anddraw concluding remarks in these developments and recommend potentialdirections for the continued endeavor.

A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability