mlsanityData to 7 Oct 2026

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

arXiv 2202.03342 ↗First posted 7 Feb 2022eess.IV cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 55 of its 192 references that are in the map. First posted on arXiv 7 Feb 2022.

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55 of 192 references.

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Imagescited by 6
  2. 2024Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mappingcited by 4
  3. 2023Reflections from the Workshop on AI-Assisted Decision Making for Conservationcited by 2
  4. 2023Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentationcited by 0