mlsanityData to 7 Oct 2026

Land Cover Mapping in Limited Labels Scenario: A Survey

arXiv 2103.02429 ↗First posted 3 Mar 2021cs.LG cs.CVcited by 3

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Abstract (arXiv)

Land cover mapping is essential for monitoring global environmental change and managing natural resources. Unfortunately, traditional classification models are plagued by limited training data available in existing land cover products and data heterogeneity over space and time. In this survey, we provide a structured and comprehensive overview of challenges in land cover mapping and machine learning methods used to address these problems. We also discuss the gaps and opportunities that exist for advancing research in this promising direction.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 33 of its 73 references that are in the map. First posted on arXiv 3 Mar 2021.

Builds on

33 of 73 references.

References · 33

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21 more
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Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 1.

3 papers.

  1. 2021CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labelscited by 8
  2. 2021Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mappingcited by 6
  3. 2023Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classificationcited by 4