# Land Cover Mapping in Limited Labels Scenario: A Survey

arXiv: https://arxiv.org/abs/2103.02429 · HTML: https://mlsanity.com/paper/2103.02429/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 73 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models](https://mlsanity.com/paper/1807.05713/) (2018)
- [Tile2Vec: Unsupervised representation learning for spatially distributed data](https://mlsanity.com/paper/1805.02855/) (2018)
- [SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion](https://mlsanity.com/paper/1906.07789/) (2019)
- [Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping](https://mlsanity.com/paper/1510.00098/) (2015)
- [Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples](https://mlsanity.com/paper/2010.00882/) (2020)
- [Meta-Learning for Few-Shot Land Cover Classification](https://mlsanity.com/paper/2004.13390/) (2020)
- [The color out of space: learning self-supervised representations for Earth Observation imagery](https://mlsanity.com/paper/2006.12119/) (2020)
- [Generating Interpretable Poverty Maps using Object Detection in Satellite Images](https://mlsanity.com/paper/2002.01612/) (2020)
- [Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale](https://mlsanity.com/paper/1704.02965/) (2017)
- [Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery](https://mlsanity.com/paper/1905.01447/) (2019)
- [Few-shot Object Detection on Remote Sensing Images](https://mlsanity.com/paper/2006.07826/) (2020)
- [RS-MetaNet: Deep meta metric learning for few-shot remote sensing scene classification](https://mlsanity.com/paper/2009.13364/) (2020)
- [Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery](https://mlsanity.com/paper/1712.03323/) (2017)
- [Unsupervised Deep Feature Extraction for Remote Sensing Image Classification](https://mlsanity.com/paper/1511.08131/) (2015)
- [Lightweight and Robust Representation of Economic Scales from Satellite Imagery](https://mlsanity.com/paper/1912.08197/) (2019)
- [Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities](https://mlsanity.com/paper/2002.08254/) (2020)

## Built on by (most cited first, 3 of 3)

- [CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels](https://mlsanity.com/paper/2107.12499/) (2021)
- [Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping](https://mlsanity.com/paper/2108.07323/) (2021)
- [Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2302.01793/) (2023)

## 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.