# Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping

arXiv: https://arxiv.org/abs/2108.07323 · HTML: https://mlsanity.com/paper/2108.07323/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 26 of 26)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [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)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Learning Representations for Automatic Colorization](https://mlsanity.com/paper/1603.06668/) (2016)
- [Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction](https://mlsanity.com/paper/1611.09842/) (2016)
- [Joint Unsupervised Learning of Deep Representations and Image Clusters](https://mlsanity.com/paper/1604.03628/) (2016)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Colorization as a Proxy Task for Visual Understanding](https://mlsanity.com/paper/1703.04044/) (2017)
- [Representation Learning by Learning to Count](https://mlsanity.com/paper/1708.06734/) (2017)
- [Tile2Vec: Unsupervised representation learning for spatially distributed data](https://mlsanity.com/paper/1805.02855/) (2018)
- [In-domain representation learning for remote sensing](https://mlsanity.com/paper/1911.06721/) (2019)
- [Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping](https://mlsanity.com/paper/1510.00098/) (2015)
- [The color out of space: learning self-supervised representations for Earth Observation imagery](https://mlsanity.com/paper/2006.12119/) (2020)
- [Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification](https://mlsanity.com/paper/2003.02899/) (2020)
- [Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping](https://mlsanity.com/paper/2105.02963/) (2021)
- [Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data](https://mlsanity.com/paper/2001.00994/) (2020)
- [Land Cover Change Detection via Semantic Segmentation](https://mlsanity.com/paper/1911.12903/) (2019)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)

## Built on by (most cited first, 6 of 6)

- [Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin](https://mlsanity.com/paper/2301.00363/) (2023)
- [Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach](https://mlsanity.com/paper/2110.10275/) (2021)
- [SRAI: Towards Standardization of Geospatial AI](https://mlsanity.com/paper/2310.13098/) (2023)
- [Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data](https://mlsanity.com/paper/2304.14541/) (2023)
- [Unsupervised Urban Land Use Mapping with Street View Contrastive Clustering and a Geographical Prior](https://mlsanity.com/paper/2504.17551/) (2025)
- [Self-supervised cross-modality learning for uncertainty-aware object detection and recognition in applications which lack pre-labelled training data](https://mlsanity.com/paper/2411.03082/) (2024)

## Abstract (arXiv)

Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a major obstacle for training advanced machine learning models. Common techniques of addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors. Self-supervised learning is an alternative approach that learns feature representation from unlabeled images without using any human annotations. In this paper, we introduce a new method for land cover mapping by using a clustering based pretext task for self-supervised learning. We demonstrate the effectiveness of the method on two societally relevant applications from the aspect of segmentation performance, discriminative feature representation learning and the underlying cluster structure. We also show the effectiveness of the active sampling using the clusters obtained from our method in improving the mapping accuracy given a limited budget of annotating.