# A Self-Supervised Approach to Land Cover Segmentation

arXiv: https://arxiv.org/abs/2310.18251 · HTML: https://mlsanity.com/paper/2310.18251/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images](https://mlsanity.com/paper/1805.06561/) (2018)
- [Unsupervised Semantic Segmentation by Distilling Feature Correspondences](https://mlsanity.com/paper/2203.08414/) (2022)
- [Self-Supervised Learning with Swin Transformers](https://mlsanity.com/paper/2105.04553/) (2021)
- [LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery](https://mlsanity.com/paper/2005.02264/) (2020)
- [Vision Transformer: Vit and its Derivatives](https://mlsanity.com/paper/2205.11239/) (2022)

## Abstract (arXiv)

Land use/land cover change (LULC) maps are integral resources in earth science and agricultural research. Due to the nature of such maps, the creation of LULC maps is often constrained by the time and human resources necessary to accurately annotate satellite imagery and remote sensing data. While computer vision models that perform semantic segmentation to create detailed labels from such data are not uncommon, litle research has been done on self-supervised and unsupervised approaches to labelling LULC maps without the use of ground-truth masks. Here, we demonstrate a self-supervised method of land cover segmentation that has no need for high-quality ground truth labels. The proposed deep learning employs a frozen pre-trained ViT backbone transferred from DINO in a STEGO architecture and is fine-tuned using a custom dataset consisting of very high resolution (VHR) sattelite imagery. After only 10 epochs of fine-tuning, an accuracy of roughly 52% was observed across 5 samples, signifying the feasibility of self-supervised models for the automated labelling of VHR LULC maps.