# PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances

arXiv: https://arxiv.org/abs/2401.09271 · HTML: https://mlsanity.com/paper/2401.09271/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://mlsanity.com/paper/2103.16607/) (2021)
- [Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.09910/) (2022)
- [Adversarial Learning for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/1802.07934/) (2018)
- [Self-Supervised Representation Learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2110.09327/) (2021)
- [Semantic Segmentation of Remote Sensing Images with Sparse Annotations](https://mlsanity.com/paper/2101.03492/) (2021)
- [Self-supervised Audiovisual Representation Learning for Remote Sensing Data](https://mlsanity.com/paper/2108.00688/) (2021)
- [Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks](https://mlsanity.com/paper/1608.00775/) (2016)
- [FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2208.00400/) (2022)
- [Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training](https://mlsanity.com/paper/2205.08416/) (2022)
- [Self Supervised Learning for Few Shot Hyperspectral Image Classification](https://mlsanity.com/paper/2206.12117/) (2022)

## Built on by (most cited first, 2 of 2)

- [Applications of Knowledge Distillation in Remote Sensing: A Survey](https://mlsanity.com/paper/2409.12111/) (2024)
- [Enhancing Monocular Height Estimation via Weak Supervision from Imperfect Labels](https://mlsanity.com/paper/2506.02534/) (2025)

## Abstract (arXiv)

Arctic Permafrost is facing significant changes due to global climate change. As these regions are largely inaccessible, remote sensing plays a crucial rule in better understanding the underlying processes not just on a local scale, but across the Arctic. In this study, we focus on the remote detection of retrogressive thaw slumps (RTS), a permafrost disturbance comparable to landslides induced by thawing. For such analyses from space, deep learning has become an indispensable tool, but limited labelled training data remains a challenge for training accurate models. To improve model generalization across the Arctic without the need for additional labelled data, we present a semi-supervised learning approach to train semantic segmentation models to detect RTS. Our framework called PixelDINO is trained in parallel on labelled data as well as unlabelled data. For the unlabelled data, the model segments the imagery into self-taught pseudo-classes and the training procedure ensures consistency of these pseudo-classes across strong augmentations of the input data. Our experimental results demonstrate that PixelDINO can improve model performance both over supervised baseline methods as well as existing semi-supervised semantic segmentation approaches, highlighting its potential for training robust models that generalize well to regions that were not included in the training data. The project page containing code and other materials for this study can be found at urlhttps://khdlr.github.io/PixelDINO/.