PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances
This work addresses the challenge of training robust semantic segmentation models for permafrost disturbance detection in remote Arctic regions, where data scarcity is a bottleneck, offering an incremental improvement in generalization without additional labeled data.
The paper tackles the problem of detecting retrogressive thaw slumps (RTS) in Arctic permafrost using remote sensing, where limited labeled data hinders model accuracy, and presents PixelDINO, a semi-supervised learning framework that improves model performance over supervised baselines and existing semi-supervised methods.
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 \url{https://khdlr.github.io/PixelDINO/}.