A Self-Supervised Approach to Land Cover Segmentation
Charles Moore Dakota Hester
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.