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.

翻译：土地利用/土地覆盖变化（LULC）地图是地球科学和农业研究中不可或缺的资源。由于此类地图的性质，其制作往往受限于精确标注卫星影像和遥感数据所需的时间和人力资源。尽管利用计算机视觉模型对遥感数据进行语义分割以生成详细标注的做法并不罕见，但关于采用自监督和无监督方法在没有真实掩模的情况下标注LULC地图的研究却很少。本文展示了一种无需高质量真实标注的自监督土地覆盖分割方法。所提出的深度学习模型采用冻结的预训练ViT骨干网络（从DINO迁移至STEGO架构），并使用包含极高分辨率（VHR）卫星影像的自定义数据集进行微调。经过仅10个周期的微调，在5个样本上观测到约52%的准确率，这表明自监督模型用于VHR LULC地图自动化标注具有可行性。