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.

通过自我监督方法和深度学习，在没有高质量地面真实标签的情况下，可实现对高分辨率土地利用 / 土地覆盖变化图的自动标注，准确率达到约 52%。

自监督的土地覆盖分割方法

A Self-Supervised Approach to Land Cover Segmentation

This paper presents a framework to automate the labelling process for
gestures in musical performance videos with a 3D Convolutional Neural Network
(CNN). While this idea was proposed in a previous study, this paper introduces
several novelties: (i) Presents a novel method to overcome the class imbalance
challenge and make learning possible for co-existent gestures by batch
balancing approach and spatial-temporal representations of gestures. (ii)
Performs a detailed study on 7 and 18 categories of gestures generated during
the performance (guitar play) of musical pieces that have been video-recorded.
(iii) Investigates the possibility to use audio features. (iv) Extends the
analysis to multiple videos. The novel methods significantly improve the
performance of gesture identification by 12 %, when compared to the previous
work (51 % in this study over 39 % in previous work). We successfully validate
the proposed methods on 7 super classes (72 %), an ensemble of the 18
gestures/classes, and additional videos (75 %).

该论文介绍了一个自动化标注音乐表演视频中手势的框架，使用 3D 卷积神经网络，通过批处理平衡和空间 - 时间手势表示等方法，成功提高了 12% 的手势识别准确率，对跨多个视频的多个手势 / 类别进行了详细的研究，并探讨了使用音频特征的可能性。