Cross-view geo-localization has garnered notable attention in the realm of
computer vision, spurred by the widespread availability of copious geotagged
datasets and the advancements in machine learning techniques. This paper
provides a thorough survey of cutting-edge methodologies, techniques, and
associated challenges that are integral to this domain, with a focus on
feature-based and deep learning strategies. Feature-based methods capitalize on
unique features to establish correspondences across disparate viewpoints,
whereas deep learning-based methodologies deploy convolutional neural networks
to embed view-invariant attributes. This work also delineates the multifaceted
challenges encountered in cross-view geo-localization, such as variations in
viewpoints and illumination, the occurrence of occlusions, and it elucidates
innovative solutions that have been formulated to tackle these issues.
Furthermore, we delineate benchmark datasets and relevant evaluation metrics,
and also perform a comparative analysis of state-of-the-art techniques.
Finally, we conclude the paper with a discussion on prospective avenues for
future research and the burgeoning applications of cross-view geo-localization
in an intricately interconnected global landscape.

本文针对跨视域地理定位领域的前沿方法、技术和挑战进行了综述，着重介绍了基于特征和深度学习策略。研究涵盖了建立不同视点之间对应关系的基于特征的方法，以及采用卷积神经网络嵌入视图不变属性的深度学习方法。对跨视域地理定位遇到的各种挑战进行了探讨，如视点和照明的变化、遮挡的发生，并提出了相应的创新解决方案。此外，本文介绍了基准数据集和评估指标，并对最先进技术进行了比较分析。最后，对未来研究以及跨视域地理定位在全球复杂网络环境中蓬勃发展的应用进行了讨论。

跨视角地理定位：综述

Cross-view geo-localization: a survey

The segmentation of kidney layer structures, including cortex, outer stripe,
inner stripe, and inner medulla within human kidney whole slide images (WSI)
plays an essential role in automated image analysis in renal pathology.
However, the current manual segmentation process proves labor-intensive and
infeasible for handling the extensive digital pathology images encountered at a
large scale. In response, the realm of digital renal pathology has seen the
emergence of deep learning-based methodologies. However, very few, if any, deep
learning based approaches have been applied to kidney layer structure
segmentation. Addressing this gap, this paper assesses the feasibility of
performing deep learning based approaches on kidney layer structure
segmetnation. This study employs the representative convolutional neural
network (CNN) and Transformer segmentation approaches, including Swin-Unet,
Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We
quantitatively evaluated six prevalent deep learning models on renal cortex
layer segmentation using mice kidney WSIs. The empirical results stemming from
our approach exhibit compelling advancements, as evidenced by a decent Mean
Intersection over Union (mIoU) index. The results demonstrate that Transformer
models generally outperform CNN-based models. By enabling a quantitative
evaluation of renal cortical structures, deep learning approaches are promising
to empower these medical professionals to make more informed kidney layer
segmentation.

这篇论文评估了深度学习方法在肾脏层结构分割上的可行性，研究采用了代表性的卷积神经网络和 Transformer 分割方法，并在小鼠肾脏癌症 WSIs 上定量评估了六个深度学习模型，结果显示 Transformer 模型通常优于基于 CNN 的模型。