At present, with the advance of satellite image processing technology, remote sensing images are becoming more widely used in real scenes. However, due to the limitations of current remote sensing imaging technology and the influence of the external environment, the resolution of remote sensing images often struggles to meet application requirements. In order to obtain high-resolution remote sensing images, image super-resolution methods are gradually being applied to the recovery and reconstruction of remote sensing images. The use of image super-resolution methods can overcome the current limitations of remote sensing image acquisition systems and acquisition environments, solving the problems of poor-quality remote sensing images, blurred regions of interest, and the requirement for high-efficiency image reconstruction, a research topic that is of significant relevance to image processing. In recent years, there has been tremendous progress made in image super-resolution methods, driven by the continuous development of deep learning algorithms. In this paper, we provide a comprehensive overview and analysis of deep-learning-based image super-resolution methods. Specifically, we first introduce the research background and details of image super-resolution techniques. Second, we present some important works on remote sensing image super-resolution, such as training and testing datasets, image quality and model performance evaluation methods, model design principles, related applications, etc. Finally, we point out some existing problems and future directions in the field of remote sensing image super-resolution.

Recently, network representation learning has been widely used to mine and analyze network characteristics, and it is also applied to blockchain, but most of the embedding methods in blockchain ignore the heterogeneity of network, so it is difficult to accurately describe the characteristics of the transaction. As smart society evolves, Ethereum makes smart contracts reality, while the mine of transaction characteristics appearing on the Ethereum platform is scarce; thus, there is an urgent need to mine Ethereum from contract and transfer. In this article, we propose a heterogeneous network representation learning method to mine implicit information inside Ethereum transactions. Specifically, we construct an Ethereum transaction network by collecting transaction data from normal and phishing Ethereum accounts. Then, we propose a walk strategy that combines timestamps and transaction amounts to represent the information that occurs at the time of a transaction. To mine the types of nodes and edges, we use a heterogeneous network representation learning method to map the transaction network to a low-dimensional space. Finally, we improve the accuracy of the embedding results in the node classification task, which has important implications for Ethereum mining as well as identity recognition.

Lane detection is crucial for autonomous driving systems (ADS), utilizing sensors like cameras and LiDAR to identify lanes and understand vehicle position, direction, and lane shape. It provides data support for the control system to make informed driving decisions. In this survey, we review recent advancements in lane detection, focusing on both 2D techniques and emerging 3D methods. We begin with an overview of the significance of lane detection in ADS, followed by an analysis of the evolution of 2D techniques over the past decade, covering traditional and deep learning approaches. We also examine recent advancements in 3D lane detection. Additionally, we summarize evaluation metrics and popular datasets in the field. Finally, we discuss current challenges and future directions in lane detection, aiming to provide valuable insights for researchers and developers in this technology.

Remote sensing image segmentation has large uncertainty related to the heterogeneity of similar objects and complex spectrum in satellite images, causing the traditional segmentation methods to be greatly limited. Existing semantic segmentation methods represented by deep learning have made breakthrough progress. However, traditional deep learning methods, such as deep convolution neural network, are a completely deterministic model, which cannot describe the uncertainty of remote sensing image well. To solve this problem, a new deep neural network combined with fuzzy logic units is proposed in this paper, called RSFCNN (Remote Sensing image segmentation with Fuzzy Convolutional Neural Network). The network integrates convolution units and fuzzy logic units. Convolution units are used to extract discriminant features with different proportions, thus providing comprehensive information for pixel-level remote sensing image segmentation. Fuzzy logic units are used to deal with various uncertainties and provide more reliable segmentation results, and each unit handles the feature maps at a particular image scale by Gaussian blur function. Experiments were carried out on two data sets, and the results showed that RSFCNN has higher segmentation accuracy and better performance than state-of-the-art algorithms.