Super-resolution methods have significantly contributed to the improvement of the spatial resolution of remote sensing images. The development of deep learning empowers novel methods to learn informative feature representation from massive low-resolution (LR) and high-resolution (HR) image pairs. Conventional remote sensing image super-resolution methods, however, may fail in large-scale (×8,×9) super-resolution tasks. Specifically, a larger scale factor corresponds to the less information in LR images, which is a considerable challenge to super-resolution. To address the issue, we propose a novel method for single remote sensing image super-resolution (SRSISR) based on stratified dense-sampling to effectively extract image features. Specifically, the proposed super-resolution dense-sampling residual attention network (SRDSRAN) combines dense-sampling and residual learning to improve multi-level feature fusion and gradient propagation, and employs local and global attentions to learn important features and long-range interdependence in the channel and spatial dimensions. Meanwhile, we also devise a discriminator model using local and global attentions, and with the loss function integrating L ₁ pixel loss, L ₁ perceptual loss, and relativistic adversarial loss to obtain the perceptually realistic images. Besides, we introduce a chain training to promote the performance and expedite the training process for large-scale super-resolution. Experimental results on UC Merced image and other multispectral data demonstrated that our SRDSRAN outperformed the current state-of-the-art methods quantitatively and in visual quality, and obtained a higher classification accuracy in scene classification, proving its potential for applications with other downstream tasks. We will open source our code at https://github.com/Meng-333/SRADSGAN.

Online reviews are crucial for identifying the factors that affect the dynamic evolution of tourist sentiment, improving tourist satisfaction. This study employs pre-trained models BERT and BERTopic and social network analysis to examine 228,062 reviews collected from Ctrip using Python. The factors influencing tourist sentiment across natural tourism attractions (NTAs), cultural tourism attractions (CTAs), and theme park tourism attractions (TPTAs) were explored before, during, and after the pandemic. The findings reveal that there was minimal change in the types of factors influencing before and during the pandemic, significant changes in the values of factors during the pandemic, and fluctuations in both the types and values of factors after the pandemic. Regardless of the period, influences on negative sentiment were more dispersed, while positive emotions were more polarized. Based on these insights, we propose theoretical contributions and improvement strategies for enhancing resilience and promoting high-quality development in different types of attractions.

The prediction of tropical cyclone (TC) intensity remains a challenging task. Although satellite imagery is suitable for time-sensitive TC intensity prediction tasks due to its low-latency acquisition, it is underutilized in existing studies because of the complexities involved in modeling dynamic TC features from satellite imagery. In this study, a multi-task neural network framework that leverages satellite imagery as predictors for forecasting future TC intensity is proposed. Specifically, an encoding block is designed to integrate prior information into satellite imagery, and a multi-task strategy is employed to enhance modeling capabilities by learning multiple objectives from shared representations. Furthermore, a physics-guided constraint loss is constructed to exploit the cross-fusion features and mutual constraints of the predicted indicators. The experimental results demonstrate that models built upon the proposed framework achieve competitive performance in TC intensity prediction, which validates the generalization and effectiveness of the framework. Additionally, the ablation studies confirm that both the multi-task strategy and the physics-guided constraint loss effectively improve the prediction performance.