Deep learning-based super-resolution (SR) techniques play a crucial role in enhancing the spatial resolution of images. However, remote sensing images present substantial challenges due to their diverse features, complex structures, and significant size variations in ground objects. Moreover, recovering lost details from low-resolution remote sensing images with complex and unknown degradations, such as downsampling, noise, and compression, remains a critical issue. To address these challenges, we propose ConvMambaSR, a novel super-resolution framework that integrates state-space models (SSMs) and Convolutional Neural Networks (CNNs). This framework is specifically designed to handle heterogeneous and complex ground features, as well as unknown degradations in remote sensing imagery. ConvMambaSR leverages SSMs to model global dependencies, activating more pixels in the super-resolution task. Concurrently, it employs CNNs to extract local detail features, enhancing the model’s ability to capture image textures and edges. Furthermore, we have developed a global–detail reconstruction module (GDRM) to integrate diverse levels of global and local information efficiently. We rigorously validated the proposed method on two distinct datasets, RSSCN7 and RSSRD-KQ, and benchmarked its performance against state-of-the-art SR models. Experiments show that our method achieves SOTA PSNR values of 26.06 and 24.29 on these datasets, respectively, and is visually superior, effectively addressing a variety of scenarios and significantly outperforming existing methods.

A novel aerobic, yellow-pigmented, Gram-staining-negative, rod-shaped bacterial strain, CUG00004T, was isolated from a soil sample. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain CUG00004T was a member of the genus Flavobacterium and showed high sequence similarity with Flavobacterium soli DSM 19725T (96.9%) and Flavobacterium glaciei CGMCC 0499T (95.6%). The level of DNA-DNA relatedness between strain CUG00004T and Flavobacterium soli DSM 19725T and Flavobacterium glaciei CGMCC 1.5380T was 42.5±0.7 % and 43.4 %, respectively. Strain CUG00004T was alkaline and grew optimally at pH 8.0 at 28 ºC and in the presence of 0%-1% (w/v) NaCl. This organism contained menaquinone-6 (MK-6) as the only isoprenoid quinone and iso-C15:0, iso-C17:0 3-OH, anteiso-C15:0 and summed feature 9 (comprising iso-C17:1 ω9c and/or C16:0 10-methyl) as the major fatty acids. The DNA G+C content of strain CUG00004T was 36.3 mol %. On the basis of phylogenetic, physiological and chemotaxonomic analyses, strain CUG00004T represents a novel species, for which Flavobacterium terriphilum is proposed, with strain CUG00004T (=CCTCC AB 2014151T=KCTC 42876T) as the type strain.

In recent years, deep neural networks, including Convolutional Neural Networks, Transformers, and State Space Models, have achieved significant progress in Remote Sensing Image (RSI) Super-Resolution (SR). However, existing SR methods typically overlook the complementary relationship between global and local dependencies. These methods either focus on capturing local information or prioritize global information, which results in models that are unable to effectively capture both global and local features simultaneously. Moreover, their computational cost becomes prohibitive when applied to large-scale RSIs. To address these challenges, we introduce the novel application of Receptance Weighted Key Value (RWKV) to RSI-SR, which captures long-range dependencies with linear complexity. To simultaneously model global and local features, we propose the Global-Detail dual-branch structure, GDSR, which performs SR by paralleling RWKV and convolutional operations to handle large-scale RSIs. Furthermore, we introduce the Global-Detail Reconstruction Module (GDRM) as an intermediary between the two branches to bridge their complementary roles. In addition, we propose the Dual-Group Multi-Scale Wavelet Loss, a wavelet-domain constraint mechanism via dual-group subband strategy and cross-resolution frequency alignment for enhanced reconstruction fidelity in RSI-SR. Extensive experiments under two degradation methods on several benchmarks, including AID, UCMerced, and RSSRD-QH, demonstrate that GSDR outperforms the state-of-the-art Transformer-based method HAT by an average of 0.09 dB in PSNR, while using only 63% of its parameters and 51% of its FLOPs, achieving an inference speed 3.2 times faster.

We propose the Enhanced Predictive Recurrent Neural Network (EnPredRNN) based on PredRNN to extend the period of spatio-temporal prediction. To better capture global spatial dependencies, we integrate the Self-Attention (SA) module into PredRNN’s basic unit, forming the Enhanced Long Short-Term Memory (EnLSTM). Aiming at the problem that the standard temporal memory state in PredRNN contains insufficient information about inter-frame motion, we propose the Enhanced Temporal Memory (ETM) by aggregating past multi-step temporal memory states. Aiming at the gradient vanishing problem in Recurrent Neural Networks (RNN), the Alleviating Gradient Vanishing (AGV) structure is used to construct the high-speed path that facilitates gradient propagation. Experimental results show that EnPredRNN effectively extends spatio-temporal prediction from ten to thirty time-steps.

Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based and recurrent-free models. The critical success index (CSI), probability of detection (POD), false alarm rate (FAR), mean square error (MSE), mean absolute error (MAE), and learned perceptual image patch similarity (LPIPS), were used to evaluate the forecasting abilities. The results showed that (1) recurrent-free models have significant parameter quantity and computing power advantages, especially the SimVP model. Among the recurrent-based models, PredRNN and PredRNN++ demonstrate good predictive capabilities for changes in echolocation and intensity, PredRNN++ performs better in predicting long sequences (1 h); (2) SimVP uses Inception to extract temporal features, which cannot capture the complex physical changes in radar echo images and fails to extract spatial–temporal correlations and accurately predict heavy rainfall areas effectively. Therefore, we constructed the SimVP-GMA model, replacing the temporal prediction module in SimVP and modifying the spatial encoder part. Compared with SimVP, the MSE and LPIPS indicators were improved by 0.55 and 0.0193, respectively. It can be seen from the forecast images that the forecast details have been significantly improved, especially in the forecasting of heavy rainfall weather.

Deep learning for precipitation forecasting remains constrained by complex meteorological factors affecting accuracy. To address this issue, this paper proposes TransMambaCNN, which is a spatiotemporal transformer network fusing state-space models and CNNs for short-term precipitation forecasting. The core of the model employs a Convolutional State-Space Module (C-SSM), which efficiently extracts spatiotemporal features from multi-source meteorological variables by replacing the self-attention mechanism in the Vision Transformer (ViT) with an Attentive State-Space Module (ASSM) and augmenting its feature extraction capacity with integrated depthwise convolution. Its dual-branch architecture consists of a global branch, where C-SSM captures long-range dependencies and global spatiotemporal patterns, and a local branch, which leverages multi-scale convolutions based on SimVP’s Inception structure to extract fine-grained local features. The deep fusion of these dual branches significantly enhances spatiotemporal feature representation.Experiments demonstrate that in southeastern China and adjacent marine areas (period of high precipitation: April–September), TransMambaCNN achieves a 13.38% and 47.67% improvement in Threat Score (TS) over PredRNN at thresholds of ≥25 mm and ≥50 mm, respectively. In the Qinghai Sanjiangyuan region of western China (a precipitation-scarce area), TransMambaCNN’s TS score surpasses SimVP by 11.86 times at the ≥25 mm threshold.