As one road network is often the backbone in a geo-spatial dataset, capturing and/or updating the road networks using remote sensing imagery play an important role in traffic management, urban planning, vehicle navigation, and emergency management. Along with the progress of remote sensing launching technologies and the successful applications of deep learning in the field of computer vision, it has become more and more efficient and economical to employ deep learning methods for road-features extractions from very high-resolution (VHR) remote sensing imagery. Meanwhile, as one of the most significant and popular volunteer geographic information data sources, the OpenStreetMap (OSM) including the complete road networks in the wide world has been accumulated in the past decades. In this article, a generic and automatic approach for extracting road networks from VHR remote sensing images has been proposed based on fully convolutional neural network, in which the road centerlines from OSM have been employed to construct the labels for the model training and validation. In the conducted experiments on various VHR image datasets with two different spatial resolutions of 0.3 and 1 m, the proposed model has demonstrated quite satisfactory results – the overall completeness and correctness of the roads extraction from VHR remote sensing images exceed 94.0% and 98.0%, respectively.

Remote sensing images acquired by the FY4 satellite are crucial for regional cloud monitoring and meteorological services. Inspired by the success of deep learning networks in image super-resolution, we applied image super-resolution to FY4 visible spectrum (VIS) images. However, training a robust network directly for FY4 VIS image super-resolution remains challenging due to the limited provision of high resolution FY4 sample data. Here, we propose a super-resolution and transfer learning model, FY4-SR-Net. It is composed of pretraining and fine-tuning models. The pretraining model was developed using a deep residual network and a large number of FY4 A 4 and 1 km resolution VIS images as the training data. The knowledge derived from 4 km to 1 km resolution images was incorporated into FY4 B 1 km to 0.25 km resolution VIS images. The FY4-SR-Net is fine-tuned by incorporating limited 1 km and 0.25 km resolution panchromatic images, and then producing 1km super-resolution VIS images of the FY4 satellite. Using the one-day FY4 test dataset for qualitative and quantitative evaluations, the FY4-SR-Net outperformed the classic bicubic interpolation approach with a 16.12% reduction in root-mean-square error and a 2.97% rise in peak signal-to-noise ratio averages. The structural similarity value average increased by 0.0026. This article provides a new precedent for improving the spatial resolution of FY4 series meteorological satellites, which has important scientific significance and application properties.

As ground-level O ₃ has become one of the major pollutants affecting air quality in recent years, monitoring and revealing the spatial distribution pattern of O ₃ are of great significance to study air-pollution characteristics. Based on the multilayer backpropagation neural network, one O ₃ estimation model is proposed to obtain the continuous spatial distribution of O ₃ concentrations, where the Landsat 8 images, meteorological parameters, and air quality data have been integrated together as the input for the model training and validation. In order to enhance the estimation accuracy, the proposed model has been optimized with respect to the influencing factors and spatial extent. In the test areas of Beijing, Tianjin, and Shijiazhuang of China, the proposed O ₃ estimation model has demonstrated quite satisfactory performance—with the average coefficient of determination ( R ² ) larger than 0.90 and root-mean-square error smaller than 19.0 μ g/m ³ . It is worth mentioning that all the data employed in this research are freely available and can be applied nationwide in the mainland of China. Taking advantage of the generic nature and the positive O ₃ estimation results with high accuracy and spatial resolution, the proposed model can be expected to be a new way for studying air-pollution characteristics and, thus, support the decision making for environmental governance.

Referring Remote Sensing Image Segmentation (RRSIS) enables pixel-level analysis via text without predefined categories, but dataset construction is costly due to paired text-mask requirements. We propose a zero-shot RRSIS framework using pre-trained models to generate masks from text alone, avoiding training or fine-tuning. Existing zero-shot methods, effective for natural images, struggle with remote sensing’s complex semantics and spatial relationships, lacking category modeling and geospatial awareness. Therefore, we propose EKP-HRM, a novel zero-shot RRSIS framework integrating Expert Knowledge Priors (EKP) and Hierarchical Relational Matching (HRM). EKP enhances unseen category generalization through structured categorical semantics, while HRM decomposes scene descriptions for attribute-specific matching and contextual reasoning. Experiments on RRSIS-D show EKP-HRM outperforms prior methods.

High-resolution offshore digital elevation models (DEMs) are essential for coastal geomorphology, marine resource management, and disaster prevention. While deep learning-based super-resolution (SR) techniques have become a mainstream solution for enhancing DEMs, they often fail to maintain a balance between large-scale geomorphological structure and fine-scale topographic detail due to limitations in modeling spatial dependency. To overcome this challenge, we propose DEM-Asymmetric multi-scale super-resolution network (DEM-AMSSRN), a novel asymmetric multi-scale super-resolution network tailored for offshore DEM reconstruction. Our method incorporates region-level non-local (RL-NL) modules to capture long-range spatial dependencies and residual multi-scale blocks (RMSBs) to extract hierarchical terrain features. Additionally, a hybrid loss function combining pixel-wise, perceptual, and adversarial losses is introduced to ensure both geometric fidelity and visual realism. Experimental evaluations on U.S. offshore DEM datasets demonstrate that DEM-AMSSRN significantly outperforms existing GAN-based models, reducing RMSE by up to 72.47% (vs. SRGAN) and achieving 53.30 dB PSNR and 0.995056 SSIM. These results highlight its effectiveness in preserving both continental shelf-scale bathymetric patterns and detailed terrain textures. Using this model, we also constructed the USA_OD_2025, a 1/9 arc-second high-resolution offshore DEM for U.S. coastal zones, providing a valuable geospatial foundation for future marine research and engineering.