Ship detection in remote sensing images has been attracting a lot of attention due to its great application value in both military and civilian fields. However, ships in high-resolution remote sensing images are characterized by the remarkable features of multi-scale, arbitrary orientation and dense arrangement, which is a great challenge for fast and accurate target detection. In order to solve problems, we propose a YOLOV5-based oriented ship detection method of remote sensing image with contextual global attention mechanism and lightweight task-specific context decoupling (CGTC-RYOLO) in this paper. First, a cross-stage partial context transformer (CSP-COT) module is introduced to capture global contextual spatial relations using multi-head self-attention to verify their implications in implicit dependencies. Second, we propose an angle classification prediction branch in the YOLOV5 head network for detecting targets in any direction and design probability and distribution loss function (PrfoIoU) to optimize the regression effect. Third, the lightweight task-specific context decoupling (LTSCODE) for target detection is employed to replace the original head in the YOLOV5 model, which is used to solve the accuracy problem caused by YOLOV5’s hybridization of classification and localization. Ablation experiments demonstrate the importance and effectiveness of each module. Compared with the benchmark model, the CGTC-RYOLO has the 5.9%, 3.7%, and 4.3% mAP improvements on the DOTA-ship dataset, the HRSC2016 dataset, and the UCAS-AOD dataset, respectively. Moreover, the model’s generalization is also validated. Compared with state-of-the-art methods, the CGTC-RYOLO can achieve better accuracy and less parameters.

Synthetic Aperture Radar (SAR) images are crucial for maritime vessel detection; however, challenges such as blurred ship edges, strong land scattering interference, and angular regression mismatches across varying target sizes hinder accurate rotational localization. In this paper, an oriented decoupling target detection method (R-MCLST) is proposed to address these issues. The method integrates three key modules: a multi-channel positioning module (MC-PM) that employs distributed average pooling and additional coordinate channels to enhance orientation awareness; a soft threshold-based multilayer perceptron (ST-MLP) that effectively mitigates background interference while robustly extracting complex features; and a Gaussian distribution-based prediction box (GD-BPB) that transforms rotated bounding box encoding into a two-dimensional Gaussian distribution using KL divergence for adaptive parameter adjustment. Experimental evaluations on the R-SSDD and MR-HRSID datasets demonstrate that R-MCLST achieves superior performance, with the R-SSDD dataset yielding AP50 of 87.48%, AP75 of 34.96%, AP of 41.15%, and AR of 46.52%, and the MR-HRSID dataset yielding AP50 of 61.59%, AP75 of 4.96%, AP of 19.13%, and AR of 22.24%. Comparative analyses confirm that the proposed method outperforms current state-of-the-art networks in accurately localizing rotating targets under challenging SAR imaging conditions.

Flash flood can cause dramatic damages to the infrastructure of transportation networks. Because of the uncertainty of flash floods and the access function of roads, assessing the impact of flash floods on transportation systems has been a great challenge. In this study, we proposed a framework for evaluating the risk posed by flash floods to transportation network, which considers the interruption probability of road links and the expected impact on the access function of transportation. To assess the probability of interruption, we used the random forest model to output the flash floods susceptibility. Then, a multiple centrality model was used to evaluate the expected impact when a link was interrupted. The risk takes into account the flash floods susceptibility and road multiple centrality, and it identify critical links. Using the natural break points method, the result of flash floods susceptibility, road multiple centrality and risk were divided into five levels: extremely low, low, moderate, high and extremely high. Approximately 30.8% of the roads were found to be at high and extremely high risk, and most of these roads were located in Sichuan and Yunnan provinces. The flash floods susceptibility contributes more to the risk than the road multiple centrality, thus, further study should be focused on the areas with high flash floods susceptibility. We discussed different disaster prevention and mitigation measures for the different types of high risk links. The results can improve our understanding of the risks posed by flash floods to road networks. The proposed generalized network risk analysis method provides a new perspective and tool for analyzing the impact of flash floods on road network.

Synthetic aperture radar (SAR) ship classification is of great significance in the field of maritime observation. On one hand, how to comprehensively utilize the amplitude and phase information in SAR data has become a key problem for improving the performance of ship classification. On the other hand, there is a lack of available complex-valued SAR databases for the purpose of classification. To solve the above problems, a complex-valued SAR deep learning model, FDC-TA-DSN, based on four-dimensional dynamic convolution (FDC) and triple attention (TA) mechanism, is proposed. First, this new deep SAR-Net (DSN) devises an FDC module to reduce the influence of SAR speckle noise and enhance the adaptability of the network for inputting features, and a TA module to suppress background sea clutter and capture important features. Second, joint time-frequency analysis was used to obtain the radar spectrogram of SAR data, and the stacked convolutional autoencoder was used to learn the phase information of SAR data to obtain the backscattering characteristics. Finally, the two kinds of information are formed into fusion features for learning to improve the classification accuracy. To support this investigation, a complex-valued SAR dataset ComplexSAR_Ship is constructed for the first time by using the two high-resolution modes of UFS and FSI of the Gaofen-3 satellite. The dataset includes 17 ship types with nearly 3000 high-resolution ship slices. The experimental results show that, compared with the current popular networks, such as DSN, ResNet, VGG, etc., FDC-TA-DSN has achieved better performance, and the network has good generalization ability in SAR data classification.

Climate change, population increase, and urban expansion have increased the risk of flooding. Therefore, accurately identifying future changing patterns in the flood risk is essential. For this purpose, this study elaborated a new framework for a basin scale that employs a future land-use simulation model, a factor spatialization technique, and a novel hybrid model for scenario-based flood risk assessment in 2030 and 2050. Three land-use scenarios (i.e., natural growth scenario, cropland protection scenario, and ecological protection scenario) were set and applied in Jinjiang Basin to explore the changes in future flood risk under these scenarios. The results indicate the different degrees of increase in flood risk that will occur in the three scenarios. Under the natural growth (NG) scenario, the city will expand rapidly with the growth of population and economy, and the total area with high and very high flood risk will increase by 371.30 km² by 2050, as compared to 2020. However, under the ecological protection (EP) scenario, woodlands will be protected, and the growth in population, economy, and built-up lands will slow down with slightly increased risk of flooding. In this scenario, the total area with high and very high flood risk will increase by 113.75 km² by 2050. Under the cropland protection (CP) scenario, the loss of croplands will have been effectively stopped, and the flood risk will not show a significant increase under this scenario, with an increase by only 90.96 km² by 2050, similar to the EP scenario. Spatially, these increased flood risks mainly locate at the periphery of existing built-up lands, and the high-flood-risk zones are mainly distributed in the southeast of the Jinjiang Basin. The information about increasing flood risk determined by the framework provides insight into the spatio-temporal characteristics of future flood-prone areas, which facilitates reasonable flood mitigation measures to be developed at the most critical locations in the region.