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Level set method has been extensively used for image segmentation, which is a key technology of water extraction. However, one of the problems of the level-set method is how to find the appropriate initial surface parameters, which will affect the accuracy and speed of level set evolution. Recently, the semantic segmentation based on deep learning has opened the exciting research possibilities. In addition, the Convolutional Neural Network (CNN) has shown a strong feature representation capability. Therefore, in this paper, the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve, which only needs to describe the general outline of the water body, rather than the accurate edges. Compared with the traditional circular and rectangular zero-level set initialization method, this method can converge to the edge of the water body faster and more precisely; it will not fall into the local minimum value and be able to obtain accurate segmentation results. The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020. [ABSTRACT FROM AUTHOR]

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The Local Climate Zone (LCZ) scheme provides researchers with a standard method to monitor the Urban Heat Island (UHI) effect and conduct temperature studies. How to generate reliable LCZ maps has therefore become a research focus. In recent years, researchers have attempted to use Landsat imagery to delineate LCZs and generate maps worldwide based on the World Urban Database and Access Portal Tools (WUDAPT). However, the mapping results obtained by the WUDAPT method are not satisfactory. In this paper, to generate more accurate LCZ maps, we propose a novel Convolutional Neural Network (CNN) model (namely, LCZ-CNN), which is designed to cope with the issues of LCZ classification using Landsat imagery. Furthermore, in this study, we applied the LCZ-CNN model to generate LCZ mapping results for China's 32 major cities distributed in various climatic zones, achieving a significantly better accuracy than the traditional classification strategies and a satisfactory computational efficiency. The proposed LCZ-CNN model achieved satisfactory classification accuracies in all 32 cities, and the Overall Accuracies (OAs) of more than half of the cities were higher than 80%. We also designed a series of experiments to comprehensively analyze the proposed LCZ-CNN model, with regard to the transferability of the network and the effectiveness of multi-seasonal information. It was found that the first convolutional stage, corresponding to low-level features, shows better transferability than the second and third convolutional stages, which extract high-level and more image- or task-oriented features. It was also confirmed that the multi-seasonal information can improve the accuracy of LCZ classification. The thermal characteristics of the different LCZ classes were also analyzed based on the mapping results for China's 32 major cities, and the experimental results confirmed the close relationship between the LCZ classes and the magnitude of the Land Surface Temperature (LST). [ABSTRACT FROM AUTHOR]

At present, China's power grid has entered a new stage of ultra-high voltage, long-distance, large-capacity, large-unit, AC and DC hybrid interconnection grid, and the generator excitation system has an important impact on the stability of the power system. As the core component of the excitation system, the exciter plays a vital role in the safe operation of the excitation system. In view of the above problems, this paper proposes an improved CNN exciter fault diagnosis research method. To be able to learn more abundant fault features, this article first builds a dilated convolutions which is types of deep convolutional neural network (DCNN) to expand the receptive field of the convolution kernel. Then build the Pythagorean Spatial Pyramid Pooling Layer (PTSPP) to further enhance the feature information of the extracted samples. Finally, this article will generate two-dimensional matrix samples from the collected excitation voltage and excitation current signals for model training. The experimental results show that the proposed PTSPP-DCNN method has high classification accuracy in the fault diagnosis of the exciter system. The comparison results show that the fault classification accuracy of the proposed method is higher than other deep learning methods. [ABSTRACT FROM AUTHOR]

Accurate carbon price prediction is a reference that allows market participants to make decisions. This study adopts a total of 1,857 trading days of data from April 2, 2014, to June 15, 2022, in the Hubei carbon market, one of the first and largest pilot carbon markets in China for carbon price prediction. We propose a new framework based on the GA-VMD-CNN-BiLSTM-Attention hybrid model: a genetic algorithm (GA) is adopted to search the optimal parameter combination of variational mode decomposition (VMD); a convolutional neural network (CNN) is established to discover the relationship between influencing factors and carbon prices; a bidirectional long and short-term memory network (BiLSTM) is applied to extract time series information; and an attention mechanism is used to strengthen the influence of important information on carbon prices. Compared to 11 other models, the GA-VMD-CNN-BiLSTM-Attention model has a higher accuracy and stronger model reliability. In addition to deterministic point prediction, this study uses non-parametric kernel density estimation with the Gaussian kernel function (KDE-Gaussian) for interval forecasting. The forecasting can quantify the uncertainty of carbon prices and serve as a more practical reference for decision-makers. By revealing the particularly challenging issue that underlies carbon price forecasting, our analysis also sheds light on current low-carbon policies in China. The GA-VMD method achieves more accurate decomposition results. A hybrid prediction model is established based on CNN and BiLSTM. An attention mechanism is proposed for enhancing the impact of key information. Kernel density estimation with Gaussian function is adopted for interval forecasting. [ABSTRACT FROM AUTHOR]

As a newly developed classification system, the LCZ scheme provides a research framework for Urban Heat Island (UHI) studies and standardizes the worldwide urban temperature observations. With the growing popularity of deep learning, deep learning-based approaches have shown great potential in LCZ mapping. Three major cities in China are selected as the study areas. In this study, we design a deep convolutional neural network architecture, named Residual combined Squeeze-and-Excitation and Non-local Network (RSNNet), that consists of the Squeeze-and-Excitation (SE) block and non-local block to classify LCZ using freely available Sentinel-1 SAR and Sentinel-2 multispectral imagery. Overall Accuracy (OA) of 0.9202, 0.9524 and 0.9004 for three selected cities are obtained by applying RSNNet and training data of individual city, and OA of 0.9328 is obtained by training RSNNet with data from all three cities. RSNNet outperforms other popular Convolutional Neural Networks (CNNs) in terms of LCZ mapping accuracy. We further design a series of experiments to investigate the effect of different characteristics of Sentinel-1 SAR data on the performance of RSNNet in LCZ mapping. The results suggest that the combination of SAR and multispectral data can improve the accuracy of LCZ classification. The proposed RSNNet achieves an OA of 0.9425 when integrating the three decomposed components with Sentinel-2 multispectral images, 2.44% higher than using Sentinel-2 images alone. [ABSTRACT FROM AUTHOR]