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The Loess Plateau is an ecologically fragile area in China; furthermore, loess landslides are typical forms of geological disasters, which severely limit the sustainable development of the local societies and the economy. Studying the automatic detection of landslides can facilitate disaster prevention and mitigation in the Loess Plateau, and help realize the climate action goal (SDG 13) of the United Nations Sustainable Development Goals (SDGs). This paper takes typical loess areas in China as the research object, and establishes a historical loess landslide sample database based on Google Earth (GEE) image data, with a total of 1451 loess landslides. The automatic detection of loess landslides is implemented by improving the You Only Look Once X (YOLOX) algorithm. The results show that the average accuracy of landslide detection in this method is 95.43%, and the accuracy rate is 96.32%, which effectively combines the earth's big data to realize the automatic detection of loess landslides. The research results provide technical support for the promotion of disaster prevention and mitigation in China's loess regions, the realization of sustainable development goals, and the improvement of natural disaster prevention–resistance–reduction systems. [ABSTRACT FROM AUTHOR]

Removing motion blur has been an important issue in computer vision literature. Motion blur is caused by the relative motion between the camera and the photographed object. However, in recent years, some achievements have been made in the research of image deblurring by using deep learning algorithms. In this paper, an enhanced adversarial network model is proposed. The proposed model can use the weight of feature channel to generate sharp image and eliminate draughtboard artefacts. In addition, the mixed loss function enables the network to output high-quality image. The proposed approach is tested using GOPRO datasets and Lai datasets. In the GOPRO datasets, the peak signal-to-noise ratio of the proposed approach is up to 28.674, and DeblurGAN is 27.454. And the structural similarity measure can be achieved up to 0.969, and DeblurGAN is 0.939. Furthermore, the images were obtained from China's Chang'e 3 Lander to test the new algorithm. Due to the elimination of the chessboard effect, the deblurred image has a better visual appearance. The proposed method achieved higher performance and efficiency in qualitative and quantitative aspects using the benchmark dataset experiments. The results also provided various insights into the design and development of the camera pointing system, which was mounted on the Lander for capturing images of the moon and rover for Chang'e space mission. [ABSTRACT FROM AUTHOR]

The catenary support components (CSCs) are the most important devices in high-speed railways to support contact lines for powering trains. To estimate the states of CSCs, it is very necessary to locate their positions in a monitoring system based on computer vision. Considering the application scenarios and characteristics of CSCs, an automatic and quick positioning system is designed in this paper to simultaneously position the multiscale CSCs with 12 categories. In the system, an effective framework called CSCs network (CSCNET) is presented, which cascades the coarse positioning network and the fine positioning network to reduce multiscale differences between different CSCs. In the coarse positioning network, a new unsupervised clustering algorithm based on the relative positioning information is proposed to classify the catenary images. Then, a convolutional neural network (CNN) classification network is trained to extract the structural features of catenary images and generate the proposal regions with labels. In the fine positioning network, a modified CNN positioning framework is applied to obtain the accurate positions of CSCs based on the coarse positioning results. Due to the special lightweight structure with a classification network, the relative position information is applied and makes the CSCNET sensitive to small-scale components. The experimental results from some high-speed railway lines in China show that the proposed system has obvious advantages in the CSCs positioning. The mean average precision and frames per second of CSCNET reach 0.837 and 2.17, respectively. Compared with some popular convolutional networks [faster region-based CNN (Faster R-CNN), etc.] and a typical positioning method, the proposed system significantly improves the AP without increasing the computational time. [ABSTRACT FROM AUTHOR]

• A linear components extraction method is proposed to identify the daily fluctuating patterns of the solar power series. • A scenarios generation algorithm is proposed to provide forecasting linear trend data. • Temporal distributed deep learning models are developed based on the detected daily fluctuating patterns of the PV power generation. • The proposed algorithm can provide accurate day-ahead solar power forecasts for distributed PV farms without local weather information. In recent years, the integration of distributed photovoltaic has witnessed explosive growth because of its great economic and environmental benefits. However, the intermittence and randomicity of PV power generation are challenging the stable and economic operation of active distribution system, especially when the local historical and forecasting meteorological data are not available. Thus, this paper presents a novel Gated Recurrent Unit based hybrid model for forecasting distributed PV power generations. The proposed method uses locally historical PV power generation data to forecast one-day-ahead solar power time series with five-minute intervals. First, a linear components extraction method is proposed to identify the daily fluctuating patterns of the solar power series. Then, temporal distributed Gated Recurrent Unit models are developed based on the detected daily fluctuating patterns of the PV power generation. A scenarios generation algorithm is proposed to provide forecasting linear trend data for each Gated Recurrent Unit model. Finally, the daily linear series scenarios, the linear and nonlinear parts of the historical data are fed into the Gated Recurrent Unit to forecast the PV generation. Data collected from an actual distributed PV farm in southeastern China are utilized to validate the effectiveness of the proposed model. The results show that the proposed model can provide a nominalized root mean square error of 6.83% and a nominalized mean absolute error of 4.12%. Compared to a single Gated Recurrent Unit forecasting model, the developed model has improved the forecasting accuracy by 62.7%. [ABSTRACT FROM AUTHOR]

• A satellite image-based photovoltaic power forecasting model is proposed. • A nonlinear cloud movement forecasting method based on deep learning is proposed. • An algorithm for intra-hour cloud condition estimation is established. • An algorithm for recognizing the cloud region that blocks sunlight is designed. The rapid variation of clouds is the main factor that causes the fluctuation of photovoltaic power. 1 1 This work was supported by the National Key Research and Development Program of China under Grant 2019YFE0118400. The satellite images contain plenty of information about clouds, applicable for photovoltaic power forecast. However, in practice, two main factors obstruct the application of the satellite images: 1) the relatively low update frequency of the satellite images mismatches the photovoltaic power forecasting frequency, and 2) the cloud region that blocks the sunlight changes significantly with time. In this paper, a novel satellite image-based approach for photovoltaic power forecast is proposed to overcome these obstacles and achieve accurate forecasting results. Firstly, concerning the hourly updated satellite images, a nonlinear cloud movement forecasting model, considering the thickness and shape changes of the cloud, is presented to forecast the hourly variation of the images. Secondly, an active cloud region selection rule is derived based on the changing solar position to dynamically select the cloud region that blocks the concerned photovoltaic power station in a satellite image. Thirdly, a sequential cloud region selection algorithm is provided to estimate the intra-hour variation of the cloud to match the photovoltaic power forecasting frequency. Finally, the photovoltaic power is predicted using the XGBoost algorithm concerning the effects of the cloud and other influencing factors. Testing results show that the proposed method can achieve more accurate photovoltaic power forecasts using the low update frequency satellite images. Meanwhile, the superior performance compared with other benchmarks also verifies the effectiveness of considering cloud information obtained by the proposed method for photovoltaic power forecast. [ABSTRACT FROM AUTHOR]

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data's intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly. [ABSTRACT FROM AUTHOR]