The Visual Question Answering (VQA) system is the process of finding useful information from images related to the question to answer the question correctly. It can be widely used in the fields of visual assistance, automated security surveillance, and intelligent interaction between robots and humans. However, the accuracy of VQA has not been ideal, and the main difficulty in its research is that the image features cannot well represent the scene and object information, and the text information cannot be fully represented. This paper used multi-scale feature extraction and fusion methods in the image feature characterization and text information representation sections of the VQA system, respectively to improve its accuracy. Firstly, aiming at the image feature representation problem, multi-scale feature extraction and fusion method were adopted, and the image features output of different network layers were extracted by a pre-trained deep neural network, and the optimal scheme of feature fusion method was found through experiments. Secondly, for the representation of sentences, a multi-scale feature method was introduced to characterize and fuse the word-level, phrase-level, and sentence-level features of sentences. Finally, the VQA model was improved using the multi-scale feature extraction and fusion method. The results show that the addition of multi-scale feature extraction and fusion improves the accuracy of the VQA model.

Context: At present, many cities are facing severe water-resources problems caused by urbanisation. With the development of remote sensing and geostatistics, they have been widely used in urban water-resource monitoring. Aims: To review and summarise the application of remote sensing and geostatistics in monitoring urban water resources and prospect for their furtherdevelopment. Methods: First, bibliometrics was used to analyse the existing literature in this field. We then discuss the use of remote sensing and geostatistics to improve urban water-resources monitoring capacity, focusing on the classification of technologies and equipment and their applications in urban surface-water and urban groundwater monitoring. Finally, a look at the future research direction is taken. Conclusions: In the past decade, the relevant research has shown an upward trend. The use of remote sensing and geostatistics can improve the city’s water-resource monitoring capacity, thereby promoting better use of water resources in cities. Implications: In the future, with the development and addition of deep learning, remote-sensing and geographic-analysis systems can be used to conduct remote-sensing monitoring and data analysis on urban water resources more accurately, intelligently, and quickly, and improve the status of urban water resources.

Earthquake-triggered landslides frequently occur in active mountain areas, which poses great threats to the safety of human lives and public infrastructures. Fast and accurate mapping of coseismic landslides is important for earthquake disaster emergency rescue and landslide risk analysis. Machine learning methods provide automatic solutions for landslide detection, which are more efficient than manual landslide mapping. Deep learning technologies are attracting increasing interest in automatic landslide detection. CNN is one of the most widely used deep learning frameworks for landslide detection. However, in practice, the performance of the existing CNN-based landslide detection models is still far from practical application. Recently, Transformer has achieved better performance in many computer vision tasks, which provides a great opportunity for improving the accuracy of landslide detection. To fill this gap, we explore whether Transformer can outperform CNNs in the landslide detection task. Specifically, we build a new dataset for identifying coseismic landslides. The Transformer-based semantic segmentation model SegFormer is employed to identify coseismic landslides. SegFormer leverages Transformer to obtain a large receptive field, which is much larger than CNN. SegFormer introduces overlapped patch embedding to capture the interaction of adjacent image patches. SegFormer also introduces a simple MLP decoder and sequence reduction to improve its efficiency. The semantic segmentation results of SegFormer are further improved by leveraging image processing operations to distinguish different landslide instances and remove invalid holes. Extensive experiments have been conducted to compare Transformer-based model SegFormer with other popular CNN-based models, including HRNet, DeepLabV3, Attention-UNet, U${}^2$Net and FastSCNN. SegFormer improves the accuracy, mIoU, IoU and F1 score of landslide detectuin by 2.2%, 5% and 3%, respectively. SegFormer also reduces the pixel-wise classification error rate by 14%. Both quantitative evaluation and visualization results show that Transformer is capable of outperforming CNNs in landslide detection.

This paper surveys recent advances in pulse-coupled neural networks (PCNNs) and their applications in image processing. The PCNN is a neurology-inspired neural network model that aims to imitate the information analysis process of the biological cortex. In recent years, many PCNN-derived models have been developed. Research aims with respect to these models can be divided into three categories: (1) to reduce the number of manual parameters, (2) to achieve better real cortex imitation performance, and (3) to combine them with other methodologies. We provide a comprehensive and schematic review of these novel PCNN-derived models. Moreover, the PCNN has been widely used in the image processing field due to its outstanding information extraction ability. We review the recent applications of PCNN-derived models in image processing, providing a general framework for the state of the art and a better understanding of PCNNs with applications in image processing. In conclusion, PCNN models are developing rapidly, and it is projected that more applications of these novel emerging models will be seen in future.

In the era of big data, the Internet is enmeshed in people’s lives and brings conveniences to their production and lives. The analysis of user preferences and behavioral predictions of user data can provide references for optimizing information structure and improving service accuracy. According to the present research, user’s behavior on social networking sites has a great correlation with their personality, and the five characteristics of the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model can cover all aspects of a user’s personality. It is important in identifying a user’s OCEAN personality model to analyze their digital footprints left on social networking sites and to extract the rules of users’ behavior, and then to make predictions about user behavior. In this paper, the Latent Dirichlet Allocation (LDA) topic model is first used to extract the user’s text features. Second, the extracted features are used as sample input for a BP neural network. The results of the user’s OCEAN personality model obtained by a questionnaire are used as sample output for a BP neural network. Finally, the neural network is trained. A mapping model between the probability of the user’s text topic and their OCEAN personality model is established to predict the latter. The results show that the present approach improves the efficiency and accuracy of such a prediction.

Context: Numerous dams have been built in China to develop hydropower, a sustainable and clean energy source. In recent years, the impact of dam construction on the regional climate has gradually attracted the attention of researchers. Aims: This study has evaluated the impact of large-scale dam construction on regional precipitation. Methods: This paper used the precipitation data of more than 2400 national stations of the China National Meteorological Information Center from 1990 to 2012. The regional precipitation data before and after the construction of the Xiaolangdi Dam and the Three Gorges Dam on the Yangtze River were analysed using geostatistical tools. Wavelet transform and Yamamoto signal-to-noise ratio analysis were further adopted. Key results: Analysis of the variation points of precipitation characteristics confirmed the correlation between dam construction and regional precipitation; the precipitation values in the two dam study areas had an increasing trend after the completion of the dams, and the fitting trend line showed an obvious increasing trend. Conclusions: According to the analysis of precipitation variation points, it can be concluded that the establishment of the dam affected the precipitation in the area of ~200 km upstream and increased the precipitation value in this area. The study showed that dam construction has a strong correlation with regional precipitation. Implications: It is speculated that the construction of super dams will have a greater impact on precipitation.