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Rock classification provides vital information to geosciences and geological engineering practices. Reaping the benefits of the advances of computer vision-based deep learning artificial intelligence (AI) technology, this study aims to develop a next-generation convolutional neural network (CNN) to perform automatic rock classification. Two major challenging issues have been particularly addressed. First, most of the previous rock classifications are simply transfer learning of CNNs that are trained by life-like scenarios. Second, classifying rock types with similar textures leads to severe overfitting of CNNs. In this study, a novel CNN called HKUDES_Net is proposed and implemented to classify seven common Hong Kong rock types, namely fine-grained granite, medium-grained granite, coarse-grained granite, coarse ash tuff, fine ash tuff, feldsparphyric rhyolite, and granodiorite. With the aid of dynamic expansion, squeeze and excitation, and other strategies, HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. As compared with the other ten landmark CNNs and seven feature-based algorithms, HKUDES_Net has the best performance in precision (90.9%), recall (90.4%), and f1-score (90.5%). By implementing the alerting level, which restricts the training loss hovering above a small constant and prevents the validation loss from rising, the overfitting has been efficiently eliminated. The proposal and implementation of HKUDES_Net highlight the value of interdisciplinary research and will continuously pave the way for better coupling AI and geosciences. Highlights: A next-generation convolutional neural network (CNN) called HKUDES_Net is developed to perform automatic rock classification. HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. By implementing the alerting level in HKUDES_Net, the overfitting has been efficiently eliminated. Plain Language Summary: Rock classification provides vital information to geosciences and geological engineering practices. However, the traditional way of rock classification is to a certain degree experience-based. Computer vision-based artificial intelligence (AI), especially convolutional neural network (CNN), a sub-branch of AI, may help people automatically classify rock types with the hope to train AI models (CNNs), which can instantly and precisely perceive rock images similar to how human beings observe rocks. In other words, by putting a rock image into a CNN, the CNN will read the information from the image and tell people what rock type in the image is. This study develops and implements a next-generation CNN called HKUDES_Net to classify seven different rock types. Adopting different computational strategies, HKUDES_Net can classify rock types of different grain sizes with similar texture patterns/colors, which is challenging for other CNNs. As compared with the other landmark CNNs and machine learning algorithms, HKUDES_Net has much a better prediction accuracy performance. The proposal and implementation of HKUDES_Net exemplify an interdisciplinary research study, and we are confident that this paper will be instrumental to pave the way for further coupling AI and geosciences. [ABSTRACT FROM AUTHOR]

The dissemination of fake news on social media platforms is an issue of considerable interest, as it can be used to misinform people or lead them astray, which is particularly concerning when it comes to political events. The recent event of Hong Kong protests triggered an outburst of fake news posts that were identified on Twitter, which were then promptly removed and compiled into datasets to promote research. These datasets focusing on linguistic content were used in previous work to classify between tweets spreading fake and real news using traditional machine learning algorithms (Zervopoulos et al., in: IFIP international conference on artificial intelligence applications and innovations, Springer, Berlin, 2020). In this paper, the experimentation process on the previously constructed dataset is extended using deep learning algorithms along with a diverse set of input features, ranging from raw text to handcrafted features. Experiments showed that the deep learning algorithms outperformed the traditional approaches, reaching scores as high as 99.3% F1 Score, with the multilingual state-of-the-art model XLM-RoBERTa outperforming other algorithms using raw untranslated text. The combination of both traditional and deep learning algorithms allows for increased performance through the latter, while also gaining insight regarding tweet structure from the interpretability of the former. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is essential for regional landslide risk assessment and mitigation. Most past studies involved cell-based analysis that takes landslide incidents as geo-spatial points. Nevertheless, given that a landslide is a two-dimensional polygon on maps and a three-dimensional object in the real world, an object-wise assessment is more logical. Fusing with artificial intelligence (AI) techniques, this paper proposes a novel AI-powered object-based landslide susceptibility assessment method to address this issue. First, landslide and non-landslide objects are defined based on an optimal object size determined by statistics of historical landslides. Next, landslide and non-landslide samples are constructed by integrating geoenvironmental data layers derived from multi-source data. Subsequently, AI techniques are applied to learn susceptibility prediction based on the prepared samples. To illustrate the proposed method, a comprehensive case study of Hong Kong is conducted, in which six AI algorithms are evaluated including logistic regression (area under curve, AUC = 0.949), random forest (AUC = 0.951), LogitBoost (AUC = 0.958), convolutional neural network (CNN) (AUC = 0.966), bidirectional long short-term memory architecture of recurrent neural network (BiLSTM-RNN) (AUC = 0.966), and CNN-LSTM (AUC = 0.972), among which the BiLSTM-RNN and CNN-LSTM algorithms are applied in landslide susceptibility assessment for the first time. Results confirm that the proposed object-based method outperforms the traditional cell-based method significantly. Equally importantly, the case study produced the first set of AI-based territory-wide landslide susceptibility maps for Hong Kong. These maps can be used as a fundamental tool for quantifying natural terrain landslide risk and identifying susceptible zones where landslide mitigation measures may be needed. • A novel artificial intelligence and object-based landslide susceptibility method is proposed and validated. • The first set of territory-wide landslide susceptibility maps for Hong Kong is produced based on AI techniques. • CNN-LSTM and BiLSTM-RNN are successfully applied to landslide susceptibility analysis for the first time. • The proposed AI-powered object-based method outperforms the cell-based method significantly. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM. [ABSTRACT FROM AUTHOR]