Landslides constitute a significant hazard to human life, safety and natural resources. Traditional landslide investigation methods demand considerable human effort and expertise. To address this issue, this study introduces an innovative landslide segmentation framework, EMR-HRNet, aimed at enhancing accuracy. Initially, a novel data augmentation technique, CenterRep, is proposed, not only augmenting the training dataset but also enabling the model to more effectively capture the intricate features of landslides. Furthermore, this paper integrates a RefConv and Multi-Dconv Head Transposed Attention (RMA) feature pyramid structure into the HRNet model, augmenting the model’s capacity for semantic recognition and expression at various levels. Last, the incorporation of the Dilated Efficient Multi-Scale Attention (DEMA) block substantially widens the model’s receptive field, bolstering its capability to discern local features. Rigorous evaluations on the Bijie dataset and the Sichuan and surrounding area dataset demonstrate that EMR-HRNet outperforms other advanced semantic segmentation models, achieving mIoU scores of 81.70% and 71.68%, respectively. Additionally, ablation studies conducted across the comprehensive dataset further corroborate the enhancements’ efficacy. The results indicate that EMR-HRNet excels in processing satellite and UAV remote sensing imagery, showcasing its significant potential in multi-source optical remote sensing for landslide segmentation.

Deformation can effectively reflect the structural state of concrete dams and, thus, establishing na accurate concrete dam deformation prediction model is important for dam health monitoring and early warning strategies. To address the problem that the spatial coordinates introduced in the traditional multi-point deformation prediction model of dams not being able to accurately and efficiently reflect the spatial correlation of multiple-measuring points, a 2D-1D-CNN model is proposed which expresses the spatial correlation between each measuring point through spatial feature vectors, replacing the spatial coordinates in the traditional multi-point model. First, the spatial feature vector is extracted from the historical spatio-temporal panel series of deformation values of measuring points via a Two-Dimensional Convolutional Neural Network (2D-CNN); second, the vector is combined with the environmental impact factor of dam deformation to form the final input factor of fused spatial features; and, thirdly, this vector is combined with the environmental impact factors of dam deformation to form the final input factor of fused spatial features, and the non-linear linkage between the factors and the measured displacement values is constructed by the efficient feature processing capability of a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the prediction results. Finally, the actual monitoring data of a concrete dam in China are used as an example to verify the validity of the model. The results show that the proposed model outperforms the other models in most cases, respectively, which verifies the effectiveness of the proposed model in this paper.

The dragon fruit industry holds significant market potential and is crucial for rural economic development. However, a comprehensive understanding and precise technical approach for analyzing the spatiotemporal dynamics of dragon fruit agriculture remain lacking. This study utilizes Nighttime Light (NTL) remote sensing data and proposes the Vegetation and Impervious area Adjusted Nighttime light Dragon fruit Index (VIANDI) to extract artificial light sources associated with dragon fruit cultivation. Furthermore, a regression model is constructed to estimate production based on light intensity. By integrating geospatial analysis methods, this study reveals the spatiotemporal evolution of dragon fruit cultivation area and production in Guangxi, China, from 2017 to 2022. The results demonstrate that the proposed method effectively monitors the dynamics of dragon fruit agriculture, achieving a Kappa Coefficient of 0.72 for area extraction and a Mean Relative Error (MRE) of 8.90% for production estimation. The spatial pattern of dragon fruit production follows a northwest–southeast distribution, with its centroid located in Nanning. The spatial expansion of cultivation areas exhibited an initial growth phase followed by stabilization, whereas production distribution transitioned from expansion to aggregation, maintaining an overall upward trend. Notably, 2019 marks a key turning point in these trends. Additionally, the rapid increase in light pollution intensity within cultivation areas warrants further attention. The study results have advanced precise monitoring of dragon fruit agriculture and enhanced understanding of its spatiotemporal evolution patterns.

This paper firstly inquired the categories of knowledge on dam safety monitoring expert system and the knowledge acquisition process. Secondly, it not only analyzed knowledge representation about dam safety monitoring expert system, that is, factual knowledge, rule knowledge and procedure knowledge, but also established a simple knowledge base based on relational database for dam safety monitoring expert system including fact base, rule base and knowledge inference strategies. Finally, knowledge base can be applied to dam safety monitoring expert system for providing the dam safety monitoring working with a guarantee.