Presents corrections to the paper, (Corrections to “Accelerating Cross-Scene Co-Seismic Landslide Detection Through Progressive Transfer Learning and Lightweight Deep Learning Strategies”).

The “black-box” nature of machine learning (ML) and deep learning (DL) models has raised concerns about the trustworthiness of landslide susceptibility mapping (LSM) results among users. Existing studies have applied many techniques to interpret LSM models, but they predominantly focused on individual model interpretations, lacked comparisons of interpretation results across different models, and failed to fully explore the potential of explainable artificial intelligence (AI) techniques in LSM. This study develops an innovative model interpretation framework based on the Shapley additive explanation (SHAP) method and different ML and DL models, to analyze the decision mechanisms differences and discuss the geospatial heterogeneity of landslide conditioning factors (LCFs). A geospatial database is constructed, including historical landslides, 16 common LCFs, and three earthquake-related LCFs for two study areas: Zigui and Jiuzhaigou. The data are then divided into training and testing sets in a 7:3 ratio for four models: random forest (RF), extreme gradient boosting decision tree (XGBoost), residual network, and densely connected convolutional networks (DenseNets). Finally, global and local interpretations are provided using the SHAP method. The analysis indicates that: 1) XGBoost consistently outperforms the other models in both study areas, achieving Kappa coefficient (Kappa), overall accuracy (OA), and area under the receiver operating characteristic curve (AUC) values of 0.9416, 0.9738, and 0.9757 for Zigui, and 0.8525, 0.9337, and 0.9312 for Jiuzhaigou and 2) the global interpretation shows that the same LCFs play different roles in the XGBoost and DenseNet, reflecting different decision mechanisms among LSM models. Moreover, local interpretations demonstrate that the same LCFs contribute differently in the two areas, highlighting the geospatial heterogeneity in LSM.

Wearable Internet of Things (IoT) devices are easily compromised by malware due to their security vulnerabilities. The bots infected by malware may continue to infect healthy neighbor devices through wireless communication technology in mobile wearable IoT networks (WIoT). All bots form a botnet which eventually leads to a series of malicious attacks. Therefore, it is necessary to predict the dynamic malware propagation path between wearable devices, which can help provide target immunization measures on devices to prevent the formation of botnets. In this article, we capture the local interaction and spatial–temporal propagation behavior of malware utilizing the individual-based cellular automata (CA) model. First, taking into account the mobility of walking users carrying wearable devices in the actual WIoT, we present a human mobility model called Gauss–Markov truncated Levy walk (GM-TLW) to describe the movement patterns of mobile users. Second, based on the moving coordinates of all wearable devices obtained from the GM-TLW mobility model, we leverage the improved CA propagation model to study the time evolution of the number of bots and the spreading spatial distribution of malware. We compare our propagation model with the differential equation model and traditional CA model, and analyze the impact of various parameters on the dynamics of botnet formation using numerical simulations. Finally, detailed simulation results show that the GM-TLW model is more suitable for realistic human mobility scenarios. In addition, the proposed CA-based model is more precise than the differential equation model to modeling the malware propagation and provides a basis for defenders to adopt the optimal malware control strategies.

Sudden co-seismic landslides strike, causing widespread devastation and demanding a rapid response. The swift and accurate acquisition of landslide information is essential for effective disaster relief. Deep learning (DL)-based computer-aided interpretation methods have emerged as cutting-edge tools for landslide detection. Nevertheless, traditional DL approaches face limitations, such as high annotation costs, slow processing speeds, and low generalizability, rendering them unsuitable for rapid co-seismic landslide recognition tasks. This study presents a progressive approach for co-seismic landslide detection. First, we develop a Multi-scale Feature Fusion Lightweight Neural Network (MFFLnet), achieving exceptional generalizability and speed while maintaining precision. Second, we employ the deep transfer learning (TL) strategy, enabling MFFLnet to leverage prior landslide knowledge from a source domain and a refined data augmentation algorithm to combat overfitting. The proposed methodology is implemented in two co-seismic landslide scenes in Hokkaido, Japan, and Luding, China. Experimental results demonstrate that the proposed method exhibits outstanding performance in regional landslide recognition and robust performance across different co-seismic landslide detection scenarios. Our approach proves competitive in efficient co-seismic landslide disaster recognition and cross-scene identification, showcasing significant applicability in the face of rapid response demands.

This study focuses on the Badui region in eastern Tibet, an area with complex topography featuring numerous valleys, ravines, and frequent geological hazards. Given the economic expansion in this region, advanced techniques are essential for analyzing the distribution of geological hazards and developing early warnings of geological hazards. The research employs enhanced small baseline subset interferometric synthetic aperture radar (ESBAS-InSAR) technology, which provides more ascending and descending data than traditional small baseline subset-InSAR (SBAS-InSAR), allowing reprojection into vertical and horizontal components. Following dimensionality reduction through principal component analysis (PCA) and k-means clustering, the horizontal displacements were categorized into four clusters, and the vertical displacements were categorized into five clusters. Time-series data of vertical and horizontal displacements, rainfall, and normalized difference vegetation index (NDVI) were then used to assess 16 displacement prediction models. The temporal fusion transformer (TFT) model demonstrated the best predictive performance. To further improve accuracy, 11 static variables such as clusters, elevation, slope, aspect, distance from faults, time-varying known categorical variable, and earthquake times, were added as the TFT input variables. Results indicate that the optimized TFT model reduces the root-mean-square error (RMSE) from 3.4842 to 2.1707, the mean absolute percentage error (MAPE) from 2625.6399 to 2154.5505, and the mean absolute error (MAE) from 2.4392 to 2.3731. Overall, this study provides a framework for multivariate, multistep forecasting of diverse deformation modes across large areas and identifies distinct landslide deformation patterns through clustering, thereby enhancing the prediction of landslide deformation.

Remote sensing change detection (RSCD) aims to explore surface changes from co-registered pair of images. However, the high cost of memory and computation in previous convolutional neural network (CNN)-based methods prevent their successes from being applied to real-world applications. Therefore, we propose a novel lightweight network, which identifies changes based on the features extracted by mobile networks via progressive feature aggregation and supervised attention, termed as A2Net. Considering the less powerful representation capability of mobile networks, we design a neighbor aggregation module (NAM) to fuse features within nearby stages of the backbone to strengthen the representation capability of temporal features. Then, we propose a progressive change identifying module (PCIM) to extract temporal difference information from bitemporal features. Besides, we design a supervised attention module (SAM) to reweight features for effectively aggregating multilevel features from high levels to low levels. With NAM, PCIM, and SAM incorporated, A2Net can achieve favorable results compared with the state-of-the-art methods on three challenging RSCD datasets with fewer parameters (3.78 M) and lower computation costs (6.02 G). The demo code of this work is publicly available athttps://github.com/guanyuezhen/A2Net.