An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images

Huge challenges exist for old landslide detection because their morphology features have been partially or strongly transformed over a long time and have little difference from their surrounding. Besides, small-sample problem also restrict in-depth learning. In this paper, an iterative classification and semantic segmentation network (ICSSN) is developed, which can greatly enhance both object-level and pixel-level classification performance by iteratively upgrading the feature extractor shared by two network. An object-level contrastive learning (OCL) strategy is employed in the object classification sub-network featuring a siamese network to realize the global features extraction, and a sub-object-level contrastive learning (SOCL) paradigm is designed in the semantic segmentation sub-network to efficiently extract salient features from boundaries of landslides. Moreover, an iterative training strategy is elaborated to fuse features in semantic space such that both object-level and pixel-level classification performance are improved. The proposed ICSSN is evaluated on the real landslide data set, and the experimental results show that ICSSN can greatly improve the classification and segmentation accuracy of old landslide detection. For the semantic segmentation task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448, the mIoU improves from 0.6405 to 0.6610, the landslide IoU improved from 0.3381 to 0.3743, and the object-level detection accuracy of old landslides is enhanced from 0.55 to 0.9. For the object classification task, the F1 score increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to 0.8875

A Hyper-pixel-wise Contrastive Learning Augmented Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

As a harzard disaster, landslide often brings tremendous losses to humanity, so it's necessary to achieve reliable detection of landslide. However, the problems of visual blur and small-sized dataset cause great challenges for old landslide detection task when using remote sensing data. To reliably extract semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from the boundaries of landslides through HPCL and fuses the heterogeneous infromation in the semantic space from High-Resolution Remote Sensing Images and Digital Elevation Model Data data. For full utilization of the precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method, which includes the construction of global queues that store hyper-pixel-wise samples and the updating scheme of a momentum encoder, is developed, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide dataset and experiment results show that the model greatly improves the reliablity of old landslide detection compared to the previous old landslide segmentation model, where mIoU metric is increased from 0.620 to 0.651, Landslide IoU metric is increased from 0.334 to 0.394 and F1-score metric is increased from 0.501 to 0.565

An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification

Few-shot hyperspectral classification is a challenging problem that involves obtaining effective spatial–spectral features in an unsupervised or semi-supervised manner. In recent years, as a result of the development of computer vision, deep learning techniques have demonstrated their superiority in tackling the problems of hyperspectral unmixing (HU) and classification. In this paper, we present a new semi-supervised pipeline for few-shot hyperspectral classification, where endmember abundance maps obtained by HU are treated as latent features for classification. A cube-based attention 3D convolutional autoencoder network (CACAE), is applied to extract spectral–spatial features. In addition, an attention approach is used to improve the accuracy of abundance estimation by extracting the diagnostic spectral features associated with the given endmember more effectively. The endmember abundance estimated by the proposed model outperforms other convolutional neural networks (CNNs) with respect to the root mean square error (RMSE) and abundance spectral angle distance (ASAD). Classification experiments are performed on real hyperspectral datasets and compared to several supervised and semi-supervised models. The experimental findings demonstrate that the proposed approach has promising potential for hyperspectral feature extraction and has better performance relative to CNN-based supervised classification under small-sample conditions

Evaluation on the anisotropic brittleness index of shale rock using geophysical logging

The brittleness index plays a significant role in the hydraulic fracturing design and wellbore stability analysis of shale reservoirs. Various brittleness indices have been proposed to characterize the brittleness of shale rocks, but almost all of them ignored the anisotropy of the brittleness index. Therefore, uniaxial compression testing integrated with geophysical logging was used to provide insights into the anisotropy of the brittleness index for Longmaxi shale, the presented method was utilized to assess brittleness index of Longmaxi shale formation for the interval of 3155–3175 m in CW-1 well. The results indicated that the brittleness index of Longmaxi shale showed a distinct anisotropy, and it achieved the minimum value at β = 45°-60°. As the bedding angle increased, the observed brittleness index (BI2_β) decreased firstly and increased then, it achieved the lowest value at β = 40°–60°, and it is consistent with the uniaxial compression testing results. Compared to the isotropic brittleness index (β = 0°), the deviation of the anisotropic brittleness index ranged from 10% to 66.7%, in other words, the anisotropy of brittleness index cannot be ignored for Longmaxi shale. Organic matter content is one of the main intrinsic causes of shale anisotropy, and the anisotropy degree of the brittleness index generally increases with the increase in organic matter content. The present work is valuable for the assessment of anisotropic brittleness for hydraulic fracturing design and wellbore stability analysis

Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data

The early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize section), China. The morphology and texture features of landslides were characterized with InSAR deformation monitoring data and high-resolution optical remote sensing data, combined with 17 landslide influencing factors. In the study area, 83 deformation accumulation areas of potential landslide hazards and 54 deformation accumulation areas of non-potential landslide hazards were identified through spatial overlay analysis with 64 potential active landslides, which have been confirmed by field verification. The Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were trained and tested through attribute selection and parameter optimization. Among the 17 landslide influencing factors, Drainage Density, NDVI, Slope and Weathering Degree play an indispensable role in the machine learning and recognition of landslide hazards in our study area, while other influencing factors play a certain role in different algorithms. A multi-index (Precision, Recall, F1) comparison shows that the SVM (0.867, 0.829, 0.816) has better recognition precision skill for small-scale unbalanced landslide deformation datasets, followed by RF (0.765, 0.756, 0.741), DT (0.755, 0.756, 0.748) and NB (0.659, 0.659, 0.659). Different from the previous study on landslide susceptibility and hazard mapping based on machine learning, this study focuses on how to find out the potential active landslide points more accurately, rather than evaluating the landslide susceptibility of specific areas to tell us which areas are more sensitive to landslides. This study verified the feasibility of early identification of landslide hazards by using different machine learning methods combined with deformation information and multi-source landslide influencing factors rather than by relying on human–computer interaction. This study shows that the efficiency of potential hazard identification can be increased while reducing the subjective bias caused by relying only on human experts

Sandwiched Epitaxy Growth of Single-Crystalline Hexagonal Bismuthene Nanoflakes for Highly Selective Electrocatalytic CO2-to-formate Conversion

Two-dimensional (2D) bismuthene material was predicted to possess intriguing physical and electrical properties, such as high-temperature quantum-spin Hall effect, topological edge state, high carrier mobility, and tunable band gap. However, epitaxial growth of single-crystalline 2D bismuthene nanoflakes inevitably requires a high vacuum environment, primarily due to the high surface energy constraints of Bi. Herein, we report the growth of 2D single-crystalline hexagonal bismuthene nanoflakes on Cu foil substrate at atmospheric pressure by chemical vapor deposition. Based on first-principles calculations, the structural transformation of Bi on Cu foil can be suppressed by introducing the top h-BN layer, which potentially compensates for the charge transfer from Bi to the Cu (1 1 1) surface. The sandwich structure is identified by cross-sectional SEM and EDS characterization, demonstrating that bismuthene nanoflakes are sandwiched between the h-BN film and Cu foil. Benefiting from the encapsulation of the top h-BN layer, bismuthene nanoflakes also exhibit excellent thermal stability in ambient air even after annealing at 500 °C for 10 min. For further practical application, bismuthene nanoflakes are utilized for electrochemical CO2 reduction reactions (CO2RR). These bismuthene nanoflakes demonstrate remarkable ability in converting CO2 to formic acid with a Faradaic efficiency of 96.3% at ‒1.0 V (vs. RHE) and exhibit great catalytic stability with a Faradaic efficiency of over 90% in 15 h CO2RR tests. The ultrathin 2D feature of as-prepared bismuthene nanoflakes may result in abundant CO2 adsorption sites and stabilize the intermediate *OCHO, finally favoring the formation of HCOOH. We provide an effective strategy to simultaneously synthesize and passivate 2D single-crystalline bismuthene nanoflakes towards CO2RR, which is expected to be applied to other 2D materials with strong metallic growth behavior

A Novel Method of Damage Control for Multiple Discontinuous Intestinal Injuries with Hemorrhagic Shock: A Controlled Experiment

Aim: In this study, we examined the effects of branched silicon tube (BST) and temporary closed circle (TCC) in a Beagle dog model of multiple transection of small intestine and discontinuities suspected intestinal necrosis with hemorrhagic shock. Materials and Methods: Ten male Beagle dogs were randomly divided into two groups. Hemorrhagic shock was induced by bleeding. Intestine was severed. Suspected intestinal necrotic model by ligating the mesenteric vessels was established, with a small tertiary mesenteric vessel reserved. Fracted intestines were ligated (IL group, n = 5) or reconnected with BST (IR group, n = 5). The abdominal cavity was temporarily closed with TCC. Definitive surgery was conducted after 24 h. Results: There was no statistical difference between two groups in the weight of dogs, their blood loss, fluid resuscitation, operation time of early emergency treatment (EET). After definitive surgery, all dogs in IR group and 3 dogs in IL groups were alive. 18 (90%) suspicious necrotic intestinal segments in IL group became necrotic, but 20 (80%) segments in IR group didn't develop obvious changes (p < 0.01). From 2 h after EET, the endotoxin concentration in IL group was significantly higher than that in IR group (133.87 ± 43.73 vs. 56.31 ± 24.70 pg/ml, p < 0.01). Microscopic examination revealed that much more severe damage occurred in the suspicious necrotic intestinal segments in IL group. Conclusion: Both reconnecting intestine with BST and temporary abdominal closure with TCC are viable methods of damage control for multiple discontinuous intestinal injuries

2D fin field-effect transistors integrated with epitaxial high-k gate oxide

Precise integration of two-dimensional (2D) semiconductors and high-dielectric-constant (k) gate oxides into three-dimensional (3D) vertical-architecture arrays holds promise for developing ultrascaled transistors(1-5), but has proved challenging. Here we report the epitaxial synthesis of vertically aligned arrays of 2D fin-oxide heterostructures, a new class of 3D architecture in which high-mobility 2D semiconductor fin Bi2O2Se and single-crystal high-k gate oxide Bi2SeO5 are epitaxially integrated. These 2D fin-oxide epitaxial heterostructures have atomically flat interfaces and ultrathin fin thickness down to one unit cell (1.2 nm), achieving wafer-scale, site-specific and high-density growth of mono-oriented arrays. The as-fabricated 2D fin field-effect transistors (FinFETs) based on Bi2O2Se/Bi2SeO5 epitaxial heterostructures exhibit high electron mobility (mu) up to 270 cm2 V-1 s(-1), ultralow off-state current (I-OFF) down to about 1 pA mu m(-1), high on/off current ratios (I-ON/I-OFF) up to 10(8) and high on-state current (I-ON) up to 830 mu A mu m(-1) at 400-nm channel length, which meet the low-power specifications projected by the International Roadmap for Devices and Systems (IRDS)(6). The 2D fin-oxide epitaxial heterostructures open up new avenues for the further extension of Moore&apos;s law