Search Results (2,039)

Landslides are characterized by sudden occurrence, severe destructiveness, and widespread spatial distribution. Therefore, rapid and accurate landslide detection is essential for reducing infrastructure damage and safeguarding human lives. Conventional landslide monitoring methods are generally time-consuming, labor-intensive, and inefficient, while existing deep learning-based landslide detection methods remain limited in multiscale spatial structure representation, temporal sequence modeling, and spatiotemporal feature fusion. To address these limitations, this study proposes ConFormer-Net, a spatiotemporal landslide detection model that integrates convolutional neural networks (CNNs) with a Transformer architecture for landslide detection from multi-temporal synthetic aperture radar (SAR) imagery. The proposed model adopts a hybrid architecture and incorporates a cross-attention mechanism. Specifically, a dilated convolutional network is employed to extract local spatial features of landslides, while a Transformer encoding module models the temporal dependencies among multi-temporal SAR observations. The extracted spatial and temporal features are subsequently integrated through the cross-attention mechanism to achieve accurate landslide detection. The overall detection performance of ConFormer-Net was first evaluated using samples from different regions in the publicly available Sen12Landslides dataset. The proposed model was then compared with CNN, CNN-LSTM, ConvLSTM, GRU, CNN3D and ResNet50 models. The results demonstrate that ConFormer-Net achieved the best overall performance, with an F1-score of 95.27%, an accuracy of 95.29%, a precision of 96.79%, and a recall of 93.79%. These results indicate that ConFormer-Net enables highly accurate landslide detection while maintaining moderate model complexity, demonstrating its effectiveness for landslide detection from multi-temporal SAR imagery. Full article

This study proposes a lightweight domain-assisted deep learning framework for binary human presence detection using ultra-wideband (UWB) radar. The proposed methodology processes raw UWB radar signals through statistically screened, physics-grounded signal features including Fast Fourier Transform (FFT)-based frequency-domain statistics and Hilbert Transform (HT)-derived envelope statistics which are selected via a per-subject Cohen’s d screening step and stacked as auxiliary input channels alongside the raw signal for a lightweight two-dimensional convolutional neural network (2D-CNN). A cross-subject evaluation protocol (train-on-one-subject, test-on-the-other) is adopted to assess generalization across individuals rather than relying on a pooled, sample-level split. Among the candidate features, a Frequency Standard Deviation (FSTD) is shown to match or exceed the performance of every multi-feature combination tested, indicating that targeted feature selection is more consequential than input fusion for this task. To further improve deployment efficiency, post-training INT8 quantization is applied, reducing the model to approximately 23 KB while preserving classification performance for quantization-robust configurations. Hardware-in-the-loop benchmarking on the STEdgeAI platform indicates on-device inference times ranging from approximately 0.88 ms on AI-enabled STM32N6 hardware to 117–130 ms on STM32H7-class microcontrollers; these figures reflect model inference only and exclude radar acquisition and preprocessing time. Experiments are conducted on a two-subject (one male, one female) indoor dataset; the reported cross-subject results are presented as a relative comparison across feature and quantization configurations rather than as an estimate of population-level generalization. The findings nonetheless illustrate the feasibility of combining principled feature selection with quantization-aware, hardware-validated deployment on embedded artificial intelligence (AI) platforms. Full article

Image super-resolution (SR), which aims to reconstruct a high-resolution image from a low-resolution input, has progressed from convolutional neural networks (CNNs) to transformer-based architectures. Despite this progress, lightweight transformer SR remains challenging: local or window-based operations provide limited long-range interaction, conventional query-key-value projections introduce parameter and computational redundancy, and pixel-domain loss alone provides insufficient frequency-domain constraints on fine structures. This study presents RAW, a lightweight SR network based on residual aggregation and wavelet loss. RAW uses local aggregation to preserve neighborhood textures, mesoscale grouped-residual attention to reduce projection redundancy while modeling regional dependencies, and non-local sparse aggregation to capture long-range information at a controlled cost. By integrating stationary-wavelet-transform loss with RGB-domain L1 loss, the model supervises structural and high-frequency information without adding an inference branch. Experiments on standard benchmarks demonstrate a competitive trade-off between reconstruction quality and computational complexity. For 4× SR, RAW reduces the numbers of parameters and MACs by 14.3% and 15.4%, respectively, relative to the baseline, while improving the PSNR and SSIM on Manga109 by 0.22 dB and 0.0015, respectively. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which accurate and accessible screening approaches remain an important research objective. This study proposes a hybrid meta-classifier framework for handwriting-based AD classification using the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset, which comprises 174 participants, including 89 individuals with AD and 85 healthy controls, and contains 450 handwriting-related features derived from 25 tasks. The proposed framework combines a one-dimensional convolutional neural network (1D-CNN) for automated feature learning with a heterogeneous ensemble of XGBoost, support vector machine (SVM), and random forest classifiers. The predictions of the base classifiers are subsequently integrated by an AdaBoost-based meta-classifier to generate the final classification decision. The framework achieved a pooled cross-validation accuracy of 98.28%, with a mean fold-level accuracy of 97.73 ± 1.27%, a precision of 96.90 ± 2.84%, a recall of 98.89 ± 2.49%, an F1-score of 97.84 ± 1.21%, and an AUC of 0.944 ± 0.023 across the five outer folds. In addition, evaluation on an independent handwriting-signature cohort achieved an accuracy of 91.76%, with a sensitivity of 90.74% and a specificity of 93.55%. These results indicate that the proposed framework has strong discriminative capability across the evaluated datasets. Nevertheless, further validation on larger and more diverse independent cohorts is required before conclusions regarding clinical applicability can be drawn. Future work will investigate multimodal integration and the application of explainable artificial intelligence techniques to improve the interpretability of the proposed framework. Full article

Apple watercore is an internal physiological disorder that affects fruit quality and storage stability. This study developed a non-destructive approach for classifying watercore severity in 737 Aksu ‘Fuji’ apples using near-infrared hyperspectral imaging (930–1720 nm). Watercore severity was quantified using the watercore severity index (WSI) derived from Fiji-based segmentation of equatorial cross-sectional images, and samples were classified into sound, slight, moderate, and severe classes according to published criteria. After spectral preprocessing, support vector machine (SVM), random forest (RF), baseline one-dimensional convolutional neural network (1D-CNN), and attention-enhanced 1D-CNN-CBAM-SE models were evaluated using five repeated stratified holdout experiments. SVM and 1D-CNN-CBAM-SE achieved comparable performance, with SVM obtaining an accuracy of 98.74% and a Macro-F1 score of 98.86%, and 1D-CNN-CBAM-SE achieving the same accuracy and a Macro-F1 score of 98.85%. Ablation experiments showed that attention modules improved the baseline 1D-CNN, with CBAM providing the major performance gain and the addition of SE further reducing performance variability across repeated experiments. Misclassifications were largely confined to adjacent severity classes. These results support the feasibility of NIR-HSI combined with spectral classification models for non-destructive watercore severity grading. Full article

Soil organic carbon (SOC) in permafrost regions is a critical component of terrestrial carbon reservoirs, and spatially explicit multi-depth mapping is essential for identifying subsurface carbon emission hotspots and supporting carbon neutrality accounting. The Kamalan River Basin in the Greater Khingan Mountains, situated in a discontinuous permafrost zone with relatively high SOC stocks and strong sensitivity to climate warming, was selected as the study area. A total of 90 field sampling points were collected and integrated with Sentinel-1 Synthetic Aperture Radar (SAR) imagery, Sentinel-2 multispectral imagery, and topographic data, yielding 238 environmental features. Three predictive models, Random Forest (RF), Geographically Weighted Random Forest (GWRF), and One-Dimensional Convolutional Neural Network (1D-CNN), were combined with three feature selection methods (Boruta, RF importance, and Shapley) to construct 27 model–feature–depth combinations across three soil layers (0–10 cm, 10–20 cm, and 20–30 cm). RF feature selection combined with RF model achieved the highest prediction accuracy at 0–10 cm (R² = 0.93, RMSE = 16.58 g/kg), with its bootstrap aggregation strategy effectively suppressing overfitting under small-sample and high-dimensional conditions. 1D-CNN showed relatively unstable performance, which may be associated with feature selection strategy, feature ordering sensitivity, a potential mismatch between its inductive bias and tabular environmental data. Feature importance analysis suggested that bulk density (BD) and soil moisture content (SMC) were among the most influential predictors across all depth layers, with elevation, radar backscatter coefficients, and Sentinel-1 texture features demonstrating relatively high cross-method and cross-depth stability. Boruta excluded BD and SMC at 0–10 cm, which may be partly attributable to inter-predictor redundancy, a strict independent-contribution threshold, and insufficient statistical power under small-sample conditions. For spatial mapping, the best-performing combination for each model was applied: RF feature selection with RF model, RF feature selection with GWRF model, and RF feature selection with 1D-CNN model. SOC maps revealed considerable spatial heterogeneity, with relatively high values concentrated in valley lowlands and lower values in high-altitude forest zones; mean SOC declined from 93.08 g/kg at 0–10 cm to 69.88 g/kg at 20–30 cm. Despite the limited number of sampling points (n = 90), these findings provide practical guidance for model and feature selection in SOC mapping of permafrost forests and a scientific basis for carbon sink assessment under climate warming scenarios. Conservation efforts should prioritize riparian zones and high-elevation areas with elevated SOC concentrations, while vegetation protection and reduced soil disturbance are recommended in low-SOC regions to help limit further carbon loss. Full article

Infrared hyperspectral remote sensing is effective for chemical gas detection because gas molecules exhibit characteristic absorption features in the mid- and long-wave infrared region. This study considers coexisting gas components along a common line of sight rather than geometrically distinct spatial plumes. We propose a compact spectral one-dimensional convolutional neural network (1D-CNN) for pixelwise gas species identification and the extraction of binary detection regions directly from 121-band spectra. Across five independently simulated NETD conditions and cross-noise train–test evaluations, the model maintained consistently strong classification performance over the examined noise range. On the measured scenes, the proposed model was compared with four traditional detectors and an adapted spectral Transformer baseline, showing a favorable and comparatively consistent Precision–Recall balance across the four target gases. These results support a fixed-platform field proof of concept, but do not constitute airborne-platform, edge deployment, quantitative concentration retrieval, or broad cross-site validation. Full article

Bio-based alternatives to conventional rigid foams have proven to be good substitutes owing to their enhanced sustainability and competitive performance. However, because their manufacturing processes are complex and destructive testing is often impractical, this study investigates whether microstructural features can be correlated with mechanical properties in lignin-containing rigid polyurethane (PU) foams using machine learning approaches. Various types and percentages of lignin-based polyols were investigated as partial replacements for polyol, including LHO, DCA, DCA-D, LHO-O, Kraft lignin (KL), and LHO-MD, at polyol replacement levels ranging from 12.5% to 50%, together with a control formulation. Scanning electron microscopy (SEM) images and corresponding mechanical compression data were used to train a custom state-of-the-art dual-head convolutional neural network (CNN) targeting the specific prediction of density, specific compression modulus, specific yield stress, and specific compression strength. The CNN was optimized with a weighted multi-output loss function, achieving strong predictive performance with R² values ranging from 0.850 to 0.91 and correlation coefficients above 0.92, while maintaining mean absolute error percentages below ≈9%. This proves the trained network’s capability to predict and capture morphological features governing load-bearing responses. On the other hand, Grad-CAM visualization revealed that the network focused its predictions on physically meaningful microstructural regions such as cell walls and strut junctions, which confirms that the proposed network can be classified as an interpretable, non-destructive, and data-driven framework for predicting and understanding bio-based PU foams’ mechanical behavior, hence reducing the inconvenience caused by time-consuming manufacturing and destructive testing. Full article

Bearing fault diagnosis increasingly relies on multiple sensors, since compound or multi-location faults can produce signatures that are only partially captured by a single sensor. The decision-level fusion (DLF) of independently trained models offers a practical way to combine such complementary information, yet systematic comparisons across DLF techniques remain scarce, particularly for measurements from different sensor locations. This paper benchmarks six DLF strategies, i.e, Max, Average, Majority Voting, Weighted Sum, Dempster–Shafer, and Stacking, on a dual-branch one-dimensional convolutional neural network (1D-CNN) with each branch trained end-to-end on vibration signals from a distinct bearing location. On a two-sensor test bench covering seven health conditions, all methods exceed 99.7% accuracy on clean signals, while Dempster–Shafer fusion proves markedly more robust under noise, retaining up to 84% accuracy at a 5 dB signal-to-noise ratio (SNR). A conflict-coefficient analysis further provides an interpretable account of when fusion succeeds, linking performance to the confidence complementarity between branches. Full article

Electromagnetic scattering from plunging breaking waves generates strong sea-surface radar returns that degrade radar-based maritime surveillance and target detection performance. This study develops a deep learning framework for automatic classification of simulated plunging-breaker scattering conditions using high-range-resolution profile (HRRP) data. The electromagnetic scattering data are generated using a physics-based Capillary Wave Modification Facet Scattering Model (CWMFSM) combined with ray-tracing techniques. Eight simulated plunging-breaker scattering conditions are constructed by combining two wind speeds, 7 m/s and 10 m/s, with four temporal conditions, Δt1, Δt10, Δt14, and Δt16. A total of 8000 HRRP samples are generated, with 100 normalized range-cell features extracted from each sample. Two deep learning classifiers, an artificial neural network (ANN) and a one-dimensional residual convolutional neural network (1D ResNet CNN), are comparatively evaluated. The ANN achieves an overall classification accuracy of 96%, compared with 91% for the 1D ResNet CNN under the simulated dataset and adopted model configurations. Robustness analysis under controlled additive white Gaussian noise (AWGN) conditions further shows that classification performance decreases as the signal-to-noise ratio is reduced, while noise-augmented training improves the robustness of both classifiers. Overall, the results demonstrate the feasibility of HRRP-based deep learning for distinguishing simulated plunging-breaker scattering conditions from sea-surface radar returns, providing a basis for further investigation of sea-clutter characterization and maritime radar applications. Full article

Winter wheat is a cornerstone of China’s grain production, contributing substantially to national food security and overall cereal output. This study modeled the nonlinear associations between multitemporal remote sensing variables and winter wheat yield. To produce high-spatiotemporal-resolution inputs, we used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to integrate Sentinel-2 normalized difference vegetation index (NDVI) data with MODIS NDVI data, generating NDVI composites at 8-day intervals with a 10-m spatial resolution. The NDVI, actual evapotranspiration (ET), land surface temperature (LST), precipitation (PRE), and soil moisture (SM) were selected as predictors for yield estimation because they are closely associated with winter wheat growth and yield formation during primary growth stages. By integrating the local temporal feature-learning capacity of a one-dimensional convolutional neural network (1-D CNN) with the strength of a bidirectional long short-term memory (BiLSTM) model in capturing temporal dependencies within time series, a BiLSTM–CNN model was constructed for wheat yield estimation and prediction. The BiLSTM–CNN model showed higher estimation accuracy than individual BiLSTM and 1-D CNN models, with an R² of 0.69 and root mean square error (RMSE) of 478.68 kg/hm². The use of all the parameters produced the best estimation performance among all the parameter combinations. Approximately two months before harvest, the model still provided satisfactory yield prediction accuracy. This study provides an important theoretical basis for high-accuracy regional winter wheat yield estimation and pre-harvest forecasting. Full article

Hyperspectral image (HSI) classification benefits from rich spectral information; however, high dimensionality of HSI data increases computational cost, noise sensitivity, and the risk of overfitting when labeled samples are limited. Most pretrained computer vision networks are designed for three-channel inputs, making direct application to hyperspectral cubes difficult. Conventional principal component analysis (PCA) ranks components by total variance without distinguishing useful signal variance from noise-related variance, which can reduce the reliability of the resulting representation when only a few components are retained. This paper proposes a data-augmented Noise-Adjusted Principal Component Analysis (DA-NAPCA) framework for deep learning-based HSI classification. By accounting for estimated noise covariance, NAPCA orders the transformed components by signal-to-noise ratio rather than total variance, while data augmentation mitigates the overfitting risk when labeled samples are limited. Unlike typical NAPCA/MNF applications, which select the number of retained components empirically, DA-NAPCA deliberately retains three noise-adjusted components to form a compact three-channel representation, enabling pretrained models designed for three-channel inputs to be fine-tuned without modifying their input layers. The framework is evaluated using a 3D convolutional neural network (3D-CNN) for spatial–spectral feature learning and a pretrained EfficientNet-B0 model for lightweight transfer learning. Although this paper uses 3D-CNN and EfficientNet-B0 as illustrative examples, the proposed DA-NAPCA framework is a representation-level preprocessing approach and does not require architecture-specific modification. Experiments conducted on the Indian Pines, University of Pavia, and Salinas datasets compare DA-NAPCA with RGB, band selection, PCA-based dimensionality reduction, and ablation variants. Across the three datasets, DA-NAPCA achieved mean overall accuracies of 93.11–94.71% with 3D-CNN and 95.93–97.44% with EfficientNet-B0. Compared with the second-best baseline method, DA-NAPCA improved overall accuracy by 2.75–7.58 percentage points with 3D-CNN and 1.28–2.12 percentage points with EfficientNet-B0. These results demonstrate that combining a compact noise-adjusted representation with spatial augmentation provides an effective input representation for deep learning-based HSI classification. Full article

Under intensified climate change, improving yield estimation accuracy is essential for food security and precision agricultural management. This research aimed to enhance summer maize yield estimation accuracy. GF-1 imagery was used to derive vegetation indices for leaf area index (LAI) modeling; vegetation index–LAI models used partial least squares regression (PLSR), random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), convolutional neural network (CNN), and categorical feature-enhanced gradient boosting (CatBoost). Retrieved summer maize LAI and measured soil moisture (SM) were assimilated into a calibrated World Food Studies (WOFOST) model using ensemble Kalman filtering (EnKF) and four-dimensional variational assimilation (4D-Var). The influence of ensemble size and assimilation period on the accuracy of yield simulations was analyzed. Results showed that all machine learning models effectively retrieved LAI, with CNN achieving the highest accuracy under the evaluation conditions of this study. Compared with the non-assimilation scenario, data assimilation significantly improved WOFOST-based maize yield estimation; EnKF performed best for yield estimation, whereas 4D-Var yielded slightly lower LAI simulation errors than EnKF under the experimental conditions of this study. Among the ensemble sizes evaluated, an ensemble size of 100 produced the lowest yield prediction error, while assimilation during the tasseling-to-grain-filling period resulted in lower yield prediction errors than continuous whole-growth-period assimilation under the conditions examined. Overall, data assimilation effectively improved WOFOST-based maize yield estimation and supported regional crop monitoring and precision agricultural management. Full article

Cross-condition bearing fault diagnosis suffers from severe performance degradation due to domain shift across different operating conditions, especially when only a few labeled target-domain samples are available. To address this challenge, this paper proposes a progressive attention-guided two-stage transfer learning framework for few-shot bearing fault diagnosis across different fixed operating points. First, the raw time-domain vibration signals are fused with frequency-domain representations extracted by short-time Fourier transform (STFT) to enhance fault feature representation. Then, a progressive attention-guided feature learning strategy is developed by integrating dual efficient channel attention (ECA) modules into a deep one-dimensional convolutional neural network (1D-CNN), enabling the network to adaptively emphasize fault-sensitive features while suppressing redundant information. Subsequently, a two-stage transfer learning strategy is designed, consisting of transferable feature learning from the source domain and few-shot adaptation to the target domain. During target-domain adaptation, key feature extraction layers are frozen, and a sample-balanced optimization mechanism is introduced to alleviate the dominance of source-domain samples during joint training. Experimental results on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves an average accuracy of 99.96% across three cross-condition transfer tasks. Furthermore, experiments conducted on a self-built shaft system dataset show that the proposed method achieves an average accuracy of 87.11% under three representative transfer scenarios. The results verify that the proposed framework effectively mitigates domain shift and enables accurate bearing fault diagnosis with limited labeled target-domain samples. Full article

This study assessed the integration of Unmanned Aerial Vehicles (UAVs) and satellite images (Sentinel-1 and Sentinel-2) in advanced machine-learning techniques to monitor the ABV of quinoa crops (Jacha Grano variety) across the Bolivian Altiplano. The proposed method follows a two-step procedure. First, UAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs at a 10 m spatial resolution from the crop canopy 3D model and classification, respectively. Secondly, several spectral and polarization/texture indices derived from Sentinel-2 and -1 images were integrated into three decision-tree-based machine-learning models (Random Forest-RF, Gradient Boosting-GB, eXtreme Gradient Boosting-XGB), and one CNN-based machine-learning model to estimate ABV. Additionally, a Stacking Model (STM) build on top of the three decision-tree-based models was considered for comparison. Model evaluation was also performed in a two-step approach. First, a 10-fold cross-validation strategy was used to highlight ABV sensitivity to Sentinel-2 and Sentinel-1 alone and in combination. Secondly, a Leave-One-Plot-Out Cross-Validation (LOPOCV) strategy was used to avoid autocorrelation between the training and evaluation dataset and therefore provided more insight into ABV mapping potential. The results showed that the combination of Sentinel-1 and Sentinel-2 features in the CNN model achieved the best predictive performance with R² and RMSE values of 0.64 and 0.39 m³ ∙ 100 m⁻², respectively. These findings highlight the potential of integrating multi-source information in advanced artificial intelligence algorithms for quinoa ABV monitoring, offering new insights toward the identification of sustainable practices across remote regions with complex socio-economic contexts. Full article