Search Results (208)

We compare several methods for predicting future built-up land cover using only a short yearly time series of satellite-derived binary urban maps. Existing methods of built-up expansion forecasting often rely on ancillary datasets such as utility networks, distance to transportation nodes, and population density maps, along with remotely sensed aerial or satellite imagery. Such ancillary datasets are not always available and lack the temporal density of satellite imagery. Moreover, existing work often focuses on quantifying the expected volume of built-up expansion, rather than predicting where exactly that expansion will occur. To address these gaps, we evaluate six methods for the creation of prediction maps showing expected areas of future built-up expansion, using yearly built/not-built maps derived from Sentinel-2 imagery as inputs: Cellular Automata, logistic regression, Support Vector Machines, Random Forests, Convolutional Neural Networks (CNNs), and CNNs with the addition of long short-term memory (ConvLSTM). Of these six, we find CNNs to be the best-performing method, with an average Cohen’s kappa score of 0.73 across nine study sites in the continental United States. Full article

Accurate fire source location in an aircraft cargo compartment cannot be determined by common design practices. This study proposes an advanced fire location inversion framework based on a Convolutional Long-Short-Term Memory (ConvLSTM) network. A self-designed interpolation preprocessing module is introduced to realize the integration of spatial and temporal sensor data. The model was trained and validated using a comprehensive database generated from large-scale fire dynamics simulations. Hyperparameter optimization, including a learning rate of 0.001 and a 5 × 5 convolution kernel size, can effectively avoid the systematic errors introduced by interpolation preprocessing, further enhancing model robustness. Validation in simplified scenarios demonstrated a mean squared error of 0.0042 m and a mean positional deviation of 0.095 m for the fire source location. Moreover, the present study assessed the model’s timeliness and reliability in full-scale cabin complex scenarios. The model maintained high performance across varying heights within cargo compartments, achieving a correlation coefficient of 0.99 and a mean absolute relative error of 1.9%. Noteworthily, reasonable location accuracy can be achieved with a minimum of three detectors, even in obstructed environments. These findings offer a robust tool for enhancing fire safety systems in aviation and other similar complex scenarios. Full article

Despite the pivotal role of unmanned aerial vehicles (UAVs) in intelligent inspection tasks, existing video instance segmentation methods struggle with irregular deforming targets, leading to inconsistent segmentation results due to ineffective feature offset capture and temporal correlation modeling. To address this issue, we propose a hierarchical offset compensation and temporal memory update method for video instance segmentation (HT-VIS) with a high generalization ability. Firstly, a hierarchical offset compensation (HOC) module in the form of a sequential and parallel connection is designed to perform deformable offset for the same flexible target across frames, which benefits from compensating for spatial motion features at the time sequence. Next, the temporal memory update (TMU) module is developed by employing convolutional long-short-term memory (ConvLSTM) between the current and adjacent frames to establish the temporal dynamic context correlation and update the current frame feature effectively. Finally, extensive experimental results demonstrate the superiority of the proposed HDNet method when applied to the public YouTubeVIS-2019 dataset and a self-built UAV-Seg segmentation dataset. On four typical datasets (i.e., Zoo, Street, Vehicle, and Sport) extracted from YoutubeVIS-2019 according to category characteristics, the proposed HT-VIS outperforms the state-of-the-art CNN-based VIS methods CrossVIS by 3.9%, 2.0%, 0.3%, and 3.8% in average segmentation accuracy, respectively. On the self-built UAV-VIS dataset, our HT-VIS with PHOC surpasses the baseline SipMask by 2.1% and achieves the highest average segmentation accuracy of 37.4% in the CNN-based methods, demonstrating the effectiveness and robustness of our proposed framework. Full article

High-accuracy streamflow forecasting with long lead times can help promote the efficient utilization of water resources. However, the construction of cascade reservoirs has allowed the evolution of natural continuous rivers into multi-block rivers. The existing streamflow forecasting methods fail to consider the impact of reservoir operation. Thus, a novel short-term streamflow forecasting method for multi-block watersheds was proposed by integrating machine learning and hydrological models. Firstly, based on IMERG precipitation, the forecast precipitation product’s error is corrected by the long short-term memory neural network (LSTM). Secondly, coupling convolutional LSTM (ConvLSTM) and LSTM, operation rules for cascade reservoirs are extracted. Thirdly, a short-term deterministic streamflow forecasting model was built for multi-block watersheds. Finally, according to the sources of forecasting errors, probabilistic streamflow forecasting models based on the Gaussian mixture model (GMM) were proposed, and their performances were compared. Taking the Yalong River as an example, the main results are as follows: (1) Deep learning models (ConvLSTM and LSTM) show good performance in forecast precipitation correction and reservoir operation rule extraction, contributing to streamflow forecasting accuracy. (2) The proposed streamflow deterministic forecasting method has good forecasting performance with NSE above 0.83 for the following 1–5 days. (3) The GMM model, using upstream evolutionary forecasted streamflow, interval forecasted streamflow, and downstream forecasted streamflow as the input–output combination, has good probabilistic forecasting performance and can adequately characterize the “non-normality” and “heteroskedasticity” of forecasting uncertainty. Full article

This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). Such integration offers transformative opportunities for improving dairy cattle welfare, but current applications remain limited. We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. Although object detection models, including You Only Look Once (YOLO), EfficientDet, and sequence models, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Long Short-Term Memory (convLSTM), have improved detection and classification, significant challenges remain, including occlusions, annotation bottlenecks, dataset diversity, and limited generalizability. Existing interaction inference methods rely heavily on distance-based approximations (i.e., assuming that proximity implies social interaction), lacking the semantic depth essential for comprehensive SNA. To address this, we propose innovative methodological intersections such as pose-aware SNA frameworks and multi-camera fusion techniques. Moreover, we explicitly discuss ethical challenges and data governance issues, emphasizing data transparency and animal welfare concerns within precision livestock contexts. We clarify how these methodological innovations directly impact practical farming by enhancing monitoring precision, herd management, and welfare outcomes. Ultimately, this synthesis advocates for strategic, empathetic, and ethically responsible precision dairy farming practices, significantly advancing both dairy cow welfare and operational effectiveness. Full article

The ionospheric total electron content (TEC) has complex spatiotemporal variations, making its spatiotemporal prediction challenging. Capturing long-range spatial dependencies is of great significance for improving the spatiotemporal prediction accuracy of TEC. Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. Self-Attention Convolutional Long Short-Term Memory (SA-ConvLSTM) can selectively store and focus on long-range spatial dependencies, but it requires the input length and output length to be the same due to its “n vs. n” structure, limiting its application. To solve this problem, this paper proposes an encoder-decoder SA-ConvLSTM, abbreviated as ED-SA-ConvLSTM. It can effectively capture long-range spatial dependencies using SA-ConvLSTM and achieve unequal input-output lengths through encoder–decoder structure. To verify its performance, the proposed ED-SA-ConvLSTM was compared with C1PG, ConvLSTM, and PredRNN from multiple perspectives in the area of 12.5° S–87.5° N, 25° E–180° E, including overall quantitative comparison, comparison across different months, comparison at different latitude regions, visual comparisons, and comparison under extreme situations. The results have shown that, in the vast majority of cases, the proposed ED-SA-ConvLSTM outperforms the comparative models. Full article

The South China Sea, a vital marginal sea in tropical–subtropical Southeast Asia, plays a globally significant role in marine biodiversity and climate system dynamics. The accurate monitoring of its thermal structure is essential for ecological and climatic studies, yet retrieving subsurface temperature remains challenging due to complex ocean–atmosphere interactions. This study develops a Convolutional Long Short-Term Memory (ConvLSTM) neural network, integrating multi-source satellite remote sensing data, to reconstruct the Ocean Subsurface Temperature Structure (OSTS). To address the multiparameter complexity of temperature retrieval, physical constraints—particularly the heat budget balance of water bodies—are incorporated into the loss function. Experiments demonstrate that the physics-informed ConvLSTM model significantly improves the temperature estimation accuracy by simultaneously optimizing the physical consistency and predictive performance. The proposed approach advances ocean remote sensing by synergizing data-driven learning with thermodynamic principles, offering a robust framework for understanding the South China Sea’s thermal variability. Full article

Background/Objectives: Disfluencies such as repetitions, prolongations, interjections, and blocks in sounds, syllables, or words can sometimes hinder communication. Currently, disfluencies are manually measured, which has inherent limitations, such as being time-consuming and subjective, which can lead to inconsistencies in measurement. Methods: To address these challenges, this study presents an innovative automated system for detecting disfluencies utilizing advanced artificial intelligence technologies; specifically, deep learning models such as convolutional neural networks (CNN) and convolutional long short-term memory (ConvLSTM). The system was evaluated using two benchmark datasets: FluencyBank and SEP-28K. Results: Our proposed system demonstrates remarkable performance, achieving detection accuracies of 0.97 and 0.96, respectively, for CNNs and ConvLSTM models. These results not only exceed those of prior studies but also highlight the effectiveness of our approach in enhancing stuttering evaluation. Conclusions: By providing a reliable and efficient tool for professionals in therapeutic settings, our system represents a significant advancement in the field, offering improved outcomes for individuals affected by stuttering. Full article

Accurate prediction of the spatiotemporal distribution of chlorophyll-a (Chl-a) is essential for evaluating marine ecosystem health and predicting ecological disasters. Current methods struggle to capture short-term variability and periodic trends in Chl-a, especially in noise-prone coastal regions. This study aims to enhance the prediction of marine Chl-a concentrations by introducing the chlorophyll-a concentration prediction model (ChlaPM), which was developed on the basis of a convolutional long short-term memory (ConvLSTM) network. The model integrates recent spatiotemporal feature extraction (RSTFE), periodic feature extraction (PFE), and denoising fusion (DNF) modules to effectively capture short-term spatiotemporal changes and periodic variations in Chl-a concentrations. In this study, the performance of ChlaPM in single-step and multistep predictions was evaluated using monthly average Chl-a remote sensing data spanning 1998–2023. The results indicate that compared with the RSTFE model, the ChlaPM model achieves substantial reductions in the root mean square error (RMSE) of 53.84%, 53.58%, and 49.70% for predicting Chl-a concentrations 1 month, 3 months, and 6 months into the future, respectively. These findings highlight the effectiveness of ChlaPM in addressing short-term variability and periodic trends and significantly enhances the accuracy of Chl-a prediction. Future work will focus on integrating additional relevant marine variables into the prediction model to further improve its prediction capabilities. Full article

Noise pollution in densely populated urban areas is a major issue that affects both quality of life and public health. This study explores and evaluates the application of deep learning techniques to predict urban noise levels, using the city of Madrid, Spain, as a case study. Several complementary approaches are compared: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs). Each technique contributes specific strengths to the modeling of spatiotemporal series: CNNs are effective at capturing local spatial patterns, while LSTM networks excel at modeling long-term temporal dependencies. In turn, GCNs integrate spatial structure and temporal dynamics through graph representations, achieving superior performance compared to traditional approaches or models based solely on CNN or LSTM architectures. This study provides empirical evidence of the potential of GCNs to effectively address the spatiotemporal complexity of urban noise and highlights new possibilities for their application in urban planning and environmental management. Our hybrid model, CNN1D+LSTM+TransformerConv, achieves a root mean squared error (RMSE) of 0.0169, reducing the error by 5.1% compared to the second-best model (Transformer, RMSE = 0.0178), and reaches a correlation coefficient of 0.9601. The results demonstrate that explicitly integrating the spatial component through graphs, alongside temporal sequence modeling, leads to improved prediction accuracy over alternative methods. Full article

As China’s urban–rural integration progresses, the connections between urban and rural areas continue to strengthen, making the spatial matching between transportation infrastructure and tourism resources increasingly crucial for coordinated regional development. This study investigates the spatial–temporal mismatch between transportation development and tourism spatial vitality in Yunnan Province, proposing optimization strategies to improve their coordination. Using Weibo check-in big data and OpenStreetMap transportation network data, we apply Convolutional Long Short-Term Memory (ConvLSTM) networks and bivariate spatial autocorrelation analysis to examine this relationship. The results show strong transportation–tourism matching in Kunming and surrounding areas. However, northwest and southern Yunnan exhibit significant mismatches—despite transportation improvements, underdeveloped tourism resources constrain vitality growth. Particularly in some remote regions, well-developed transportation infrastructure coexists with low tourism vitality, revealing persistent spatial mismatches between transport facilities and tourism resources. In general, transportation infrastructure development generally enhances tourism spatial vitality, but requires coordinated tourism resource development and market demand alignment. The study results provide a basis for improving the coordinated development of transportation and tourism, offering practical guidance for policymakers to promote balanced regional development and urban–rural integration. Full article

Accurate and reliable precipitation prediction remains a significant challenge due to an incomplete understanding of regional meteorological dynamics and limitations in forecasting routine weather events. To overcome these challenges, we propose a novel model, DC-CNN-BiLSTM, which integrates a dilation causal convolutional neural network (DC-CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network. The DC-CNN component, by fusing causal and dilated convolutions, extracts multi-scale spatial features from time series data. In parallel, the BiLSTM module leverages bidirectional memory cells to capture long-term temporal dependencies. This integrated approach effectively links localized meteorological inputs with broader hydrological responses. Experimental evaluation demonstrates that the DC-CNN-BiLSTM model significantly outperforms traditional models. Specifically, the model improves the Root Mean Square Error (RMSE) by 9.05% compared to ConvLSTM and by 32.3% compared to ConvGRU, particularly in forecasting medium- to long-term precipitation. In conclusion, our results validate the benefits of incorporating advanced spatio-temporal feature extraction techniques for precipitation forecasting, ultimately improving disaster preparedness and resource management. Full article

This work explores the classification of driving behaviors using a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks (ConvLSTM). Sensor data are collected from a smartphone application and undergo a preprocessing pipeline, including data normalization, labeling, and feature extraction, to enhance the model’s performance. By capturing temporal and spatial dependencies within driving patterns, the proposed ConvLSTM model effectively differentiates between normal and aggressive driving behaviors. The model is trained and evaluated against traditional stacked LSTM and Bidirectional LSTM (BiLSTM) architectures, demonstrating superior accuracy and robustness. Experimental results confirm that the preprocessing techniques improve classification performance, ensuring high reliability in driving behavior recognition. The novelty of this work lies in a simple data preprocessing methodology combined with the specific application scenario. By enhancing data quality before feeding it into the AI model, we improve classification accuracy and robustness. The proposed framework not only optimizes model performance but also demonstrates practical feasibility, making it a strong candidate for real-world deployment. Full article

This study presents a hybrid deep learning approach for bearing fault diagnosis that integrates continuous wavelet transform (CWT) with an attention-enhanced spatiotemporal feature extraction framework. The model combines time-frequency domain analysis using CWT with a classification architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (BiLSTM), and a 1D convolutional residual network (1D conv ResNet). This architecture effectively captures both spatial and temporal dependencies, enhances noise resilience, and extracts discriminative features from nonstationary and nonlinear vibration signals. The model is initially trained on a controlled laboratory bearing dataset and further validated on real and artificial subsets of the Paderborn bearing dataset, demonstrating strong generalization across diverse fault conditions. t-SNE visualizations confirm clear separability between fault categories, supporting the model’s capability for precise and reliable feature learning and strong potential for real-time predictive maintenance in complex industrial environments. Full article

Short-term sea surface temperature (SST) forecasts are crucial for operational oceanology. This study introduces a specialized Transformer model (U-Transformer) to forecast global short-term SST variability and compares its performance with Convolutional Long Short-Term Memory (ConvLSTM) and Residual Neural Network (ResNet) models. The U-Transformer model forecast consistently outperformed the ConvLSTM and ResNet models, especially in regions with active mesoscale eddies. Globally, the U-Transformer model achieved SST root mean square errors (RMSEs) ranging from 0.2 °C at a 1-day lead time to 0.54 °C at a 10-day lead time during 2020–2022, with anomaly correlation coefficients (ACCs) decreasing from 0.97 to 0.79, respectively. However, in regions characterized by active mesoscale eddies, RMSEs from the U-Transformer model exceeded the global averages by at least 40%, with values in the Gulf Stream region reaching more than twice the global average. Additionally, ACC values in active mesoscale eddy regions declined more sharply with forecast lead time compared to the global averages, decreasing from approximately 0.96 at a 1-day lead time to 0.73 at a 10-day lead time. Specifically, the ACC value dropped to 0.89 in the Gulf Stream region at a 3-day lead time, while maintaining 0.92 globally. These findings underscore the importance of advanced approaches to enhance SST forecast accuracy in challenging active mesoscale eddy regions. Full article