Search Results (372)

Building change detection involves identifying areas where buildings have changed by comparing multi-temporal remote sensing imagery of the same geographical region. Recent advances in Transformer-based methods have significantly improved remote sensing change detection. However, current Transformer models still exhibit persistent limitations in effectively extracting multi-scale semantic features within complex scenarios. To more effectively extract multi-scale semantic features in complex scenes, we propose a novel model, which is the Transformer-based Multi-Scale Semantic Extraction Change Detection Network (MSSE-CDNet). The model employs a Siamese network architecture to enable precise change recognition. MSSE-CDNet comprises four parts, which together contain five modules: (1) a CNN feature extraction module, (2) a multi-scale semantic extraction module, (3) a Transformer encoder and decoder module, and (4) a prediction module. Comprehensive experiments on the standard LEVIR-CD benchmark for building change detection demonstrate our approach’s superiority over state-of-the-art methods. Compared to existing models such as FC-Siam-Di, FC-Siam-Conc, DTCTSCN, BIT, and SNUNet, MSSE-CDNet achieves significant and consistent gains in performance metrics, with F1 scores improved by 4.22%, 6.84%, 2.86%, 1.22%, and 2.37%, respectively, and Intersection over Union (IoU) improved by 6.78%, 10.74%, 4.65%, 2.02%, and 3.87%, respectively. These results robustly substantiate the effectiveness of our framework on an established benchmark dataset. Full article

Predicting the concentrations of air pollutants, particularly PM_2.5, with accuracy and dependability is crucial for protecting human health and preserving a healthy natural environment. This research proposes a deep learning-based, robust prediction system to predict regional PM_2.5 concentrations for the next one to twenty-four hours. To start, the input features of the prediction system are initially screened using a correlation analysis of various air pollutants and meteorological factors. Next, the BiTCN-ISInformer prediction model with a two-branch parallel architecture is constructed. On the one hand, the model improves the probabilistic sparse attention mechanism in the traditional Informer network by optimizing the sampling method from a single sparse sampling to a synergistic mechanism combining sparse sampling and importance sampling, which improves the prediction accuracy and reduces the computational complexity of the model; on the other hand, through the introduction of the bi-directional time-convolutional network (BiTCN) and the design of parallel architecture, the model is able to comprehensively model the short-term fluctuations and long-term trends of the temporal data and effectively increase the inference speed of the model. According to experimental research, the proposed model performs better in terms of prediction accuracy and performance than the most advanced baseline model. In the single-step and multi-step prediction experiments of Shanghai’s PM_2.5 concentration, the proposed model has a root mean square error (RMSE) ranging from 2.010 to 10.029 and a mean absolute error (MAE) ranging from 1.436 to 6.865. As a result, the prediction system proposed in this research shows promise for use in air pollution early warning and prevention. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

Noise interference and multipath effects in complex marine environments seriously constrain the performance of hydroacoustic positioning systems. Traditional millisecond-level signal application and processing methods are widely used in existing research; however, it is difficult to meet the requirements of centimeter-level positioning accuracy in marine engineering. To address this problem, this study proposes a hydroacoustic positioning method based on a short baseline system for the cooperative reception of multi-channel signals. The method adopts ultra-short pulse signals with microsecond pulse width, and significantly improves the system signal-to-noise ratio and anti-interference capability through multi-channel signal alignment and coherent superposition techniques; meanwhile, a joint energy gradient-phase detection algorithm is designed, which solves the instability problem of the traditional cross-correlation algorithm in the detection of ultra-short pulse signals through the identification of signal stability intervals and accurate phase estimation. Simulation verification shows that the 8-hydrophone × 4-channel configuration can achieve 36.06% signal-to-noise gain under harsh environmental conditions (−10 dB), and the performance of the joint energy gradient-phase detection algorithm is improved by about 19.1% compared with the traditional method in an integrated manner. Marine tests further validate the engineering practicability of the method, with an average SNR gain of 2.27 dB achieved for multi-channel signal reception, and the TDOA estimation stability of the new algorithm is up to 32.0% higher than that of the conventional method, which highlights the significant advantages of the proposed method in complex marine environments. The results show that the proposed method can effectively mitigate the noise interference and multipath effects in complex marine environments, significantly improve the accuracy and stability of hydroacoustic positioning, and provide reliable technical support for centimeter-level accuracy applications in marine engineering. Full article

The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate for meeting the tilt monitoring requirements of super-high-rise industrial heritage chimneys. To address these issues, this study proposes a tilt monitoring method for super-high-rise industrial heritage chimneys based on LiDAR point clouds. Firstly, LiDAR point cloud data were acquired using a ground-based LiDAR measurement system. This system captures high-density point clouds and precise spatial attitude data, synchronizes multi-source timestamps, and transmits data remotely in real time via 5G, where a data preprocessing program generates valid high-precision point cloud data. Secondly, multiple cross-section slicing segmentation strategies are designed, and an automated tilt monitoring algorithm framework with adaptive slicing and collaborative optimization is constructed. This algorithm framework can adaptively extract slice contours and fit the central axes. By integrating adaptive slicing, residual feedback adjustment, and dynamic weight updating mechanisms, the intelligent extraction of the unit direction vector of the central axis is enabled. Finally, the unit direction vector is operated with the x- and z-axes through vector calculations to obtain the tilt-azimuth, tilt-angle, verticality, and verticality deviation of the central axis, followed by an accuracy evaluation. On-site experimental validation was conducted on a super-high-rise industrial heritage chimney. The results show that, compared with the results from the traditional method, the relative errors of the tilt angle, verticality, and verticality deviation of the industrial heritage chimney obtained by the proposed method are only 9.45%, while the relative error of the corresponding tilt-azimuth is only 0.004%. The proposed method enables high-precision, non-contact, and globally perceptive tilt monitoring of super-high-rise industrial heritage chimneys, providing a feasible technical approach for structural safety assessment and preservation. Full article

Long-term and accurate prediction of air pollutant concentrations can serve as a foundation for air pollution warning and prevention, which is crucial for social development and human health. In this study, we provide a model for predicting the concentration of air pollutants based on big data spatiotemporal correlation analysis and deep learning methods. Based on an improved temporal convolutional network (TCN) and a bi-directional gated recurrent unit (BiGRU) as the fundamental architecture, the model adds two attention mechanisms to improve performance: Squeeze and Excitation Networks (SENet) and Convolutional Block Attention Module (CBAM). The improved TCN moves the residual connection layer to the network’s front end as a preprocessing procedure, improving the model’s performance and operating efficiency, particularly for big data jobs like air pollution concentration prediction. The use of SENet improves the model’s comprehension and extraction of long-term dependent features from pollutants and meteorological data. The incorporation of CBAM enhances the model’s perception ability towards key local regions through an attention mechanism in the spatial dimension of the feature map. The TCN-SENet-BiGRU-CBAM model successfully realizes the prediction of air pollutant concentrations by extracting the spatiotemporal features of the data. Compared with previous advanced deep learning models, the model has higher prediction accuracy and generalization ability. The model is suitable for prediction tasks from 1 to 12 h in the future, with root mean square error (RMSE) and mean absolute error (MAE) ranging from 5.309~14.043 and 3.507~9.200, respectively. Full article

As a national treasure, architectural heritage carries multiple value dimensions such as history, technology, art, and culture. With the increasing demand for architectural heritage protection and utilization, the traditional static digital model of architectural heritage based on geometric expression can no longer meet the practical application of multi-stage and multi-level scenarios. To this end, this paper proposes a value-chain-driven multi-level digital twin model of architectural heritage. Based on the three-stage logic of protection, management, and dissemination of value-chain classification, it integrates four types of models: geometry, physics, rules, and behavior. Combined with different hierarchical application levels, the digital model of architectural heritage is refined into a VCLOD (Value-Chain-Driven Level of Detail) detail hierarchy system to achieve a unified expression from spatial form restoration to intelligent response. Through the empirical application of three typical scenarios: the full-area guided tour of the Forbidden City, the exhibition curation of the central axis and the preventive protection of the Meridian Gate, the model shows the following specific results: (1) the efficiency of tourist guidance is improved through real-time personalized path planning; (2) the exhibition planning and visitor experience are improved through dynamic monitoring and interactive management of the exhibition environment; (3) the predictive analysis and preventive protection measures of structural safety are realized, effectively ensuring the structural safety of the Meridian Gate. The research results provide a theoretical basis and practical support for the systematic expression and intelligent evolution of digital twins of architectural heritage. Full article

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, and buses. To address the limitations of existing methods in capturing these diverse trajectory characteristics, we propose a spatiotemporal multi-model ensemble framework, which is an ensemble model called GLEN (GCN and LSTM Ensemble Network). Firstly, the trajectory feature adaptive driven model selection mechanism classifies trajectories into dynamic travel and fixed-route scenarios. Secondly, we use a Graph Convolutional Network (GCN) to capture dynamic travel patterns and Long Short-Term Memory (LSTM) network to model fixed-route patterns. Subsequently the outputs of these models are dynamically weighted, integrated, and fused over a spatiotemporal grid to produce accurate forecasts of urban total traffic flow at multiple future time steps. Finally, experimental validation using Beijing’s Chaoyang district datasets demonstrates that our framework effectively captures spatiotemporal and interactive characteristics between multimodal travel trajectories and outperforms mainstream baselines, thereby offering robust support for urban traffic management and planning. Full article

This study proposes an innovative method that integrates multi-source remote sensing technologies and artificial intelligence to meet the urgent needs of deformation monitoring and ecohydrological environment analysis in Great Wall heritage protection. By integrating synthetic aperture radar (InSAR) technology, low-altitude oblique photogrammetry models, and the three-dimensional Gaussian splatting model, an integrated air–space–ground system for monitoring and understanding the Great Wall is constructed. Low-altitude tilt photogrammetry combined with the Gaussian splatting model, through drone images and intelligent generation algorithms (e.g., generative adversarial networks), quickly constructs high-precision 3D models, significantly improving texture details and reconstruction efficiency. Based on the 3D Gaussian splatting model of the AHLLM-3D network, the integration of point cloud data and the large language model achieves multimodal semantic understanding and spatial analysis of the Great Wall’s architectural structure. The results show that the multi-source data fusion method can effectively identify high-risk deformation zones (with annual subsidence reaching −25 mm) and optimize modeling accuracy through intelligent algorithms (reducing detail error by 30%), providing accurate deformation warnings and repair bases for Great Wall protection. Future studies will further combine the concept of ecological water wisdom to explore heritage protection strategies under multi-hazard coupling, promoting the digital transformation of cultural heritage preservation. Full article

The fraction of absorbed photosynthetically active radiation (FAPAR) stands as a pivotal parameter within the Earth system, quantifying the energy exchange between vegetation and solar radiation. Accordingly, there is an urgent need for comprehensive validation studies to accurately quantify uncertainties and improve the reliability of FAPAR-based applications. This study validated five global FAPAR products, MOD15A2H, MYD15A2H, VNP15A2H, GEOV2, and GEOV3, over four boreal forest sites in North America. Qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) of each product were analyzed. Time series high-resolution reference FAPAR maps were developed using the Harmonized Landsat and Sentinel-2 dataset. The reference FAPAR maps revealed a strong agreement with the in situ FAPAR from AmeriFlux (correlation coefficient (R) = 0.91; root mean square error (RMSE) = 0.06). The results revealed that global FAPAR products show similar uncertainties (RMSE: 0.16 ± 0.04) and moderate agreement with the reference FAPAR (R = 0.75 ± 0.10). On average, 34.47 ± 6.91% of the FAPAR data met the goal requirements of the Global Climate Observing System (GCOS), while 54.41 ± 6.89% met the threshold requirements of the GCOS. Deciduous forests perform better than evergreen forests, and the products tend to underestimate the reference data, especially for the beginning and end of growing seasons in evergreen forests. There are no obvious quality differences at different QQFs, and the relative QQI can be used to filter high-quality values. To enhance the regional applicability of global FAPAR products, further algorithm improvements and expanded validation efforts are essential. Full article

With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

The Xixia Imperial Tombs (XITs) represent a crucial, yet still largely mysterious, component of the Tangut civilization’s legacy. Located in northwestern China, this extensive necropolis offers invaluable insights into the Tangut state, culture, and burial practices. This study employs an integrated approach utilizing multi-resolution and multi-temporal satellite remote sensing data, including Gaofen-2 (GF-2), Landsat-8 OLI, declassified GAMBIT imagery, and Google Earth, combined with deep learning techniques, to conduct a comprehensive archaeological investigation of the XITs’ burial landscape. We performed geomorphological analysis of the surrounding environment and automated identification and mapping of burial mounds and mausoleum features using YOLOv5, complemented by manual interpretation of very-high-resolution (VHR) satellite imagery. Spectral indices and image fusion techniques were applied to enhance the detection of archaeological features. Our findings demonstrated the efficacy of this combined methodology for archaeology prospect, providing valuable insights into the spatial layout, geomantic considerations, and preservation status of the XITs. Notably, the analysis of declassified GAMBIT imagery facilitated the identification of a suspected true location for the ninth imperial tomb (M9), a significant contribution to understanding Xixia history through remote sensing archaeology. This research provides a replicable framework for the detection and preservation of archaeological sites using readily available satellite data, underscoring the power of advanced remote sensing and machine learning in heritage studies. Full article

Urban–rural integration (URI) plays a crucial role in advancing rural revitalization and the modernization of agriculture. Nevertheless, numerous nations encounter persistent obstacles, including inefficient resource mobility across urban–rural divides and uneven industrial distribution, while striving to foster such integration. Agricultural new quality productive forces (ANPFs) offer an innovation-led production framework fueled by advances in agricultural technology, allowing urban–rural integration (URI) through improved resource mobility between cities and rural regions. Utilizing panel data from 30 Chinese provinces (2013–2022), this study employs a two-way fixed effects model, mediation analysis model, threshold regression model, and the spatial Durbin model to investigate the transmission mechanism and spatial spillover effect of agricultural new quality productive forces (ANPFs) on urban–rural integration (URI). The findings show the following: (1) Agricultural new quality productive forces (ANPFs) significantly influence urban–rural integration (URI). (2) The influence is significantly stronger in western China than in the eastern and central regions. (3) Industrial restructuring and upgrading (IND) function as a mediating influence in this connection. (4) The role of informatization (INF) has a dual-threshold effect. (5) Geographically, while these forces promote local integration, they may impede progress in nearby regions. This study provides new empirical insights into the factors that influence urban–rural integration (URI) and proposes policy solutions to promote sustainable regional development. Full article