Search Results (4,777)

Persistent cloud cover during the growing season and mosaic cropping patterns introduce temporal gaps and mixed pixels, undermining the reliability of large-scale crop identification and acreage statistics. To address these issues, we develop a high spatiotemporal-resolution remote-sensing approach tailored to heterogeneous farmlands. First, an improved Spatiotemporal Adaptive Reflectance Fusion Model (STARFM) is used to fuse Landsat, Sentinel-2, and MODIS observations, reconstructing a continuous Normalized Difference Vegetation Index (NDVI) time series at 30 m spatial and 8-day temporal resolution. Second, at the field scale, we derive phenological descriptors from the reconstructed series—key phenophase timing, amplitude, temporal trend, and growth rate—and use a Random Forest (RF) classifier for detailed crop discrimination. We further integrate SHapley Additive exPlanations (SHAP) to quantify each feature’s class-discriminative contribution and signed effect, thereby guiding feature-set optimization and threshold refinement. Finally, we generate a 2024 crop distribution map and conduct comparative evaluations. Relative to baselines without fusion or without phenological variables, the fused series mitigates single-sensor limitations under frequent cloud/rain and irregular acquisitions, enhances NDVI continuity and robustness, and reveals inter-crop temporal phase shifts that, when jointly exploited, reduce early-season confusion and improve identification accuracy. Independent validation yields an overall accuracy (OA) of 90.78% and a Cohen’s kappa(κ) coefficient of 0.882. Coupling dense NDVI reconstruction with phenology-aware constraints and SHAP-based interpretability demonstrably improves the accuracy and reliability of cropping-structure extraction in complex agricultural regions and provides a reusable pathway for regional-scale precision agricultural monitoring. Full article

Maritime UAV operations face challenges in real-time ship detection. Complex ocean backgrounds, drastic scale variations, and prevalent distant small targets create difficulties. We propose MCB-RT-DETR, a real-time detection transformer enhanced by multi-component boosting. This method builds upon the RT-DETR architecture. It significantly improves detection under wave interference, lighting changes, and scale differences. Key innovations address these challenges. An Orthogonal Channel Attention (Ortho) mechanism preserves high-frequency edge details in the backbone network. Receptive Field Attention Convolution (RFAConv) enhances robustness against background clutter. A Small Object Detail Enhancement Pyramid (SOD-EPN) strengthens small-target representation. SOD-EPN combines SPDConv with multi-scale CSP-OmniKernel transformations. The neck network integrates ultra-lightweight DySample upsampling. This enables content-aware sampling for precise multi-scale localization. The method maintains high computational efficiency. Experiments on the SeaDronesSee dataset show significant improvements. MCB-RT-DETR achieves 82.9% mAP@0.5 and 49.7% mAP@0.5:0.95. These correspond to improvements of 4.5% and 3.4% relative to the baseline model. Inference speed maintains 50 FPS for real-time processing. The outstanding performance in cross-dataset tests further validates the algorithm’s strong generalization capability on DIOR remote sensing images and VisDrone2019 aerial scenes. The method provides a reliable visual perception solution for autonomous maritime UAV operations. Full article

Pansharpening plays a crucial role in remote sensing applications, as it enables the generation of high-spatial-resolution multispectral images that simultaneously preserve spatial and spectral information. However, most current methods struggle to preserve local textures and exploit spectral correlations across bands while modeling nonlocal information in source images. To address these issues, we propose a cascaded local–nonlocal pansharpening network (CLNNet) that progressively integrates local and nonlocal features through stacked Progressive Local–Nonlocal Fusion (PLNF) modules. This cascaded design allows CLNNet to gradually refine spatial–spectral information. Each PLNF module combines Adaptive Channel-Kernel Convolution (ACKC), which extracts local spatial features using channel-specific convolution kernels, and a Multi-Scale Large-Kernel Attention (MSLKA) module, which leverages multi-scale large-kernel convolutions with varying receptive fields to capture nonlocal information. The attention mechanism in MSLKA enhances spatial–spectral feature representation by integrating information across multiple dimensions. Extensive experiments on the GaoFen-2, QuickBird, and WorldView-3 datasets demonstrate that the proposed method outperforms state-of-the-art methods in quantitative metrics and visual quality. Full article

This paper analyzes a surface plasmon resonance (SPR) sensor utilizing silver (Ag) and Zirconium Nitride (ZrN) for glucose concentration detection in urine samples by the transfer matrix method (TMM). For effective SP excitation, a high-RI BAF10 prism is thought to be used as the coupling layer in the suggested theoretical design. The performance of the proposed SPR biosensor is theoretically evaluated using the wavelength interrogation technique by analyzing wavelength sensitivity (WS), detection accuracy (DA), figure of merit (FoM), and penetration depth (PD) parameters. Glucose in urine samples serves as the sensing medium (SM) in this biosensor configuration. The sensor achieves a maximum wavelength sensitivity of 6416.66 nm/RIU with a penetration depth of 297.53 nm. The ZrN structure incorporated in the biosensor demonstrates enhanced wavelength sensitivity through its molecular recognition sites that provide strong binding with glucose molecules. The improved wavelength sensitivity is attributed to the greater resonance wavelength shift produced by ZrN, resulting in significant performance enhancement of the biosensor for glucose detection. Benefits of the proposed SPR biosensor include very small urine sample concentration requirements (usually 0 mg/dL to 10 g/dL), compatibility with compact prism-based configurations that support the development of portable and affordable point-of-care devices, and quick detection within a few seconds due to real-time plasmonic response. These features make the sensor ideal for rapid, minimally invasive, and field-deployable glucose monitoring in both home and clinical relevance. Full article

Accurate crop yield estimation under differentiated management practices is a core requirement for the development of smart agriculture. However, current yield estimation models face two major challenges: limited adaptability to different management practices, thus exhibiting poor generalizability, and ineffective integration of multi-source remote sensing features, limiting further improvements in estimation accuracy. To address these issues, this study integrated UAV-based multispectral and thermal infrared remote sensing data to propose a yield estimation framework based on multi-source feature fusion. First, three machine learning algorithms—Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to retrieve key biochemical parameters of winter wheat. The RF model demonstrated superior performance, with retrieval accuracies for chlorophyll, nitrogen, and phosphorus contents of R² = 0.8347, 0.5914, and 0.9364 and RMSE = 0.2622, 0.4127, and 0.0236, respectively. Subsequently, yield estimation models were constructed by integrating the retrieved biochemical parameters with phenotypic traits such as plant height and biomass. The RF model again exhibited superior performance (R² = 0.66, RMSE = 867.28 kg/ha). SHapley Additive exPlanations (SHAP) analysis identified May chlorophyll content (Chl-5) and March chlorophyll content (Chl-3) as the most critical variables for yield prediction, with stable positive contributions to yield when their values exceeded 2.80 mg/g and 2.50 mg/g, respectively. The quantitative assessment of management practices revealed that the straw return + 50% inorganic fertilizer + 50% organic fertilizer (RIO50) treatment under the combined organic–inorganic fertilization regime achieved the highest measured grain yield (11,469 kg/ha). Consequently, this treatment can be regarded as an optimized practice for attaining high yield. This study confirms that focusing on chlorophyll dynamics during key physiological stages is an effective approach for enhancing yield estimation accuracy under varied management practices, providing a technical basis for precise field management. Full article

Self-powered sensors, leveraging their integrated energy harvesting–signal sensing capability, effectively overcome the bottlenecks of traditional sensors, including reliance on external power resources, high maintenance costs, and challenges in large-scale distributed deployment. As a result, they have become a major research focus in fields such as flexible electronics, smart healthcare, and human–machine interaction. This paper reviews the core technical paths of six major types of self-powered sensors developed in recent years, with particular emphasis on the working principles and innovative material applications associated with frictional charge transfer and electrostatic induction, pyroelectric polarization dynamics, hydrovoltaic interfacial streaming potentials, piezoelectric constitutive behavior, battery integration mechanism, and photovoltaic effect. By comparing representative achievements in fields closely related to self-powered sensors, it summarizes breakthroughs in key performance indicators such as sensitivity, detection range, response speed, cyclic stability, self-powering methods, and energy conversion efficiency. The applications discussed herein mainly cover several critical domains, including wearable medical and health monitoring systems, intelligent robotics and human–machine interaction, biomedical and implantable devices, as well as safety and ecological supervision. Finally, the current challenges facing self-powered sensors are outlined and future development directions are proposed, providing a reference for the technological iteration and industrial application of self-powered sensors. Full article

We investigated the impacts of a rapidly changing hydrometeorological regime on coastal erosion in the shallow, seasonally freezing Sea of Azov from 2000 to 2025. Our comparative approach integrated numerical modeling (SWAN), satellite remote sensing, and long-term field observations at two high-erosion sites: the Northern Site in Taganrog Bay and the Southern Site at the open sea boundary. The results demonstrate that coastal erosion is governed by complex, site-specific interactions rather than direct regional climatic trends. A major regime shift characterized by declining fast ice and increasing storm activity during the extended warm season has amplified coastal vulnerability, particularly after 2010. Despite high long-term average erosion rates at both sites, 1.1 to 1.6 m/year in the north and 1.5 to 1.8 m/year in the south, their annual erosion patterns were largely non-synchronous. The Northern Site is controlled by geological structure and surge phenomena, with peak rates reaching 8.5 m/year, while the Southern Site is governed by storm waves and extreme surges, enduring dynamic loads up to 10.0 tf/m². These results provide complex interaction nature of coastal processes and hydrometeorological components and its response to climate change in periodically freezing sea. These findings are vital for improving vulnerability models and underscore the necessity of site-specific hazard assessments for seasonally freezing coasts under a warming climate. Full article

Surface crack detection and temporal evolution analysis are fundamental tasks in remote sensing and photogrammetry, providing critical information for slope stability assessment, infrastructure safety inspection, and long-term geohazard monitoring. However, current unmanned aerial vehicle (UAV)-based crack detection pipelines typically treat spatial detection and temporal change analysis as separate processes, leading to weak geometric consistency across time and limiting the interpretability of crack evolution patterns. To overcome these limitations, we propose the Longitudinal Crack Fitting Network (LCFNet), a unified and physically interpretable framework that achieves, for the first time, integrated time-series crack detection and evolution analysis from UAV remote sensing imagery. At its core, the Longitudinal Crack Fitting Convolution (LCFConv) integrates Fourier-series decomposition with affine Lie group convolution, enabling anisotropic feature representation that preserves equivariance to translation, rotation, and scale. This design effectively captures the elongated and oscillatory morphology of surface cracks while suppressing background interference under complex aerial viewpoints. Beyond detection, a Lie-group-based Temporal Crack Change Detection (LTCCD) module is introduced to perform geometrically consistent matching between bi-temporal UAV images, guided by a partial differential equation (PDE) formulation that models the continuous propagation of surface fractures, providing a bridge between discrete perception and physical dynamics. Extensive experiments on the constructed UAV-Filiform Crack Dataset (10,588 remote sensing images) demonstrate that LCFNet surpasses advanced detection frameworks such as You only look once v12 (YOLOv12), RT-DETR, and RS-Mamba, achieving superior performance (mAP_50:95 = 75.3%, F1 = 85.5%, and CDR = 85.6%) while maintaining real-time inference speed (88.9 FPS). Field deployment on a UAV–IoT monitoring platform further confirms the robustness of LCFNet in multi-temporal remote sensing applications, accurately identifying newly formed and extended cracks under varying illumination and terrain conditions. This work establishes the first end-to-end paradigm that unifies spatial crack detection and temporal evolution modeling in UAV remote sensing, bridging discrete deep learning inference with continuous physical dynamics. The proposed LCFNet provides both algorithmic robustness and physical interpretability, offering a new foundation for intelligent remote sensing-based structural health assessment and high-precision photogrammetric monitoring. Full article

The continuous advancement of modern weaponry has intensified the pursuit of next-generation ballistic protection systems that integrate lightweight architectures, superior flexibility, and high energy absorption efficiency. This review provides a technological overview of current trends in the design, processing, and performance optimization of metallic, ceramic, polymeric, and composite materials for ballistic applications. Particular emphasis is placed on the role of advanced surface coatings and nanostructured interfaces as enabling technologies for improved impact resistance and multifunctionality. Conventional materials such as high-strength steels, alumina, silicon carbide, boron carbide, Kevlar^®, and ultra-high-molecular-weight polyethylene (UHMWPE) continue to dominate the field due to their outstanding mechanical properties; however, their intrinsic limitations have prompted a transition toward nanotechnology-assisted solutions. Functional coatings incorporating nanosilica, graphene and graphene oxide, carbon nanotubes (CNTs), and zinc oxide nanowires (ZnO NWs) have demonstrated significant enhancement in interfacial adhesion, inter-yarn friction, and energy dissipation. Moreover, multifunctional coatings such as CNT- and laser-induced graphene (LIG)-based layers integrate sensing capability, electromagnetic interference (EMI) shielding, and thermal stability, supporting the development of smart and adaptive protection platforms. By combining experimental evidence with computational modeling and materials informatics, this review highlights the technological impact of coating-assisted strategies in the evolution of lightweight, high-performance, and multifunctional ballistic armor systems for defense and civil protection. Full article

Understanding how green spaces foster sense of place is critical for sustainable urban planning and human well-being, yet no comprehensive synthesis has integrated findings across the fragmented literature spanning multiple disciplines. This systematic review analyzed 497 empirical studies examining green space-place attachment relationships, following PRISMA guidelines across three major databases through June 2025. Beyond documenting the field’s rapid growth—from 10 annual publications pre-2010 to over 50 by 2021—this review reveals critical patterns and gaps with implications for theory and practice. While the term ‘place attachment’ was most frequently used (45% of studies), the field employs diverse terminology often without clear definitional boundaries. Only 18% comprehensively addressed the Person-Process-Place tripartite model, with process dimensions particularly neglected. This theoretical incompleteness limits the understanding of how attachments form and evolve. Geographic analysis exposed severe disparities: 78% of studies originated from high-income countries, with Africa (2.4%) and South America (3.6%) critically underrepresented, raising questions about the applicability of current theories beyond Western contexts. Urban settings dominated (49.5%), potentially overlooking rural and indigenous perspectives essential for comprehensive understanding. Methodologically, studies demonstrated sophistication through strategic deployment of quantitative (60%), qualitative (15%), and mixed methods (25%). Key thematic areas, residence duration, restorative benefits, and pro-environmental behaviors, showed promise, yet environmental justice remained underexplored despite its critical importance. This synthesis advances the field by identifying specific pathways for progress: expanding geographic representation to develop culturally inclusive theories, employing longitudinal designs to capture attachment formation processes, developing validated cross-cultural measures, and centering environmental justice in green space planning. These findings provide essential guidance for creating equitable green spaces that foster meaningful human-nature connections across diverse global contexts. Full article

With the advancement of remote sensing technology, high-resolution images are widely used in the field of computer vision. However, image quality is often degraded due to hardware limitations and environmental interference. This paper proposes a Residual Separable Super-Resolution Reconstruction Generative Adversarial Network (RSSRGAN) for remote sensing image super-resolution. The model aims to enhance the resolution and edge information of low-resolution images without hardware improvements. The main contributions include (1) designing an optimized generator network by improving the residual dense network and introducing depthwise separable convolutions to remove BN layers, thereby increasing training efficiency—two PatchGAN discriminators are designed to enhance multi-scale detail capture—and (2) introducing content loss and joint perceptual loss on top of adversarial loss to improve global feature representation. Experimental results show that compared to the widely used SRGAN model in remote sensing (exemplified by the satellite-specific SRGAN in this study), this model improves PSNR by approximately 18.8%, SSIM by 8.0%, reduces MSE by 3.6%, and enhances the PI metric by 13.6%. It effectively enhances object information, color, and brightness in images, making it more suitable for remote sensing image super-resolution. Full article

The growing demand for advanced health-monitoring technologies has intensified the need for early diagnosis of incurable diseases and timely detection of life-threatening conditions. Among various detection modalities, electrochemical sensing has emerged as a particularly promising approach due to its simplicity, cost-effectiveness, high sensitivity, rapid response, ease of miniaturization, and compatibility with portable, wearable, and implantable platforms. The performance of electrochemical sensors is strongly governed by the morphology and physicochemical properties of electrode materials. In this context, MXenes, 2D transition-metal carbides, nitrides, and carbonitrides have attracted increasing attention for sensing applications owing to their high electrical conductivity, large surface area, hydrophilicity, and rich surface chemistry. However, their practical implementation is hindered by oxidation and environmental instability, while surface modification strategies, although improving stability, may compromise intrinsic electrochemical activity and biocompatibility. Notably, MXene-based hybrids consistently demonstrate enhanced sensing performance, underscoring their potential for flexible and wearable electrochemical devices. Despite rapid progress in this field, a comprehensive review addressing the significance of MXene hybrids, their structure–property–performance relationships, and their role in electrochemical detection remains limited. Therefore, this review summarizes recent advances in MXene-based hybrid materials for electrochemical sensing and biosensing of biologically relevant analytes, with an emphasis on design strategies, functional enhancements, and their prospects for next-generation health-monitoring technologies. Full article

Event camera vision systems have recently been gaining traction as swift and agile sensing devices in the field of unmanned aerial vehicles (UAVs). Despite their inherent superior capabilities covering high dynamic range, microsecond-level temporary resolution, and robustness to motion distortion which allow them to capture fast and subtle scene changes that conventional frame-based cameras often miss, their utilization has yet to be widespread. This is due to challenges like insufficient real-world validation, unstandardized simulation platforms, limited hardware integration and a lack of ground truth datasets. This systematic review paper presents an investigation that seeks to explore the dynamic vision sensor christened event camera and its integration to (UAVs). The review synthesized peer-reviewed articles between 2015 and 2025 across five thematic domains, datasets, simulation tools, algorithmic paradigms, application areas and future directions, using the Scopus and Web of Science databases. This review reveals that event cameras outperformed traditional frame-based systems in terms of latency and robustness to motion blur and lighting conditions, enabling reactive and precise UAV control. However, challenges remain in standardizing evaluation metrics, improving hardware integration, and expanding annotated datasets, which are vital for adopting event cameras as reliable components in autonomous UAV systems. Full article

Vegetation segmentation in Very High-Resolution (VHR) satellite imagery has become an essential task for ecological monitoring, supporting diverse applications such as large-scale vegetation conservation and detailed segmentation of small local areas. In particular, multi-class vegetation segmentation, which distinguishes various vegetation types beyond simple binary segmentation of vegetation and non-vegetation, enables detailed analysis of subtle ecosystem changes and has gained increasing importance. However, the annotation of VHR satellite imagery requires extensive time and effort, resulting in a lack of datasets for vegetation segmentation, especially those including multi-class annotations. To address this limitation, this study proposes MultiVeg, a deep learning dataset based on VHR satellite imagery for detailed multi-class vegetation segmentation. MultiVeg includes preprocessed 0.5 m resolution images collected by the KOMPSAT-3 and 3A satellites from 2014 to 2023, covering diverse environments such as urban, agricultural, and forest regions. Each image was carefully annotated by experts into three semantic classes, which are Background, Tree, and Low Vegetation, and validated through a structured quality check process. To verify the effectiveness of MultiVeg, seven representative semantic segmentation models, including convolutional neural network and Transformer-based architectures, were trained and comparatively analyzed. The results demonstrated consistent segmentation performance across all classes, confirming that MultiVeg is a high-quality and reliable dataset for deep learning-based multi-class vegetation segmentation research using VHR satellite imagery. The MultiVeg will be publicly available through GitHub (release v1.0), serving as a valuable resource for advancing deep leaning-based vegetation segmentation research in the remote sensing field. Full article

The operational efficiency and precision of boom sprayers, as critical equipment for protecting field crops, are vital to global food security and agricultural sustainability. In precision agriculture systems, achieving uniform pesticide application fundamentally depends on maintaining stable boom posture during operation. However, severe boom vibration not only directly causes issues like missed spraying, double spraying, and pesticide drift but also represents a critical bottleneck constraining its functional realization in cutting-edge applications. Despite its importance, achieving absolute boom stability is a complex task. Its suspension system design faces a fundamental technical contradiction: effectively isolating high-frequency vehicle vibrations caused by ground surfaces while precisely following large-scale, low-frequency slope variations in the field. This paper systematically traces the evolutionary path of self-balancing boom technology in addressing this core contradiction. First, the paper conducts a dynamic analysis of the root causes of boom instability and the mechanism of its detrimental physical effects on spray quality. This serves as a foundation for the subsequent discussion on technical approaches for boom support and balancing systems. The paper also delves into the evolution of sensing technology, from “single-point height measurement” to “point cloud morphology perception,” and provides a detailed analysis of control strategies from classical PID to modern robust control and artificial intelligence methods. Furthermore, this paper explores the deep integration of this technology with precision agriculture applications, such as variable rate application and autonomous navigation. In conclusion, the paper summarizes the main challenges facing current technology and outlines future development trends, aiming to provide a comprehensive reference for research and development in this field. Full article