Search Results (588)

To address the challenges of low detection accuracy and weak generalization in UAV aerial imagery caused by complex ground environments, significant scale variations among targets, dense small objects, and background interference, this paper proposes an improved lightweight multi-scale small-object detection model, MBD-YOLO (MBFF module, BiMS-FPN, and Dual-Stream Head). Specifically, to enhance multi-scale feature extraction capabilities, we introduce the Multi-Branch Feature Fusion (MBFF) module, which dynamically adjusts receptive fields through parallel branches and adaptive depthwise convolutions, expanding the receptive field while preserving detail perception. We further design a lightweight Bidirectional Multi-Scale Feature Aggregation Pyramid Network (BiMS-FPN), integrating bidirectional propagation paths and a Multi-Scale Feature Aggregation (MSFA) module to mitigate feature spatial misalignment and improve small-target detection. Additionally, the Dual-Stream Head with NMS-free architecture leverages a task-aligned architecture and dynamic matching strategies to boost inference speed without compromising accuracy. Experiments on the VisDrone2019 dataset demonstrate that MBD-YOLO-n surpasses YOLOv8n by 6.3% in mAP50 and 8.2% in mAP50–95, with accuracy gains of 17.96–55.56% for several small-target categories, while increasing parameters by merely 3.1%. Moreover, MBD-YOLO-s achieves superior detection accuracy, efficiency, and generalization with only 12.1 million parameters, outperforming state-of-the-art models and proving suitable for resource-constrained embedded deployment scenarios. The superior performance of MBD-YOLO, which harmonizes high precision with low computational demand, fulfills the critical requirements for real-time deployment on resource-limited UAVs, showing great promise for applications in traffic monitoring, urban security, and agricultural surveying. Full article

Coal mining disturbances in semi-arid grasslands affect land surface phenology (LSP), impacting ecosystem functions, restoration target setting, and carbon sequestration; however, the magnitude and spatial extent of these disturbances and their detectability across vegetation indices (VIs), remain insufficiently constrained. We developed and applied a streamlined quantitative framework to delineate the extent and intensity of mining-induced phenological disturbance and to compare the sensitivity and stability of commonly used VIs. Using Harmonized Landsat Sentinel (HLS) surface reflectance data over the Yimin mine, we reconstructed multitemporal VI trajectories and derived phenological metrics; directional phenology gradients were used to delineate disturbance, and VI responsiveness was evaluated via mean difference (MD) and standard deviation (SD) between affected and control areas. Research findings indicate that the impact of mining extends to an area approximately four times the size of the mining site, with the start of season (SOS) in affected areas occurring about 10 days later than in unaffected areas. Responses varied markedly among VIs, with the Modified Soil-Adjusted Vegetation Index (MSAVI) exhibiting the highest spectral stability under disturbance. This framework yields an information-rich quantification of phenological impacts attributable to mining and provides operational guidance for index selection and the prioritization of restoration and environmental management in semi-arid mining landscapes. Full article

Spatial planning deals with the organization and regulation of space with the goal to improve the quality of life of its inhabitants. Spatial planning plays a vital role in land administration, encompassing land development, management, land use assessment, resource allocation, and environmental protection. The significance of integrating spatial-planning information into the ISO 19152 Land Administration Domain Model (LADM) framework has been recognized in the LADM second edition, Part 5, where a part for spatial plan information is introduced. The aim of this paper is to analyze the applicability of the LADM Part 5: Spatial Plan Information draft international standard to the Serbian spatial and urban planning system and to develop a country profile for Serbia in alignment with Serbian laws and regulations. An analysis of spatial and urban planning in Serbia will be performed, determining the hierarchy of spatial and urban plans based on an analysis of laws on spatial planning. The created conceptual model for spatial planning for Serbia based on the LADM Part 5: Spatial Plan Information will be harmonized with the previously created LADM country profile for Serbia. Full article

The large lakes and reservoirs of the northeastern Tibetan Plateau play a key role in regional water resources, yet their influence on terrestrial water storage (TWS) changes at different spatial scales remains unclear. This study employed the constrained forward modeling (CFM) method to correct leakage errors in level-2 spherical harmonic (SH) coefficients from the Gravity Recovery and Climate Experiment and its follow-on missions (GRACE/GRACE-FO) at three spatial scales: two circular regions covering 90,000 km² and 200,000 km², respectively, and a 220,000 km² region based on the shape of mass concentration (Mascon). TWS changes derived from SH solutions after leakage correction through CFM were compared with level-3 Mascon solutions. Individual water storage components, including lake and reservoir water storage (LRWS), groundwater storage (GWS), and soil moisture storage (SMS), were quantified, and their relationships with precipitation were assessed. From 2003 to 2022, the CFM method effectively mitigated signal leakage, revealing an overall upward trend in TWS at all spatial scales. Signals from Qinghai Lake and Longyangxia Reservoir dominated the long-term trend and amplitude variations of LRWS, respectively. LRWS explained more than 47% of the TWS changes, and together with GWS, accounted for over 85% of the changes. Both CFM-based and Mascon-based TWS changes indicated a consistent upward trend from January 2003 to September 2012, followed by declines from November 2012 to May 2017 and October 2018 to December 2022. During the decline phases, GWS contributions increased, while LRWS contributions and component exchange intensity decreased. LRWS, SMS, and TWS changes were significantly correlated with precipitation, with varying time lags. These findings underscore the value of GRACE/GRACE-FO data for monitoring multiscale TWS dynamics and their climatic drivers in lake- and reservoir-dominated regions. Full article

The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-based observations—has significantly expanded the spatial and temporal scales at which watershed processes can be analyzed. Concurrently, advances in model coupling strategies, ranging from loose to embedded architectures, have enabled more dynamic and holistic representations of interactions among hydrology, water quality, and ecological systems. However, a unifying operational framework that links multi-source data, cross-scale coupling, and rigorous uncertainty propagation to actionable, real-time decision support is still missing, largely due to gaps in interoperability and stakeholder engagement. Addressing these limitations demands the development of intelligent, adaptive modeling frameworks that leverage hybrid physics-informed machine learning, cross-scale process integration, and continuous real-time data assimilation. Open science practices and transparent model governance are essential for ensuring reproducibility, stakeholder trust, and policy relevance. The recent literature indicates that loose coupling predominates, physics-informed ML tends to generalize better in data-sparse settings, and uncertainty communication remains uneven. Building on these insights, this review synthesizes methods for data harmonization and cross-scale integration, compares coupling architectures and data assimilation schemes, evaluates uncertainty and interoperability practices, and introduces the Smart Integrated Watershed Eco-Assessment Framework (SIWEAF) to support adaptive, real-time, stakeholder-centered decision-making. Full article

Urban areas increasingly face heat-related climate risks, necessitating targeted, nature-based interventions such as tree planting to improve resilience, livability, and public health. This study presents a data-driven workflow to identify urban tree planting potential (TPP) in the city of Dortmund, Germany. The approach integrates high-resolution spatial datasets capturing land cover, shading, thermal comfort, population density, and critical infrastructure. All variables were harmonized within a 50 m hexagonal grid, normalized, and combined into a composite TPP score using weighting schemes informed by expert judgment and sensitivity testing. Spatial and non-spatial clustering were applied to group urban areas by shared characteristics, and a connectivity analysis evaluated the spatial coherence of high-potential cells and their relationship to existing green infrastructure. The findings demonstrate the potential to strengthen urban green infrastructure and guide coordinated planting strategies while addressing both ecological and social priorities. The presented workflow offers a flexible, transferable tool to support municipalities in prioritizing effective greening interventions and integrating climate adaptation objectives into urban development planning. Full article

Assessing the variation in the thermal conductivity of heterogeneous rock materials can be critical when upscaling models to simulate geothermal system operation, especially for petrothermal systems, where conduction dominates over convection. This study’s objective was to evaluate heterogeneity effects when assessing the thermal conductivity of geological materials, in this case, metamorphic rocks from Kuujjuaq (Canada), where the installation of a ground-coupled heat pump system is expected. Four core samples of gneissic rocks were analyzed in detail and compared to results obtained from a thermal response test. Thermal conductivity measurements in dry conditions were performed on the cylindrical surface of the samples with an optical thermal conductivity scanner. The 2D thermal conductivity spatial distribution was obtained by linear interpolation and used for numerical modeling to simulate steady-state conductive heat transfer along the sample vertical direction. Then, the effective thermal conductivity was computed according to Fourier’s law, using the simulated temperature to investigate the effect of scale variation with the heterogeneity. Results indicate the importance of distinguishing between the sample section’s effective thermal conductivity and local average thermal conductivity. Significant scale effects were identified with a variation ratio comprised between −10% and +16% when varying the length of the sample section. The representative elementary volume for the effective thermal conductivity was determined equivalent to half of the sample length. This volume gave a thermal conductivity that is equal to the harmonic mean of the laboratory-assessed values with a relative error <5%. A comparison between the in situ and laboratory-assessed thermal conductivity indicates that the thermal conductivity inferred from the thermal response test is adequate for sizing a geothermal system, assuming a range of variability equivalent to 1.5 times its standard deviation. Full article

Historic districts face persistent challenges balancing heritage preservation and urban vitality due to fragmented governance and static conservation. This study develops a multi-source data-driven evaluation system coupling spatial quality and urban vitality, focusing on China’s Republican-era historic districts with Nanjing’s Yihe Road as a case study. Integrating field surveys and big data (street view imagery, POI data, heatmaps), we quantitatively assess environmental quality and vitality. Key findings reveal a distinct spatial pattern: “high-quality concentration internally” and “high-vitality concentration externally,” where core areas exhibit functional homogenization and low vitality, while peripheries show high pedestrian activity but lack spatial coherence. Clustering analysis categorizes streets into four types based on quality and vitality levels, highlighting contradictions between static conservation and adaptive reuse. The study deepens understanding of spatial differentiation mechanisms and reveals universal patterns for sustainable development strategies. A multi-tiered governance strategy is proposed: urban-level flexible governance harmonizes cross-departmental policies via adaptive planning, district-level differentiated governance activates spatial value through functional reorganization, and street-level fine-grained management prioritizes incremental micro-renewal. The research underscores the critical need to balance heritage preservation with contemporary functional demands during urban renewal, offering a practical framework to resolve spatial conflicts and reconcile conservation with regeneration. Full article

Tidal wetlands are essential for coastal resilience, biodiversity, and carbon storage; yet, many are increasingly vulnerable to sea-level rise due to insufficient sediment supply. This study presents a national-scale, GIS-based model that quantifies riverine inorganic sediment contributions to tidal wetland accretion across over 700,000 coastal catchments in the contiguous United States. By integrating datasets from USGS, NOAA, and USFWS, the model calculates sediment yield, thickness, and accretion balance, enabling comparison with current sea-level rise projections. Results reveal significant regional disparities: the Northeast and Midwest exhibit higher sediment accumulation, while the Pacific and Southeast show widespread sediment deficits. Spatial statistical analyses identified clusters of high and low sediment supply, highlighting areas of resilience and vulnerability. A total of 93 field sites confirmed the model’s ability to distinguish between riverine-dominated and mixed-source sedimentation regimes. These findings underscore the importance of riverine sediment in sustaining wetland elevation and inform where non-riverine sources may be critical. The model’s outputs have been shared with coastal planners and stakeholders to support local decision-making, conservation prioritization, and adaptation strategies. This work demonstrates both the challenges and fruitfulness of harmonizing disparate national datasets into a unified framework for assessing wetland vulnerability and provides a scalable tool for guiding coastal resilience planning in the face of accelerating sea-level rise. Full article

In this paper, in order to effectively reduce the electromagnetic noise of automotive electronic water pumps, a Hybrid Random Carrier Space Vector Pulse Width Modulation Hybrid Random Carrier Space Vector Pulse Width Modulation, (HRCSVPWM) technique based on linear congruential generator (LCG) algorithm is proposed to study the suppression effect of current harmonics and acoustic vibration response with an automotive electronic water pump as the research object. Firstly, the HRCSVPWM based technique is proposed on the basis of SVPWM and pulse width modulation strategies. Secondly, the performance of random numbers generated for HRCSVPWM is analyzed, and it is proposed to use an LCG random number generator to generate excellent random numbers combined with a genetic algorithm to quickly determine the optimal values of three random parameters, namely, random number Ri, mixing degree coefficient Ki, and spreading width Ti, which enhances the stochasticity and spatial traversal of random sequences and ensures the effect of the HRSVPWM control method. Finally, simulation analysis is carried out, and a noise experimental platform is built for experimental verification. The results show that using the improved HRCSVPWM control strategy, compared with the SVPWM control strategy, the total harmonic content decreased by close to 21.81%, and the sound pressure level amplitude decreased by an average of approximately 6 dB. Full article

Harmonizing economic growth and carbon emissions is key to reaching the “dual carbon” targets. This research centers on the seven key urban agglomerations within the Yellow River Basin (YRB) and establishes an integrated research framework of decoupling effect quantification–spatial association recognition–driving factor analysis. By combining the Tapio decoupling model, a modified gravity model, social network analysis (SNA), and the Logarithmic Mean Divisia Index (LMDI) method, the study systematically evaluates the decoupling states, spatial association structure, and driving mechanisms between regional carbon emissions and economic growth from 2001 to 2020. The results show that: (1) All seven urban agglomerations exhibit a simultaneous upward trend in both carbon emissions and GDP, but significant regional disparities exist, with some agglomerations demonstrating a green growth pattern where economic growth outpaces carbon emissions. (2) Weak decoupling is the predominant type among urban agglomerations and their constituent cities in the YRB. Notably, some regions have regressed to growing connection or growing negative decoupling during 2016–2020. (3) The spatial network of carbon emission decoupling effects exhibits a core-periphery structure characterized by stronger eastern regions and weaker western regions, with the Shandong Peninsula and Guanzhong Plain urban agglomerations serving as core nodes for regional linkage. (4) Per capita GDP and technological level play a dominant role in promoting decoupling, while energy intensity and the population carrying intensity of the real economy are the primary inhibiting factors; the impact of industrial structure shows an unstable direction. Grounded in these findings, this study formulates differentiated carbon reduction pathways tailored to regional heterogeneity, providing theoretical insights and actionable guidance to facilitate the low-carbon transition and coordinated governance of urban agglomerations. Full article

High-order harmonic generation with mid-infrared laser fields has been considered the most promising method to produce soft X-rays attosecond pulses, which provides an important tool for probing the ultrafast electronic dynamics in atoms, molecules, and solids in real time. However, quantum diffusion of the electron wavepacket can lead to a dramatic drop of the harmonic yield when a mid-infrared laser field is used. Here we theoretically demonstrate that a spatially structured (SS) laser field can suppress quantum diffusion of the electron wavepacket and lead to a significant enhancement of high-order harmonic generation, compared with those generated by the spatially homogeneous (SH) laser field. The SS laser field is inhomogeneous in transverse direction perpendicular to the laser polarization and homogeneous in the polarization direction of the laser field. The electric field presents a valley structure. It is found that this valley structure can confine the electron wavepacket around the parent ion, prevent the electron wavepacket spreading, and finally lead to the significant enhancement of the high-order harmonics. Our results provide a novel method for controlling the ultrafast electron wavepacket dynamics of HHG. Full article

Electroencephalography (EEG) is a non-invasive biosensing platform with a spatial-frequency content that is of significant relevance for a multitude of aspects in the neurosciences, ranging from optimal spatial sampling of the EEG to the design of spatial filters and source reconstruction. In the past, simplified spherical head models had to be used for this analysis. We propose a method for spatial frequency analysis in EEG for realistically shaped volume conductors, and we exemplify our method with a five-compartment Boundary Element Method (BEM) model of the head. We employ the recently developed technique for spatial harmonic analysis (Sphara), which allows for spatial Fourier analysis on arbitrarily shaped surfaces in space. We first validate and compare Sphara with the established method for spatial Fourier analysis on spherical surfaces, discrete spherical harmonics, using a spherical volume conductor. We provide uncertainty limits for Sphara. We derive relationships between the signal-to-noise ratio (SNR) and the required spatial sampling of the EEG. Our results demonstrate that conventional 10–20 sampling might misestimate EEG power by up to 50%, and even 64 electrodes might misestimate EEG power by up to 15%. Our results also provide insights into the targeting problem of transcranial electric stimulation. Full article

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article

The compaction quality of soil–rock mixture (SRM) subgrades critically influences infrastructure stability, but conventional settlement difference methods exhibit high spatial sampling bias (error > 15% in heterogeneous zones) and fail to characterize the overall compaction quality. These limitations lead to under-compaction (porosity > 25%) or over-compaction (aggregate fragmentation rate > 40%), highlighting the need for real-time monitoring. This study develops an intelligent compaction system integrating (1) vibration acceleration sensors (PCB 356A16, ±50 g range) for compaction meter value (CMV) acquisition; (2) near-infrared (NIR) moisture meters (NDC CM710E, 1300–2500 nm wavelength) for real-time moisture monitoring (sampling rate 10 Hz); and (3) an embedded edge-computing module (NVIDIA Jetson Nano) for Python-based data fusion (FFT harmonic analysis + moisture correction) with 50 ms processing latency. Field validation on Linlin Expressway shows that the system meets JTG 3430-2020 standards, with the compaction qualification rate reaching 98% (vs. 82% for conventional methods) and 97.6% anomaly detection accuracy. This is the first system integrating NIR moisture correction (R² = 0.96 vs. oven-drying) with CMV harmonic analysis, reducing measurement error by 40% compared to conventional ICT (Bomag ECO Plus). It provides a digital solution for SRM subgrade quality control, enhancing construction efficiency and durability. Full article