Search Results (674)

Reliable estimation of tree-level diameter at breast height (DBH) and stem volume from remote sensing data remains challenging across structurally heterogeneous plantation forests due to cross-site domain shift. This study proposes a structurally site-aware modelling framework designed to mitigate site-induced errors by prioritising training samples structurally proximate to the target site in predictor space. Using unmanned aerial vehicle-based laser scanning (ULS)-derived metrics from 20 geographically independent radiata pine plantation sites in New Zealand, we compared standard pooled workflows with site-aware implementations across multiple feature selection and regression combinations under leave-one-site-out (LOSO) validation. For DBH, the optimal site-aware Elastic Net configuration achieved a mean rRMSE of 16.0% and coefficient of determination (R²) of 0.607, reducing relative error by up to 23.7% compared with the corresponding standard workflow. Gains were more pronounced for stem volume, where the site-aware model achieved a mean rRMSE of 34.5% and R² of 0.648, substantially reducing cross-site errors observed under standard parametric formulations by 85.2% and outperforming a previously published high-dimensional Random Forest benchmark built on the same dataset (mean rRMSE of 35.6% and R² of 0.631). Feature selection patterns revealed that standard workflows converged on a narrow set of universally dominant structural predictors, whereas the site-aware approach redistributed predictor importance across sites, reflecting adaptive alignment to local structural variations. These findings demonstrate that correcting structural domain misalignment can enhance model transferability while maintaining parsimony, offering a scalable solution for operational multi-site forest inventory modelling. Full article

Seafloor massive sulfide (SMS) deposits formed by hydrothermal circulation generate measurable self-potential (SP) anomalies in seawater, providing an effective geophysical indicator of sulfide mineralization. In this study, a remotely operated vehicle (ROV)-borne SP survey was conducted at the Yuhuang hydrothermal field on the Southwest Indian Ridge to investigate the spatial distribution of SMS mineralization. The survey operated at a near-bottom altitude of approximately 10 m, substantially lower than that typically achieved by autonomous underwater vehicles (AUVs) or towed systems, enabling high-resolution data acquisition with improved signal quality. To efficiently discretize complex seafloor topography under irregular data coverage, an adaptive octree mesh was employed, enabling computationally efficient three-dimensional inversion over a large survey area and recovery of the subsurface source current density distribution. The inversion results resolve a main anomaly zone spatially correlated with known SMS mineralization, as well as an additional anomaly zone that was not resolved by previous surveys and suggests potential mineralization. Anomalies associated with known mineralization show good spatial agreement with independent near-bottom observations and drilling results. The results demonstrate that ROV-borne SP surveying combined with adaptive meshing and three-dimensional inversion provides a reliable approach for imaging SMS mineralization in deep-sea environments. Full article

This study has explored the effectiveness of machine learning methods for high-precision spatial interpolation of meteorological variables, aiming to provide accurate atmospheric delay corrections for high-precision edge and corner nets observation in complex-terrain environments such as the Xiluodu Hydropower Station, thereby enhancing the accuracy of deformation monitoring. Considering the significant limitations of traditional interpolation methods such as Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) in capturing spatial variability under complex topographic conditions, we systematically introduced machine learning algorithms including Random Forest (RF)and eXtreme Gradient Boosting (XGBoost, XGB) to compare their performance with traditional methods for high-density interpolation of sparsely distributed temperature, relative humidity, and surface pressure, respectively. Concurrently, we proposed an enhanced XGB model incorporating center-point features (XGB-C) which frames spatial interpolation as a supervised learning problem that learns physical mapping from synoptic backgrounds to local microclimates instead of relying on geometric distances alone. The interpolation performance indices (RMSE, MAE, and R²) were evaluated with daily meteorological observations from 47 stations (38 for training, 9 for testing) during 2023–2024. Results demonstrate that machine learning methods significantly outperform traditional approaches, with XGB-C achieving the highest accuracy (R² ≈ 1.00 for pressure, 0.97 for humidity, 0.83 for temperature). Moreover, the interpolation performance also exhibits a dependence on seasons and the station location. Greater challenges are shown in the summer season and in the “Urban and Built-Up” and “Croplands” areas. These findings highlight the substantial advantages of machine learning, particularly the proposed XGB-C, for meteorological interpolation in mountainous hydropower station environments where accurate atmospheric correction is crucial for deformation monitoring. This also lays a solid foundation for developing operational ML-based interpolation models trained with high-quality labels derived from unmanned aerial vehicle (UAV) remote sensing data. Full article

Remotely operated underwater vehicles (ROVs) play a significant role in the domain of underwater robotics, as observed in the field of deep-sea aquaculture. However, conventional stationary suction-tube underwater collection robots often struggle to efficiently collect target organisms located within complex reef environments. To address this limitation, this paper proposes an underwater object suction robot with a variable flexible tube. For vision-based object recognition tasks, stable vehicle motion is essential, as hydrodynamic disturbances can significantly degrade visual accuracy. Therefore, a systematic numerical investigation is conducted into the hydrodynamic characteristics of the ROV under different suction-tube shapes. Computational fluid dynamics (CFD) simulations are used to evaluate the resistance acting on the vehicle. The results provide guidance for motion control strategies aimed at reducing disturbance effects and improving the robustness of underwater robotic vision. Full article

Medical drone delivery (MDD), defined as the use of uncrewed aerial vehicles to transport medical products, is an emerging technological innovation responding to persistent health supply chain challenges in rural and low-resource settings. Within sub-Saharan Africa, MDD systems have demonstrated large-scale success in improving key health outcomes, health supply chain efficiency, and reductions in medical product stockouts and wastage. However, the existing evidence base on the effectiveness of this technology is dominated by quantitative, performance-based evaluations, with limited emphasis on the community-driven mechanisms that shape such outcomes. Drawing on original qualitative research, this article presents a qualitative secondary analysis (QSA) of interview data collected as part of a larger case study on MDD in Madagascar. The QSA, guided by socio-technical systems theory, analyzes a subset of 18 interviews with 23 community-level stakeholders to understand how MDD affects healthcare services in remote regions of the country. Participants reported that MDD led to downstream healthcare improvements in vaccination coverage and malaria-related health outcomes. These improvements were enabled through four interconnected socio-technical mechanisms: (1) improved medical product availability through the mitigation of geographic and transportation barriers, (2) stabilization of vaccine and cold chain transportation, (3) building trust and healthcare-seeking behaviours through predictable service delivery, and (4) reduced physical, mental, and financial burdens experienced by healthcare workers. A final, cross-cutting theme emphasized was the criticality of MDD program continuity, with participants noting that operation disruptions or withdrawals risked reversing benefits and breaking communities’ trust in the health system. By centering lived realities, perceptions, and social processes, this article bridges the gap between predominantly quantitative evidence on MDD systems and the experiences of the communities they are intended to serve. Full article

Artificial intelligence (AI)-enabled unmanned aerial vehicles (UAVs) are rapidly emerging as transformative technologies for sustainable healthcare logistics, particularly in remote and infrastructure-constrained regions. Despite growing implementation, the environmental, social, and governance (ESG) implications of these systems remain insufficiently synthesized in the literature. This study conducts a PRISMA-ScR-guided Systematic Review of 37 peer-reviewed studies selected from 333 records across six major scientific databases (2015–2026). The analysis reveals a sharp acceleration of research after 2021, with over 80% of publications produced between 2021 and 2024, indicating increasing global interest in AI-supported autonomous medical logistics. Evidence demonstrates that AI-enabled drones can substantially reduce delivery times; expand access to blood, vaccines, and essential medicines; and enhance emergency response capacity in rural and disaster-affected environments. From a sustainability perspective, AI-driven route optimization and autonomous navigation may reduce transport-related emissions, supporting climate-responsive healthcare supply chains. However, large-scale deployment remains constrained by regulatory fragmentation, cybersecurity risks, operational limitations, and challenges with social acceptance. This review proposes an ESG-oriented framework linking technological innovation, ethical governance, and equitable healthcare access while identifying key research gaps in lifecycle sustainability assessment, cost-effectiveness modeling, and real-world implementation aligned with the Sustainable Development Goals (SDGs). Full article

Autonomous systems are expanding rapidly, driving a demand for robust perception technologies capable of navigating challenging, unstructured environments. While urban autonomy has made significant progress, off-road environments pose unique challenges, including dynamic terrain and limited communication infrastructure. This research addresses these challenges by introducing a novel three-part solution for off-road autonomous vehicles. First, we present a large-scale off-road dataset curated to capture the visual complexity and variability of unstructured environments, providing a realistic training ground that supports improved model generalization. Second, we propose a Confusion-Aware Loss (CAL) that dynamically penalizes systematic misclassifications based on class-level confusion statistics. When combined with cross-entropy, CAL improves segmentation mean Intersection over Union (mIoU) on the off-road test set from 68.66% to 70.06% and achieves cross-domain gains of up to ~0.49% mIoU on the Cityscapes dataset. Third, leveraging semantic segmentation as an intermediate representation, we introduce a spatial overlay video encoding scheme that preserves high-fidelity RGB information in semantically critical regions while compressing non-essential background regions. Experimental results demonstrate Peak Signal-to-Noise Ratio (PSNR) improvements of up to +5 dB and Video Multi-Method Assessment Fusion (VMAF) gains of up to +40 points under lossy compression, enabling efficient and reliable off-road autonomous operation. This integrated approach provides a robust framework for real-time remote operation in bandwidth-constrained environments. Full article

This paper presents the design of a sensor system for remote measurements of exhaust emissions from automotive combustion engines. The system’s purpose is to determine the likelihood of a given vehicle’s potential harmfulness to the environment. This system, if implemented, could detect vehicles posing a threat to the environment in road traffic. A remote measurement system can be installed in the front of a measuring vehicle driving behind the vehicle being diagnosed. This approach allows for rapid road testing of multiple vehicles while they are operating in real-world conditions where engines can emit the highest levels of undesirable pollutants. Exceeding emission standards may be related to modifications made to the vehicle’s exhaust gas aftertreatment systems, engine wear, or malfunctions of engine-related systems such as the diesel particulate filter (DPF) or catalytic converter. Toxic and undesirable substances include carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO_x), carbon dioxide (${\mathrm{C}\mathrm{O}}_2$), and particulate matter (PM) particles. The main goal of the measurements is to identify vehicles that potentially pose a threat to the environment during normal operation. The sensor system consists of several types of sensors utilizing various physical and chemical phenomena, with particular emphasis on their low cost and easy availability. The measurement unit utilizes MEMS technology, photoacoustic spectroscopy, electrochemical methods, light absorption and scattering, spectrophotometry, and electro-optical detection. Full article

Accurate mapping of grass species in biodiverse ecosystems, such as wetlands, is critical for ecological protection. Rapid advancements in remote sensing have established satellite data as a critical tool for wetland grass species mapping; however, its relatively coarse spatial resolution and susceptibility to cloud contamination limit the distinction of co-occurring species at fine scales. While Unmanned Aerial Vehicle (UAV) remote sensing offers high resolution and operational flexibility, relying on single-source features is often insufficient for fine-scale wetland species mapping due to the spectral similarity of co-occurring species. On the other hand, the fusion of multi-source remote sensing features (i.e., spectral, textural, and geometric features) likely provides a promising solution for achieving accurate, fine-scale grass species mapping in biodiverse ecosystems. In this study, we developed a wetland grass species mapping framework integrating spectral, textural, and geometric features derived from UAV RGB and multispectral imagery. Using a dataset of 95,880 image objects representing 24 wetland grass species classes collected in two years in Dajiu Lake National Wetland Park of China, we evaluated three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—across various feature combinations. We found that while spectral features (i.e., red edge, normalized green–red difference index [NGRDI], and normalized difference vegetation index [NDVI]) (related to leaf pigment concentrations and cellular structures) exhibited the highest importance in wetland grass species mapping, textural (i.e., contrast) and geometric features (i.e., aspect ratio) significantly enhanced classification performance as complementary information, yielding improvements of up to 10.5% in overall accuracy (OA) and 0.103 in Macro-F1 scores. Specifically, the fusion of spectral, textural, and geometric features achieved optimal performance with an OA of 81.9% and a Macro-F1 of 0.807. Furthermore, the XGBoost model outperformed SVM and RF, improving OA by 9.4% and 2.8%, and Macro-F1 by 0.08 and 0.035, respectively. By identifying the optimal feature combination and machine learning algorithm, this study establishes an accurate method for wetland grass species mapping, offering new opportunities for ecological assessment and precision conservation in biodiverse landscapes. Full article

Polarimetric information is essential for scattering interpretation and target characterization in synthetic aperture radar (SAR) remote sensing, yet many resource-constrained platforms (e.g., small satellites and unmanned aerial vehicles (UAVs)) operate with limited polarization modes or even a single radio frequency (RF) chain, which limits full polarimetric scattering acquisition. To address this limitation, this paper proposes a single-channel framework for estimating the full polarization scattering matrix (PSM) enabled by time-varying polarization modulation. The transmit/receive polarization states are steered along predefined trajectories on the Poincaré sphere to generate time-varying polarization tags that are encoded into the received echoes through the target’s polarization-varying response. A compact observation model is then derived to relate the single-channel echoes, the known polarization tags, and the unknown PSM; based on this, the PSM is then estimated via a least squares formulation with a low-rank approximation. Simulation results demonstrate the robust reconstruction of the full polarimetric scattering matrix under diverse modulation trajectories. For arbitrarily chosen random point targets, when the signal-to-noise ratio (SNR) exceeds −20 dB, the polarimetric similarity coefficient approaches 1, and the estimation errors of Pauli power components converge toward zero. Furthermore, the method’s reliability is validated on distributed vegetation clutter. Quantitative metrics demonstrate near-perfect statistical consistency, with polarimetric entropy and alpha angle errors within 0.14%. Overall, the proposed approach provides a practical pathway to enhance the availability of full polarimetric scattering information under limited-observation conditions, confirming its feasibility for downstream analysis in complex natural scenes while maintaining a single radio frequency (RF) chain architecture augmented by a polarization modulator. Full article

Pre-warehouse last-mile delivery is currently constrained by service radiuses and intense delivery pressures. Meanwhile, national policies are increasingly promoting a transition toward green logistics. By undertaking deliveries to remote or dispersed locations, UAVs can streamline truck routes and minimize the fuel consumption and emissions typically exacerbated by urban traffic congestion. Accordingly, this paper establishes a Ground-Vehicle and Drone Parallel Distribution Model with Priorities (PW-PDSVRP-P), quantifying customer priorities via delivery delay functions to align efficiency with social service requirements. A master–slave hybrid Large Neighborhood Search algorithm is developed and validated through a Hema Fresh case study in Xuzhou. Results define a clear “economic advantage zone” for drone adoption and reveal an adaptive assignment strategy: drones serve as mass-delivery tools in low-cost scenarios but act as “surgical tools” to prune inefficient truck segments in high-cost environments. These findings confirm that air–ground collaboration fosters a more resilient urban distribution system by balancing operational costs with environmental and social sustainability goals. Full article

Sediment management is defined as the strategic monitoring and control of erosion, transport, and deposition processes to maintain environmental and infrastructural stability. Terrestrial laser scanning (TLS) has emerged as a critical high-precision technology for monitoring sediment dynamics, erosion processes, and geomorphic change detection across diverse environments, including riverine, coastal, watershed, and infrastructure-related landscapes. While the field of TLS technology has seen significant advancements in recent years, including improvements in data accuracy, enhanced operational performance, artificial intelligence (AI), machine learning-based processing, and integration with other remote sensing tools such as unmanned aerial vehicles (UAVs) and satellite light detection and ranging (LiDAR), the study has focused on these developments. These advancements have further extended the application prospects of TLS technology. Despite these advancements, there remains a crucial need to systematically identify global research trends to identify the effectiveness, limitations, and knowledge gaps of TLS in sediment management. The methodological advantages and challenges of TLS applications provide insights into its gradual development role in enhancing sediment monitoring and environmental resilience. The objective of this study is to synthesize the current state of sediment management by conducting a systematic review of 108 peer-reviewed research papers retrieved from academic databases, including Google Scholar, ResearchGate, ScienceDirect, Scopus, and Web of Science, from 28 countries, published between 2000 and 2025. The study will evaluate the effectiveness of TLS methodologies in comparison to conventional techniques and management procedures, following the PRISMA 2020 guidelines. It will examine their capacity to enhance measurement accuracy, reduce error margins, and improve structural guidelines, particularly by advancing TLS technology through the integration of AI and machine learning (ML) algorithms. The findings of the study indicate that TLS and Iterative Closest Point (ICP) techniques can enhance the analysis of 3D models of dam deformation, ensuring improved structural monitoring and safety. The findings offer insights into the evolving role of TLS in sediment monitoring, emphasizing its potential for enhancing environmental management and climate resilience strategies. Furthermore, this review identifies future research directions to optimize TLS applications in sediment management through interdisciplinary approaches. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

Remotely operated vehicles (ROVs) equipped with multibeam forward-looking sonar are widely used for underwater object search in environments where visibility is limited. Ensuring complete three-dimensional (3D) scan coverage within a bounded mission duration remains a challenging planning problem due to sonar beam geometry and vehicle motion constraints. This study presents a deterministic, geometry-driven framework for volumetric path planning of ROV-based forward-looking sonar scanning in predefined circular and rectangular underwater volumes. The proposed approach constructs layered planar scan trajectories by explicitly incorporating sonar detection range, horizontal and vertical beamwidths, and scan volume geometry. Mission duration is analytically estimated from path length and vehicle kinematic parameters, enabling systematic comparison among multiple planning strategies. To support qualitative interpretation of scan effectiveness, a distance-based target position certainty metric is introduced and combined with the active sonar equation to estimate likely target locations within the scanned volume. Simulation results under idealized sensing and motion assumptions demonstrate that the corrected zigzag pattern for rectangular scan areas, as well as the corrected zigzag-II and corrected arithmetic spiral-III patterns for circular scan areas, achieve complete volumetric coverage with bounded mission duration and consistent localization performance. The proposed framework provides a transparent analytical baseline for evaluating volumetric scan path planning strategies for forward-looking sonar–equipped ROVs. Full article

This paper proposes a hybrid underwater drone communication system that combines underwater optical communication (UOC) and radio-frequency (RF) communication to support real-time video streaming in underwater environments. The system consists of a remotely operated vehicle (ROV) that transmits video to a surface gateway, which relays the video to onshore facilities through a 5G network. An outdoor experiment conducted in a maritime environment measured the received signal strength indicator (RSSI), signal-to-interference-plus-noise ratio (SINR), occupied bandwidth, and end-to-end (E2E) throughput at 700 MHz and 2600 MHz with video frame rates ranging from 10 to 60 fps. The results show that the 700 MHz frequency band provides higher RSSI and SINR, which support more reliable long-range communications, while the 2600 MHz frequency band provides lower RSSI and SINR but a larger bandwidth. The maximum E2E throughput achieved was 53.5 Mbps at 700 MHz and 58.64 Mbps at 2600 MHz. Increasing frame rates mainly affects throughput by reducing SINR. These results analyze the coverage–capacity trade-off and provide valuable insights for drone-assisted hybrid UOC-RF communication in underwater video streaming applications. Full article