Search Results (208)

This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article

The dynamic vision sensor (DVS), or event camera, is emerging as a successful sensing solution for many application fields. While state-of-the-art datasets for event-based vision are well-structured and suitable for the designed goals, they often rely on simulated data or are recorded in loosely controlled conditions, thereby making it challenging to understand the sensor response to varying camera parameters and illumination conditions. To address this knowledge gap, this work introduces the JRC INVISIONS Neuromorphic Sensors Parametric Tests dataset, an extensive collection of event-based data specifically acquired in controlled scenarios that systematically vary bias settings and environmental factors, enabling rigorous evaluation of sensor performance, robustness, and artifacts under realistic conditions that existing datasets lack. The dataset is composed of 2156 scenes recorded with two different off-the-shelf event cameras, eventually paired with a frame camera across three different controlled scenarios: moving targets, mechanical vibrations, and rotation speed estimation; the inclusion of ground truth enables the evaluation of standard computer vision tasks. The proposed manuscript is complemented by an experimental analysis of sensor performance under varying speeds and illumination, event statistics, and acquisition artifacts such as event loss and motion-induced distortions due to line-based readout. The dataset is publicly available and, to the best of our knowledge, represents the first dataset of its kind in the literature, providing a valuable resource for the research community to advance the development of event-based vision systems and applications. Full article

Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a novel multi-information assisted Joint Detection and Tracking (JDT) framework for ground moving targets. This study enhances detection and tracking performance by integrating multi-source information, specifically echo information, road network data, and velocity limits, enabling bidirectional data exchange between the detector and tracker for multiple ground targets. An adaptive threshold detector is developed by incorporating a priori information and tracker feedback. Additionally, we innovatively propose an improved Variable Structure Interacting Multiple Model (VS-IMM) filter that leverages road network constraints and detector outputs for tracking, featuring an enhanced model probability calculation to significantly reduce computational time. Simulation results demonstrate that the proposed method significantly improves data association accuracy and tracking precision. Full article

The Hippocratic principle primum non nocere, or “first, do no harm”, serves as a vital lens through which to re-evaluate modern oncology practices. While recent advances such as immunotherapy, targeted agents, and precision medicine have transformed cancer care, these treatments are not without risk. Even with improved tolerability, they may still lead to substantial toxicities, particularly in frail patients with advanced cancer. The pursuit of survival often overshadows the patient’s quality of life, with aggressive interventions frequently continuing beyond the point of meaningful benefit. This perspective article argues for a more individualized and ethically grounded approach to cancer treatment, emphasizing the careful assessment of each patient’s clinical status, values, and goals. By integrating geriatric and palliative assessments, improving shared decision making, and moving away from a default treatment-at-all-costs mindset, clinicians can better align care with what truly matters to patients. Honoring primum non nocere in oncology means not only extending life when appropriate but ensuring that life remains worth living. Full article

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground-moving target indication (GMTI) radar systems has received great interest. However, because of size, weight, and power (SWaP) limitations, the UAV may not be able to equip a highly accurate inertial navigation system (INS), which leads to reduced accuracy in the moving target relocation. To solve this issue, we propose using an image registration algorithm, which matches a Doppler beam sharpening (DBS) image of detected moving targets to a synthetic aperture radar (SAR) image containing coordinate information. However, when using conventional SAR image registration algorithms such as the SAR scale-invariant feature transform (SIFT) algorithm, additional difficulties arise. To overcome these difficulties, we developed a new image-matching algorithm, which first estimates the errors of the UAV platform to compensate for geometric distortions in the DBS image. In addition, to showcase the relocation improvement achieved with the new algorithm, we compared it with the affine transformation and second-order polynomial algorithms. The findings of simulated and real-world experiments demonstrate that our proposed image transformation method offers better moving target relocation results under low-accuracy INS conditions. Full article

Micro-Doppler signatures play a crucial role in capturing target features for the radar classification task, and the time–frequency distribution method is widely used to represent micro-Doppler signatures in many applications including human activities, ground moving target identification, and different types of drones distinguishing. However, most existing studies that utilize radar micro-Doppler spectrograms often require extended observation times to effectively represent the cyclostationarity and periodic modulation of radar signals to achieve promising classification results. In addition, the presence of noise in real-world environments poses challenges by generating weak micro-Doppler features and a low signal-to-noise ratio (SNR), leading to a significant decline in classification accuracy. In this paper, we present a novel one-dimensional (1D) denoising and classification cascaded framework designed for low-resolution radar targets using a micro-Doppler spectrum. This framework provides an effective signal-based solution for feature extraction and recognition from the single-frame micro-Doppler spectrum in a conventional pulsed radar system, which boasts high real-time efficiency and low computation requirements under conditions of low resolution and a short dwell time. Specifically, the proposed framework is implemented using two cascaded subnetworks: Firstly, for radar micro-Doppler spectrum denoising, we propose an improved 1D DnCNN subnetwork to enhance noisy or weak micro-Doppler signatures. Secondly, an AlexNet subnetwork is cascaded for the classification task, and the joint loss is calculated to update the denoising subnetwork and assist with optimal classification performance. We have conducted a comprehensive set of experiments using six types of targets with a ground surveillance radar system to demonstrate the denoising and classification performance of the proposed cascaded framework, which shows significant improvement over separate training of denoising and classification models. Full article

Video satellites provide high-temporal-resolution remote sensing images that enable continuous monitoring of the ground for applications such as target tracking and airport traffic detection. In this paper, we address the problems of object occlusion and the tracking of rotating objects in satellite videos by introducing a rotation-adaptive tracking algorithm for correlation filters with motion estimation (RACFME). Our algorithm proposes the following improvements over the KCF method: (a) A rotation-adaptive feature enhancement module (RA) is proposed to obtain the rotated image block by affine transformation combined with the target rotation direction prior, which overcomes the disadvantage of HOG features lacking rotation adaptability, improves tracking accuracy while ensuring real-time performance, and solves the problem of tracking failure due to insufficient valid positive samples when tracking rotating targets. (b) Based on the correlation between peak response and occlusion, an occlusion detection method for vehicles and ships in satellite video is proposed. (c) Motion estimations are achieved by combining Kalman filtering with motion trajectory averaging, which solves the problem of tracking failure in the case of object occlusion. The experimental results show that the proposed RACFME algorithm can track a moving target with a 95% success score, and the RA module and ME both play an effective role. Full article

The ability to monitor forest areas after disturbances is key to ensure their regrowth. Problematic situations that are detected can then be addressed with targeted regeneration efforts. However, achieving this with automated photo interpretation is problematic, as training such systems requires large amounts of labeled data. To this effect, we leverage citizen science data (iNaturalist) to alleviate this issue. More precisely, we seek to generate pre-training data from a classifier trained on selected exemplars. This is accomplished by using a moving-window approach on carefully gathered low-altitude images with an Unmanned Aerial Vehicle (UAV), WilDReF-Q (Wild Drone Regrowth Forest—Quebec) dataset, to generate high-quality pseudo-labels. To generate accurate pseudo-labels, the predictions of our classifier for each window are integrated using a majority voting approach. Our results indicate that pre-training a semantic segmentation network on over 140,000 auto-labeled images yields an $F1$ score of $43.74$% over 24 different classes, on a separate ground truth dataset. In comparison, using only labeled images yields a score of $32.45$%, while fine-tuning the pre-trained network only yields marginal improvements ($46.76$%). Importantly, we demonstrate that our approach is able to benefit from more unlabeled images, opening the door for learning at scale. We also optimized the hyperparameters for pseudo-labeling, including the number of predictions assigned to each pixel in the majority voting process. Overall, this demonstrates that an auto-labeling approach can greatly reduce the development cost of plant identification in regeneration regions, based on UAV imagery. Full article

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground moving target indication (GMTI) radar systems has received great interest. In GMTI, moving target relocation is an essential requirement, because the positions of the moving targets are usually displaced. For a multichannel radar system, the position of moving targets can be accurately obtained by estimating their interferometric phase. However, the high position accuracy requirements of antennas and the computational resource requirements of algorithms limit the applications of relocation algorithms in UAV-borne GMTI radar systems. In addition, the clutter’s interferometric phase can be severely affected by the undesired phase error in the site. To overcome these issues, we propose an improved knowledge-based (KB) algorithm. In the algorithm, moving targets can be relocated by comparing their interferometric phase with the clutter’s phase. As for the undesired phase error, the algorithm first employs a random sample consensus (RANSAC) algorithm to iteratively filter the outliers. Compared with other classic relocation algorithms, the proposed algorithm shows better relocation accuracy and can be applied in real-time applications. The performance of the proposed improved KB algorithm was evaluated using both simulated and real experimental data. Full article

Background: Compared to other groups of Canadians, Black people have been significantly more affected by COVID-19 and appear to be more hesitant to receive the COVID-19 vaccine. This article identifies approaches or strategies to increase vaccine confidence and uptake among Black people in Canada. Methods: Thirty-six Black people of diverse ethnicities, aged 18 years and above, living in six provinces across Canada were interviewed. An inductive thematic approach was employed to analyze the interview data. Results: Building trust was at the center of the strategies identified and spoke to the meaningful and practical ways the sociocultural realities of Black people living in Canada can be used to inform and implement the most effective health interventions. Identified strategies include public education, building trust through Black-led community engagement, and addressing barriers to vaccine convenience focusing on health literacy and communication. Together, these strategies consider the nuance of the message, diversity of messenger(s), and communication channels and call for a move away from generic health promotion messages to tailored communications grounded in community expertise and the experiences of Black people across all levels of healthcare service provision. Conclusions: Health promotion and public health messages must acknowledge difference, tailor approaches to target audiences, and foster lasting collaborations informed by members of the Black community. Government agencies and healthcare service providers should foster the relationships established during the pandemic, document lessons learned, remove systemic barriers to healthcare, and create an emergency preparedness guide for community engagement and health promotion for Black people living in Canada. Full article

This paper deals with the application of a novel fiber optic SHM system for bonding lines monitoring of a composite aircraft wing within Clean Sky 2 program framework. With the aim of controlling the structural state of the reference component, several targets may be addressed, including safety increase through a periodic update of the integrity level, maintenance costs reduction, for instance, by moving to an on-demand from the usual scheduled approach, and even design benefits by envisaging the possibility of modulating the safety coefficients due to an increased knowledge of the intimate structural system behavior. Specifically, an original SHM architecture is herein presented, based on the use of distributed optical fibers, and implementing a proprietary algorithm, to detect bonding lines damage. Ground testing with a full-scale wing box successfully validated the system’s capability to identify damage. To assess maturity, a TRL evaluation has been carried out, whose results are summarized and discussed. Such a process allowed us to highlight specific areas for technological improvement, such as modeling-testing synergy and operational environment definition. The work herein reported is expected to address these aspects while achieving full-scale aircraft integration, paving the way for enhanced structural robustness and operational safety in future aircraft. Full article

The application of UAVs in surveillance, disaster management, and military operations has surged, necessitating robust and real-time tracking systems for moving targets. However, accurately tracking and predicting the trajectories of ground targets pose significant challenges due to factors such as target occlusion, varying speeds, and dynamic environments. To address these challenges and advance the capabilities of UAV-based tracking systems, a novel vision-based approach is introduced in this paper. This approach leverages the visual data captured by the UAV’s onboard cameras to achieve real-time tracking, geolocation, trajectory recovery, and predictive analysis of moving ground targets. By employing filter, regression and optimization techniques, the proposed system is capable of accurately estimating the target’s current position and predicting its future path even in complex scenarios. The core innovation of this research lies in the development of an integrated algorithm that combines object detection, target geolocation, and trajectory estimation into a single, cohesive framework. This algorithm not only facilitates the online recovery of the target’s motion trajectory but also enhances the UAV’s autonomy and decision-making capabilities. The proposed methods are validated through real flight experiments, demonstrating their effectiveness and feasibility. Full article

Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant area of focus and innovation within the SAR research community. In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. A multi-target tracking algorithm for ViSAR that utilizes re-identification (ReID) features and a multi-stage data association process is proposed. The algorithm extracts high-dimensional ReID features using the Dense-Net121 network for enhanced shadow detection and calculates a cost matrix by integrating ReID feature cosine similarity with Intersection over Union similarity. A confidence-based multi-stage data association strategy is implemented to minimize missed detections and trajectory fragmentation. Kalman filtering is then employed to update trajectory states based on shadow detection. Both simulation experiments and actual data processing experiments have demonstrated that, in comparison to two traditional video multi-target tracking algorithms, DeepSORT and ByteTrack, the newly proposed algorithm exhibits superior performance in the realm of ViSAR multi-target tracking, yielding the highest MOTA and HOTA scores of 94.85% and 92.88%, respectively, on the simulated spaceborne ViSAR data, and the highest MOTA and HOTA scores of 82.94% and 69.74%, respectively, on airborne field data. Full article

This paper focuses on the problem of ground-motion target localization tracking and motion state estimation for high-altitude reconnaissance using fixed-wing UAVs. Our goal is to accurately locate and track ground-moving targets and estimate their motion using visible light images, laser measurements of distance, and UAV position and attitude information. Firstly, this paper uses the target detection model of YOLOv8 to obtain the target pixel positions, combined with the measurement data, to establish the geolocalization model of the ground-motion target. Secondly, a motion state estimation algorithm with hierarchical filtering is proposed, and this algorithm performs motion state estimation for optoelectronic loads and ground-motion targets separately. Using the laser range sensor measurements as constraints, the optoelectronic load angle state quantities are involved together in estimating the ground target motion state, resulting in improved accuracy of ground-motion target localization tracking and motion state estimation. The experimental data show that the UAV ground-motion target localization tracking and motion estimation algorithm using hierarchical filtering reduces the localization tracking error by at least 7.5 m and the motion state estimation error by at least 0.8 m/s compared to other algorithms. Full article

In dual-channel synthetic aperture radar (SAR) systems, the estimation of the four-dimensional motion parameters of the ground maneuvering target is a critical challenge. In particular, when spatial degrees of freedom are used to enhance the target’s output signal-to-clutter-plus-noise ratio (SCNR), it is possible to have multiple solutions in the parameter estimation of the target. To deal with this issue, a novel algorithm for estimating the motion parameters of ground moving targets in dual-channel SAR systems is proposed in this paper. First, the random sample consensus (RANSAC) and modified adaptive 2D calibration (MA2DC) are used to prevent the target’s phase from being distorted as a result of channel balancing. To address range migration, the RFRT algorithm is introduced to achieve arbitrary-order range migration correction for moving targets, and the generalized scaled Fourier transform (GSCFT) algorithm is applied to estimate the polynomial coefficients of the target. Subsequently, we propose using the synthetic aperture length (SAL) of the target as an independent equation to solve for the four-dimensional parameter information and introduce a windowed maximum SNR method to estimate the SAL. Finally, a closed-form solution for the four-dimensional parameters of ground maneuvering targets is derived. Simulations and real data validate the effectiveness of the proposed algorithm. Full article