Search Results (23)

Snow cover fraction (SCF) records derived from optical satellite sensors such as AVHRR are systematically interrupted by cloud contamination and polar night conditions, leaving large spatiotemporal data gaps that limit their utility for climate and hydrological applications. This study presents a U-Net–based deep learning framework for reconstructing missing SCF values in Scandinavia over a 15-year period (2000–2014), using the ESA CCI L3C SCFV AVHRR v4.0 product as both partial input and training target. The model integrates physically meaningful auxiliary predictors (snow water equivalent (SWE), near-surface air temperature, elevation, and land cover) harmonized to a common 0.05° grid, enabling reconstruction in the complete absence of concurrent optical observations. Trained on a single year with extensive synthetic masking (91.5% of valid SCF pixels withheld), the U-Net achieves an R² of 0.9342 and RMSE of 0.1127, outperforming spatial interpolation, a SWE-based physical baseline, and pixel-wise machine learning baselines. Feature importance analysis confirms that SWE and temperature dominate predictive skill, with the observed SCF input contributing negligibly. Independent validation against ground station observations yields 86.7% binary classification accuracy and an F1 score of 88.0%, comparable to the 87.8% accuracy of the original satellite retrievals, demonstrating the viability of deep learning–based gap-filling for producing continuous SCF records under cloud cover and polar night. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

Forest ecosystems in northeastern Japan are characterized by natural mixed forests, where sustainable management has always been limited because of their difficult accessibility. The aims of this study are first, to assess mixed forest composition, and second, to train Mask R-CNN models with these data in order to detect and segment trees in a 19-ha mixed forest composed mainly of beech (Fagus crenata), oak (Quercus crispula), magnolia (Magnolia obovata) and larch (Larix kaempferi). The Mask R-CNN model was applied in two experimental scenarios: a single multi-class model and species-specific models. RGB images consisted of four orthomosaics (August, September, October 2024 and October 2025), which yielded 1725, 359, 129 and 525 samples of each tree species, respectively. The Unmanned Aerial Vehicle (UAV)-QField validation method improved the classification accuracy of the annotations and made it possible to map each tree species distribution and understand the composition of mixed forests along an elevation gradient. The multi-class model demonstrated an overall precision of 0.59, a recall of 0.53, and an F1-score of 0.56. The detection performance for individual tree species was similar for both models. Based on these results, the multi-class model is more suitable because it decreases the possibility of misclassification of tree species. Full article

Accurate perception of deformable objects on the lunar surface is essential for autonomous construction and in situ resource utilization (ISRU) missions. However, the lack of representative lunar imagery limits the development of data-driven models for pose estimation and manipulation. We present SynBag 1.0, a large-scale synthetic dataset designed for training and benchmarking six-degree-of-freedom (6-DoF) pose estimation algorithms on regolith-filled construction bags. SynBag 1.0 employs rigid-body representations of bag meshes designed to approximate deformable structures with varied levels of feature richness. The dataset was generated using a novel framework titled MoonBot Studio, built in Unreal Engine 5 with physically consistent lunar lighting, low-gravity dynamics, and dynamic dust occlusion modeled through Niagara particle systems. SynBag 1.0 contains approximately 180,000 labeled samples, including RGB images, dense depth maps, instance segmentation masks, and ground-truth 6-DoF poses in a near-BOP format. To verify dataset usability and annotation consistency, we perform zero-shot 6-DoF pose estimation using a state-of-the-art model on a representative subset of the dataset. Variations span solar azimuth, camera height, elevation, dust state, surface degradation, occlusion level, and terrain type. SynBag 1.0 establishes one of the first open, physically grounded datasets for 6-DoF-object perception in lunar construction and provides a scalable basis for future datasets incorporating soft-body simulation and robotic grasping. Full article

Background: Innovative training strategies aimed at improving physiological efficiency are of growing interest in kinesiology and sports performance. Elevation training masks (ETMs) offer a practical means of inducing hypoxia-like stress. However, evidence of their effectiveness in recreationally active populations remains limited. This pilot study examined the efficiency of a five-week progressive ETM protocol combined with high-intensity interval training (HIIT) in eliciting physiological, hematological, and body-composition adaptations relevant to endurance performance. Methods: Nine recreationally active men completed a five-week intervention consisting of three treadmill-based sessions per week: one weekly incremental Conconi test and two structured aerobic–anaerobic HIIT sessions performed with an ETM. Mask resistance was progressively increased to simulate altitudes of approximately 900–3600 m. Hematological variables (erythrocytes, hemoglobin, hematocrit, erythrocyte indices, leukocytes, and platelets), body composition, maximal heart rate (HRmax), and peripheral oxygen saturation (SpO₂) were assessed pre- and post intervention. Data were analyzed using paired-sample t-tests and repeated-measures ANOVA, with effect sizes reported (Cohen’s d, ω²). Results: A significant main effect of time on SpO₂ was observed (F(1, 8) = 130.61, p < 0.001, ω² = 0.69), along with a significant effect of training week (F(4, 32) = 17.41, p < 0.001, ω² = 0.43), and a significant Time × Week interaction (F(4, 32) = 15.20, p < 0.001, ω² = 0.42), indicating progressively greater post-exercise oxygen desaturation with increasing simulated altitude. Significant post-intervention increases were found in erythrocyte count, hemoglobin concentration, and hematocrit (p ≤ 0.009, d = 1.15–1.55), alongside increases in mean corpuscular volume and mean corpuscular hemoglobin. Platelet count increased significantly (p = 0.001, d = 1.68), while leukocyte values remained unchanged (p > 0.05). Body mass index (p = 0.049, d = 0.77) and body fat percentage (p = 0.012, d = 1.08) decreased following the intervention. HRmax tended to be lower at higher simulated altitudes. Conclusions: A five-week progressive ETM-HIIT protocol efficiently induced hematological and body-composition adaptations associated with improved oxygen transport and metabolic efficiency in recreationally active men. These findings support ETM-based training as an accessible strategy for enhancing physiological efficiency in endurance-oriented kinesiology practice, warranting confirmation in larger randomized controlled studies. Full article

The increasing adoption of artificial intelligence (AI) in intelligent healthcare systems has elevated the demand for robust medical imaging and vision-based inventory solutions. For an intelligent healthcare inventory system, accurate recognition and classification of medical items, including medicines and emergency supplies, are crucial for ensuring inventory integrity and timely access to life-saving resources. This study presents a hybrid deep learning framework, EfficientDet-BiFormer-ResNet, that integrates three specialized components: EfficientDet’s Bidirectional Feature Pyramid Network (BiFPN) for scalable multi-scale object detection, BiFormer’s bi-level routing attention for context-aware spatial refinement, and ResNet-18 enhanced with triplet loss and Online Hard Negative Mining (OHNM) for fine-grained classification. The model was trained and validated on a custom healthcare inventory dataset comprising over 5000 images collected under diverse lighting, occlusion, and arrangement conditions. Quantitative evaluations demonstrated that the proposed system achieved a mean average precision (mAP@0.5:0.95) of 83.2% and a top-1 classification accuracy of 94.7%, outperforming conventional models such as YOLO, SSD, and Mask R-CNN. The framework excelled in recognizing visually similar, occluded, and small-scale medical items. This work advances real-time medical item detection in healthcare by providing an AI-enabled, clinically relevant vision system for medical inventory management. Full article

Air pollution is an escalating global concern with significant implications for human health and athletic performance. This narrative review synthesizes and critically compares the current literature on the impact of air pollution on health and football performance, elucidates the physiological mechanisms involved, and evaluates available mitigation strategies. Comparative studies consistently demonstrate that football players—who frequently engage in high-intensity outdoor exercise—are particularly susceptible to the harmful effects of airborne pollutants such as particulate matter (PM), volatile organic compounds (VOCs), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO). These pollutants bypass natural respiratory defenses due to increased pulmonary ventilation during exercise, reaching deeper lung regions and triggering oxidative stress, inflammation, and impaired lung function. Evidence across studies indicates that poor air quality is associated with decreased football performance, including reduced distance covered, fewer high-intensity efforts, elevated physiological strain, and diminished training adaptation. Long-term exposure exacerbates respiratory conditions, suppresses immune function, and heightens the risk of illness and injury. Furthermore, comparative genetic research highlights inter-individual variability in pollution sensitivity, with specific gene variants conferring either increased vulnerability or resilience to adverse effects. This review also explores practical and emerging mitigation strategies—such as timing training to avoid peak pollution, utilizing air quality monitoring and antioxidant-rich diets, and promoting sustainable infrastructure—to safeguard athlete health and optimize performance. Novel approaches including respiratory training, anti-smog masks, indoor sessions, and personalized recovery protocols offer additional protection and recovery support. Full article

Background/Objectives: This study aims to determine the prevalence of stunting in children under two years old and its association with the maternal profile (including anthropometric measurements), care, feeding practices, and socioeconomic level. It also attempts to assess if stunting may contribute to an underestimation of wasting by performing a preliminary speculative analysis using the expected height for age instead of the real observed height of the children. Methods: The study employed a cross-sectional design, examining mother–child pairs in the rural municipality of Ampefy in the Itasy Region of Madagascar, between 2022 and 2023. A total of 437 mother–child (0–24 months) pairs participated in the study. A questionnaire was administered to collect data on the maternal lifestyle. Maternal medical histories were reviewed, and anthropometric parameters of both the mothers and their child were taken by specialized and trained health professionals with multiple years of experience. Results: The prevalence of stunting in children was 57.4% (95% CI: 52.64–62.10). Stunting was associated with maternal anthropometric measurements (p < 0.001), maternal education (p = 0.004), and breastfeeding (p = 0.047), which appears to have a protective effect. The weight-for-length z-score indicated that only 12.4% of the total children were affected by wasting. In the preliminary speculative analysis using the WHO height-for-age standard, the theoretical prevalence of wasting was estimated to be 42.3%, with a considerable prevalence of severe wasting. The main limitations of this study were the possible selection bias, the limitations inherent to the taking of anthropometric measurements in small children, and therefore, the possible misclassification of the children. The use of a theoretical weight-for-length z-score to estimate a theoretical prevalence of wasting using an untested speculative analysis is also a limitation to the validity of the estimation. Conclusions: Stunting affected over half of the children included in the study (57.4%), but the prevalence of wasting was below what was expected, at 12.4%. In the preliminary speculative analysis using the expected height for age, it was estimated that wasting could possibly affect up to 42.3% of the children. This discrepancy, while it cannot be taken as factual due to the nature of the analysis, could serve as a warning that perhaps the elevated rates of stunting may be masking wasting in some children and other forms of nutritional assessments may be needed in areas where stunting is prevalent. Full article

This paper aims to include the human capital elements of agricultural managers in the decision-making process of farmland quality protection behavior in farmers’ cooperatives in an effort to discuss and explore the relationship between hiring agricultural managers and the implementation of farmland quality protection behavior. Based on the survey questionnaire of 436 planting cooperatives in Sichuan, China, in 2021, the Poisson model and mediating effect model were used to explore the impact and mechanism of hiring agricultural managers on farmland quality protection behavior in farmers’ cooperatives. The empirical results reveal that hiring agricultural managers significantly elevates farmland quality protection behavior in farmers’ cooperatives. Compared to cross-period farmland quality protection behavior, hiring agricultural managers has a greater impact on single-period farmland quality protection behavior. Through the improvement of information technology application level, the farmland quality protection behavior in cooperatives can be elevated by hiring agricultural managers, but standardized management has a masking effect between the two. Accordingly, continuous development and growth of the agricultural manager team encourages cooperatives to establish a standardized system for recruiting farm managers and strengthen the link between farm managers and cooperatives. The training of agricultural managers should be optimized to deepen their mastery of techniques and knowledge and protect the quality of arable land. Material and moral incentives should be provided to encourage farm managers to focus on the long-term development of their cooperatives. The government should be encouraged to establish a platform for sharing information on farmland quality to provide technical support to farm managers to carry out targeted work on farmland quality protection. Full article

Accurate and efficient estrus detection in sows is crucial in modern agricultural practices to ensure optimal reproductive health and successful breeding outcomes. A non-contact method using computer vision to detect a change in a sow’s vulva size holds great promise for automating and enhancing this critical process. However, achieving precise and reliable results depends heavily on maintaining a consistent camera distance during image capture. Variations in camera distance can lead to erroneous estrus estimations, potentially resulting in missed breeding opportunities or false positives. To address this challenge, we propose a robust six-step methodology, accompanied by three stages of evaluation. First, we carefully annotated masks around the vulva to ensure an accurate pixel perimeter calculation of its shape. Next, we meticulously identified keypoints on the sow’s vulva, which enabled precise tracking and analysis of its features. We then harnessed the power of machine learning to train our model using annotated images, which facilitated keypoint detection and segmentation with the state-of-the-art YOLOv8 algorithm. By identifying the keypoints, we performed precise calculations of the Euclidean distances: first, between each labium (horizontal distance), and second, between the clitoris and the perineum (vertical distance). Additionally, by segmenting the vulva’s size, we gained valuable insights into its shape, which helped with performing precise perimeter measurements. Equally important was our effort to calibrate the camera using monocular depth estimation. This calibration helped establish a functional relationship between the measurements on the image (such as the distances between the labia and from the clitoris to the perineum, and the vulva perimeter) and the depth distance to the camera, which enabled accurate adjustments and calibration for our analysis. Lastly, we present a classification method for distinguishing between estrus and non-estrus states in subjects based on the pixel width, pixel length, and perimeter measurements. The method calculated the Euclidean distances between a new data point and reference points from two datasets: “estrus data” and “not estrus data”. Using custom distance functions, we computed the distances for each measurement dimension and aggregated them to determine the overall similarity. The classification process involved identifying the three nearest neighbors of the datasets and employing a majority voting mechanism to assign a label. A new data point was classified as “estrus” if the majority of the nearest neighbors were labeled as estrus; otherwise, it was classified as “non-estrus”. This method provided a robust approach for automated classification, which aided in more accurate and efficient detection of the estrus states. To validate our approach, we propose three evaluation stages. In the first stage, we calculated the Mean Squared Error (MSE) between the ground truth keypoints of the labia distance and the distance between the predicted keypoints, and we performed the same calculation for the distance between the clitoris and perineum. Then, we provided a quantitative analysis and performance comparison, including a comparison between our previous U-Net model and our new YOLOv8 segmentation model. This comparison focused on each model’s performance in terms of accuracy and speed, which highlighted the advantages of our new approach. Lastly, we evaluated the estrus–not-estrus classification model by defining the confusion matrix. By using this comprehensive approach, we significantly enhanced the accuracy of estrus detection in sows while effectively mitigating human errors and resource wastage. The automation and optimization of this critical process hold the potential to revolutionize estrus detection in agriculture, which will contribute to improved reproductive health management and elevate breeding outcomes to new heights. Through extensive evaluation and experimentation, our research aimed to demonstrate the transformative capabilities of computer vision techniques, paving the way for more advanced and efficient practices in the agricultural domain. Full article

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m² and 25.36 W/m², with biases of 3.42 W/m² and 3.92 W/m², respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics. Full article

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric $MAP$, while the regression performance of machine learning was assessed with Root Mean Square Error ($RMSE$) and Mean Absolute Error ($MAE$). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results ($Precisio{n}_{\mathrm{smoke}}=89.12\%$). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing. Full article

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and a solution based on prediction of the weight mask by an autoencoder architecture, subsequently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. Depending on the case study and the level of validation strictness, the proposed solution achieved a root mean square error (RMSE) ranging between 2 cm and 16 cm. The proposed method outperforms methods based on the straightforward sampling of photogrammetric DSM, achieving, on average, an 84% lower RMSE for stratified cross-validation and a 62% lower RMSE for all-in-case-out cross-validation. By utilizing data from other research, the proposed solution was compared on the same case study with other UAV-based methods. For that benchmark case study, the proposed solution achieved an RMSE score of 5.9 cm for all-in-case-out cross-validation and 3.5 cm for stratified cross-validation, which is close to the result achieved by the radar-based method (RMSE of 3 cm), which is considered the most accurate method available. The proposed solution is characterized by a high degree of explainability and generalization. Full article

Background: In the last two decades, high-altitude training (HAT) and elevation training masks (ETMs) have been widely used among athletes to enhance physical performance. However, few studies have examined the effect of wearing ETMs on physiological and hematological parameters in different sports. Aims: The present study aimed to investigate the impact of ETM use in athletes on several hematological and physiological indicators among cyclists, runners, and swimmers. Methods: The impact of wearing an ETM on lung function (LF), aerobic capacity (AC), and hematological levels in male university-level athletes (cyclists, runners, and swimmers) was investigated using an experimental approach. The participants (N = 44) were divided into (i) an experimental group wearing ETMs (n = 22; aged 21.24 ± 0.14 years old) and (ii) a control group not wearing ETMs (n = 22; aged 21.35 ± 0.19 years old). Both groups underwent 8 weeks of high-intensity cycle ergometer interval training. Pre- and post-training tests included the above-mentioned physiological and hematological parameters. Results: Except for FEV₁, FEV₁/FVC, VT1, and MHR in the control group and FEV₁/FVC and HRM in the experimental group, all variables were significantly improved after the 8-week cycle ergometer HIIT program. Significant benefits in favor of the experimental group were noted in terms of changes in FVC, FEV₁, VO₂max, VT1, PO to VT, VT2, and PO to VT2. Conclusions: The eight-week ETM-assisted HIIT program improved cardiorespiratory fitness and hematological variables in all participants. Future research would be useful to further investigate the physiological changes resulting from ETM-assisted HIIT programs. Full article

Physical exercise demonstrates a special case of aerosol emission due to its associated elevated breathing rate. This can lead to a faster spread of airborne viruses and respiratory diseases. Therefore, this study investigates cross-infection risk during training. Twelve human subjects exercised on a cycle ergometer under three mask scenarios: no mask, surgical mask, and FFP2 mask. The emitted aerosols were measured in a grey room with a measurement setup equipped with an optical particle sensor. The spread of expired air was qualitatively and quantitatively assessed using schlieren imaging. Moreover, user satisfaction surveys were conducted to evaluate the comfort of wearing face masks during training. The results indicated that both surgical and FFP2 masks significantly reduced particles emission with a reduction efficiency of 87.1% and 91.3% of all particle sizes, respectively. However, compared to surgical masks, FFP2 masks provided a nearly tenfold greater reduction of the particle size range with long residence time in the air (0.3–0.5 μm). Furthermore, the investigated masks reduced exhalation spreading distances to less than 0.15 m and 0.1 m in the case of the surgical mask and FFP2 mask, respectively. User satisfaction solely differed with respect to perceived dyspnea between no mask and FFP2 mask conditions. Full article