Imagery Techniques Research Articles

How do athletes perform well under pressure? A meta-study

ABSTRACT Many studies have explored athlete performance under pressure, with recent emphases on choking prevention and definitions of clutch performance. Whilst these two areas may benefit sport performance research, it may also be important to explore the prominent factors in facilitating athlete performance under pressure to promote the development of interventions targeting clutch performance in the future. In the present review, we synthesised qualitative research on athlete performance under pressure to highlight the psychological factors and strategies useful in facilitating performance under pressure in sport. Given the difficulties in reviewing and conducting quantitative research on these concepts (e.g. there currently is no validated measure of pressure), we chose to review qualitative research. Database searchers were conducted in September 2022, August 2023, and August 2024. This resulted in the identification and meta-synthesis of 29 articles reporting qualitative findings. Athletes, coaches, and sporting practitioners considered self-efficacy, demand appraisal, mental toughness, and task-focused resource allocation as important in facilitating athlete performance under pressure. Pre-performance routines, imagery techniques, self-talk, goal setting, cognitive restructuring, and simulated pressure training were also reported as useful strategies to facilitate athlete performance under pressure.

Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.

SolarSAM: Building-scale Photovoltaic Potential Assessment Based on Segment Anything Model (SAM) and Remote Sensing for Emerging City

Driven by advancements in photovoltaic (PV) technology, solar energy has emerged as a promising renewable energy source due to its ease of integration onto building rooftops, facades, and windows. For emerging cities, the lack of detailed street-level data presents a challenge for effectively assessing the potential of building-integrated photovoltaic (BIPV). To address this, this study introduces SolarSAM, a novel BIPV evaluation method that leverages satellite imagery and deep learning techniques, and an emerging city in northern China is utilized to validate the model performance. SolarSAM segmented various building rooftops using text prompt-guided semantic segmentation during the process. Separate PV models were then developed for Rooftop PV, Facade-integrated PV, and PV windows, using this segmented data and local climate information. The potential for BIPV installation, solar power generation, and city-wide power self-sufficiency were assessed, revealing that the annual BIPV power generation potential surpassed the city’s total electricity consumption by a factor of 2.5. Economic and environmental analysis were also conducted for the BIPVs on different buildings; the levelized cost of electricity is 0.18-0.41 CNY/kWh, and the annual total carbon reduction is 7.08×107 T CO2. These findings demonstrated the model’s performance and revealed the potential for BIPV power generation.

Correction: Ocholla et al. Livestock Detection and Counting in Kenyan Rangelands Using Aerial Imagery and Deep Learning Techniques. Remote Sens. 2024, 16, 2929

The authors would like to make the following corrections to their published paper [...]

Evaluation of Settlement Distribution on Detailed Spatial Plan in Sewon District, Bantul Regency, Special Region of Yogyakarta 2018 – 2038

Settlement as a place to live is one of the main needs that need to be met for the survival of human life. The need for settlement will increase along with the increase in population. The development of settlements in Sewon District. Bantul, Indonesia which is very dynamic requires monitoring to ensure that the distribution of settlements is in accordance with the spatial pattern plan that has been determined by the local government. Imagery as a remote sensing product which is then processed with the help of GIS [HV1] can be used in monitoring the distribution of settlements because it can provide more detailed information regarding land use, including settlements. This research aims to evaluate the distribution of settlements against the detailed spatial plan (RDTR) for the Sewon Urban Area (BWP) of Bantul Regency in 2018-2038. This study uses visual interpretation techniques of SPOT-7 PMS imagery in mapping existing settlement land, field surveys, and GIS processing. The research results show that mapping settlement land using imagery produces an overall accuracy of 95.20%. The settlement evaluation shows that there is a suitability of settlement land with the detailed spatial plan reaching 579.88 hectares or 87.26%, while the settlement land that is not in accordance with the detailed spatial plan is 9.62 hectares or 1.45%, and the settlement land that is temporarily not in accordance with the detailed spatial plan is 75.05 hectares or 11.29% of the total settlement area in Sewon District. Local governments must pay more attention to existing settlements with regular monitoring so that the existing settlements that are not in accordance with the detailed spatial plan will not expand.

Effects of app-based imagery training on imagery skills and technical performance in junior national squash athletes

This study explores the impact of app-based imagery and traditional methods (i.e., reflective diary) on cognitive-specific (CS) imagery in junior national squash athletes. A total of 75 participants (mean age = 15.22, SD = 2.14) without prior imagery training were allocated into app-based imagery (n = 25), reflective diary (n = 25), and control groups (n = 25). After an imagery workshop and a 12-week reinforcement, CS imagery scores were assessed pretest, post-test, and follow-up using the Sport Imagery Questionnaire (SIQ), as well as technical shot execution (i.e., drive, boast, dropshot, lob, and volley). Using one-way repeated measures analysis of variance and the Friedman test, we examined differences in CS imagery and technical skills across timepoints. The app-based group showed significant improvements in SIQ scores at post- (F(2,72) = 58.53, p < .001, η2 = 0.61) and follow-up tests (F(2,72) = 97.31, p < .001, η2 = 0.73), as well as in technical skills (drive, χ2(2) = 40.92, p < 0.001; boast, χ2(2) = 45.81, p < 0.001; dropshot, χ2(2) = 38.57, p < 0.001; lob, χ2(2) = 42.51, p < 0.001; volley, χ2(2) = 41.81, p < 0.001). A post-hoc analysis revealed that the app-based imagery group experienced the most substantial effect, surpassing reflective diary and control groups, indicating the effectiveness of app-based interventions in enhancing CS imagery and technical performance. The results demonstrate that mobile applications represent a practical and effective substitute for traditional imagery and mental skill reinforcement techniques, rendering these essential tools more accessible to athletes.

Sub-national scale mapping of individual olive trees integrating Earth observation and deep learning

The olive tree holds great cultural, environmental, and economic significance in the Mediterranean region. In particular, Morocco has been making dedicated investments over $10 billion since 2008 to fuel the transition from cereal to olive production. Understanding the spatial extent of this large-scale land conversion is critical for a variety of socioeconomic purposes. In response to this demand, we conducted a study to map individual olive trees in northern Morocco using satellite imagery and deep learning techniques at a sub-national scale. This study utilized cloud-free, very-high-resolution DigitalGlobe imagery collected between 2018 and 2022 to identify each individual olive tree in six northern Morocco provinces. We compared various deep learning models, including both transformer-based and CNN-based models, to generate patch-level spatial constraints and pixel-level tree identification. We found that transformer-based models outperformed CNN-based models in both tasks. Additionally, spatially constraining the pixel-level results improved olive tree mapping accuracy to varying degrees, depending on the initial performance of the model. The evaluation of the olive map generated from this study shows high accuracy in both surveyed and unsampled regions. This research represents the first-of-its-kind individual olive tree mapping at the sub-national scale that can help monitor the large-scale land conversions such as about 110,000 ha of olive plantings in the six Moroccan provinces studies here. Meanwhile it demonstrates a cost-effective and efficient prototype approach that can be adapted to identify similar tree crop expansion occurring in other parts of the world.

Livestock Detection and Counting in Kenyan Rangelands Using Aerial Imagery and Deep Learning Techniques

Accurate livestock counts are essential for effective pastureland management. High spatial resolution remote sensing, coupled with deep learning, has shown promising results in livestock detection. However, challenges persist, particularly when the targets are small and in a heterogeneous environment, such as those in African rangelands. This study evaluated nine state-of-the-art object detection models, four variants each from YOLOv5 and YOLOv8, and Faster R-CNN, for detecting cattle in 10 cm resolution aerial RGB imagery in Kenya. The experiment involved 1039 images with 9641 labels for training from sites with varying land cover characteristics. The trained models were evaluated on 277 images and 2642 labels in the test dataset, and their performance was compared using Precision, Recall, and Average Precision (AP0.5–0.95). The results indicated that reduced spatial resolution, dense shrub cover, and shadows diminish the model’s ability to distinguish cattle from the background. The YOLOv8m architecture achieved the best AP0.5–0.95 accuracy of 39.6% with Precision and Recall of 91.0% and 83.4%, respectively. Despite its superior performance, YOLOv8m had the highest counting error of −8%. By contrast, YOLOv5m with AP0.5–0.95 of 39.3% attained the most accurate cattle count with RMSE of 1.3 and R2 of 0.98 for variable cattle herd densities. These results highlight that a model with high AP0.5–0.95 detection accuracy may struggle with counting cattle accurately. Nevertheless, these findings suggest the potential to upscale aerial-imagery-trained object detection models to satellite imagery for conducting cattle censuses over large areas. In addition, accurate cattle counts will support sustainable pastureland management by ensuring stock numbers do not exceed the forage available for grazing, thereby mitigating overgrazing.

Potential for Events Similar to the Deadly West Salt Creek Landslide, Grand Mesa Area, Colorado

ABSTRACT The deadly West Salt Creek Landslide of 2014, one of the largest landslides to occur in the United States in historical time, was surprising in its suddenness and length of runout. Its source area, the edge of Grand Mesa in western Colorado, was potentially the source of other similar landslides in the past, based on previous mapping in the region. The purpose of this research was to evaluate the potential for similar events by detailed mapping of several canyons flanking West Salt Creek, using aerial imagery and age-dating techniques not available to previous mappers. We conclude that, while the West Salt Creek Landslide has the potential to reactivate in a similar manner to the 2014 movement, the potential for a similar event in the flanking canyons is low. Mapping and semi-quantitative geomorphic methods suggest that the deposits present in several canyons on either side of West Salt Creek are glacial or stream deposits, with one canyon containing debris-flow deposits. Our interpretations are based on the morphology of deposits in the canyons, the presence of specific landform features, measurements of mobility index and longitudinal profiles, and comparison with regional analogs. Furthermore, other mass movements in the region that are of similar magnitude had either slower movement or much shorter runout. The large block at the head of West Salt Creek and the earlier block that shattered and mobilized in 2014 are also unique features locally, distinguishing this setting from others, but also indicating the potential for a future similar event.

Effect of mindfulness-based intervention on perceived stress among breast cancer patients undergoing chemotherapy.

Breast cancer is the second most prevalent disease among women in India and one of the most dangerous and lethal. Chemotherapy-treated breast cancer patients may have perceived stress, which is defined as emotions of mental or physical exhaustion that make them feel angry or anxious. Mindfulness-based intervention (MBI) gives some ideas in line with the conventional mindfulness technique. (i) To assess the effect of MBI on perceived stress among breast cancer patients undergoing chemotherapy. (ii) To determine the association between perceived stress with selected demographic variables among breast cancer patients undergoing chemotherapy. The current quantitative pre- and postexperimental study was carried out in two oncology departments of selected cancer hospitals, Bhubaneswar. A total number of 40 breast cancer patients of perceived stress receiving chemotherapy and data were gathered by using a self-structured socio-demographic proforma and a structured Perceived Stress Scale 10. The experimental group received MBI, and a posttest was conducted on the 17th day on both the groups after 10 days of the termination of intervention. This MBI was provided to breast cancer patients for five sessions over five days continuously, each session for 45 min with mindfulness breathing exercises, progressive muscle relaxation techniques, practising meditation, and guided imagery technique. The study findings illustrated that reduction of perceived stress among female breast cancer patients undergoing chemotherapy with (t = 2.2463) (P = 0.0306) at the P < 0.05; furthermore, there is one socio-demographic variable which had association with perceived stress that is history of psychiatric illness (χ 2 = 14.1176) (P = 0.0009) and others had no association with perceived stress. MBI was an effective therapy for reducing the perceived stress of breast cancer patients undergoing chemotherapy.

A new approach to dust source mapping using visual interpretation and object-oriented segmentation of satellite imagery

The emission of dust particles, mainly from arid and semi-arid lands, as a result of climate change and human activities, is known to be a global issue. Identifying dust emission sources is the first key step in dealing with the hazardous consequences of this rising phenomenon. This study is an attempt to address one of the major challenges in mapping dust emission sources. Accordingly, an innovative approach based on visual interpretation of multi-temporal MODIS-Terra/Aqua imagery and object-oriented image segmentation techniques has been developed and implemented in the study areas of the Tigris and Euphrates basin and eastern Iran. This approach takes advantage of land surface characteristics (i.e., dust drivers), including geomorphology, soil, land use/cover, and land surface radiation, to attribute dust emission hotspots to their corresponding areas using multi-source remote sensing data. The results show that the multi-resolution segmentation algorithm with optimized parameters can identify homogeneous segments corresponding to dust emission sources in the study areas with an average spatial agreement of ∼92% compared to the reference areas. Our findings emphasize the robustness and generalizability of the proposed approach, and its capabilities can be used in a complementary way with visual interpretation of satellite images to map dust sources with high spatial accuracy.

Comprehensive review of unmanned aerial vehicle application to safety mining management

The increasing interest of users in unmanned aerial vehicle (UAV) technology has developed new fields of application for it. Presently, UAVs are working in so many areas and the mining industry has shown grown interest in the use of UAVs for routine operations in surface, underground mines, and mine waste dumps. In this paper, a thorough review has been performed on the application of UAVs in mine safety management. Systematic reviews were conducted to summarize the results of over 60 papers published during the 2010-2021 period. The study offers a review of the most common applications involving mine safety, the styles of flying control, and the UAV types exploited. The paper also contributes to the identification of prevalent data acquired by sensors and points out the most popular processing techniques of aerial imagery. The obtained results revealed that UAVs are used at mining sites for safety purposes including mine surface displacements, stability monitoring, working environment, coal fire identification, drill and blast, and mine waste dump operations. This research hopes to provide a technical reference, expanding the knowledge and recognition of UAV monitoring to manage mine safety, as well as an assessment of applications in mining activities.

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

From pixels to plant health: accurate detection of banana Xanthomonas wilt in complex African landscapes using high-resolution UAV images and deep learning

Bananas and plantains are vital for food security and smallholder livelihoods in Africa, but diseases pose a significant threat. Traditional disease surveillance methods, like field visits, lack accuracy, especially for specific diseases like Xanthomonas wilt of banana (BXW). To address this, the present study develops a Deep-Learning system to detect BXW-affected stems in mixed-complex landscapes within the Eastern Democratic Republic of Congo. RGB (Red, Green, Blue) and multispectral (MS) images from unmanned aerial vehicles UAVs were utilized using pansharpening algorithms for improved data fusion. Using transfer learning, two deep-learning model architectures were used and compared in our study to determine which offers better detection capabilities. A single-stage model, Yolo-V8, and the second, a two-stage model, Faster R-CNN, were both employed. The developed system achieves remarkable precision, recall, and F1 scores ranging between 75 and 99% for detecting healthy and BXW-infected stems. Notably, the RGB and PAN UAV images perform exceptionally well, while MS images suffer due to the lower spatial resolution. Nevertheless, specific vegetation indexes showed promising performance detecting healthy banana stems across larger areas. This research underscores the potential of UAV images and Deep Learning models for crop health assessment, specifically for BXW in complex African systems. This cutting-edge deep-learning approach can revolutionize agricultural practices, bolster African food security, and help farmers with early disease management. The study’s novelty lies in its Deep-Learning algorithm development, approach with recent architectures (Yolo-V8, 2023), and assessment using real-world data, further advancing crop-health assessment through UAV imagery and deep-learning techniques.

Machine learning approaches for estimation of the fraction of absorbed photosynthetically active radiation and net photosynthesis rate of maize using multi-spectral sensor

The fraction of absorbed photosynthetically active radiation (FAPAR) and the photosynthesis rate (Pn) of maize canopies were identified as essential photosynthetic parameters for accurately estimating vegetation growth and productivity using multispectral vegetation indices (VIs). Despite their importance, few studies have compared the effectiveness of multispectral imagery and various machine learning techniques in estimating these photosynthetic traits under high vegetation coverage. In this study, seventeen multispectral VIs and four machine learning (ML) algorithms were utilized to determine the most suitable model for estimating maize FAPAR and Pn during the kharif and rabi seasons at Tamil Nadu Agricultural University, Coimbatore, India. Results demonstrate that indices such as OSAVI, SAVI, EVI-2, and MSAVI-2 during the kharif and MNDVIRE and MSRRE during the rabi season outperformed others in estimating FAPAR and Pn values. Among the four ML methods of random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), and multiple linear regression (MLR) considered, RF consistently showed the most effective fitting effect and XGBoost demonstrated the least fitting accuracy for FAPAR and Pn estimation. However, SVR with R2 = 0.873 and RMSE = 0.045 during the kharif and MLR with R2 = 0.838 and RMSE = 0.053 during the rabi season demonstrated higher fitting accuracy, particularly notable for FAPAR prediction. Similarly, in the prediction of Pn, MLR showed higher fitting accuracy with R2 = 0.741 and RMSE = 2.531 during the kharif and R2 = 0.955 and RMSE = 1.070 during the rabi season. This study demonstrated the potential of combining UAV-derived VIs with ML to develop accurate FAPAR and Pn prediction models, overcoming VI saturation in dense vegetation. It underscores the importance of optimizing these models to improve the accuracy of maize vegetation assessments during various growing seasons.

Knowing the lay of the land: changes to land use and cover and landscape pattern in village tank cascade systems of Sri Lanka

Social-ecological systems (SESs) possess a great diversity of land use and land cover (LULC) types with unique assemblages of biodiversity and ecosystem services. However, LULC changes due to landscape fragmentation are emerging as major threats to the system productivity of SESs around the world. This study examined changes to LULC extent and landscape patterns in the Village Tank Cascade Systems (VTCSs) of Sri Lanka using satellite imagery and GIS techniques between 1994 and 2021. Multispectral Landsat images (5 TM and 8 OLI/TIRS) obtained from Google Earth Engine were classified using machine learning algorithms. Overall accuracies obtained were 85.9% (1994) and 88.6% (2021). The LULC change matrix and spatial pattern metrics were used to examine LULC and landscape pattern change dynamics over the VTCS landscapes. LULC change matrix results revealed that forest, which is the dominant LULC class covering 73.7% of the total land area was reduced by 206,725 ha due to transformation into agricultural (70.43%) and scrub (24.33%) lands between 1994 and 2021. Over this time landscape pattern of the VTCS has gradually changed from forest to agricultural land-dominated landscape, with forest and agricultural land types showing a significant negative correlation (p &amp;lt; 0.001; R2 &amp;gt; 0.868), particularly in the southeastern region. Landscape patterns were analysed based on eight spatial metrics calculated at both the landscape and class levels using FRAGSTATS spatial pattern analysis software. At the landscape level, the structure became more dispersed and complex in shape. Heterogeneity was noted to have gradually increased with weakening connectivity, whereas the fragmentation process had gradually accelerated. At the class level, the dominance of forest patches decreased, fragmentation and isolation increased, and connectivity and shape complexity reduced leading to the loss of fragmented forest habitats. The number of patches within the agricultural class increased and became more aggregated and complex in shape. Landscape performance indicators show that VTCSs have experienced a gradual loss of environmental sustainability. Assessment of LULC along with fragmentation can help to monitor the spatial pattern impacts that determine ecological integrity. Thus, the study provides scientific guidance for ecological restoration in degraded VTCSs to effectively improve ecological productivity.

Decadal Monitoring of Upwelling Dynamics in Satonda Island Waters Using Landsat-8 and Machine Learning Regression

Global warming and associated weather changes, notably the El Niño Southern Oscillation (ENSO), significantly impact marine ecosystems by altering water quality parameters such as chlorophyll-a (Chl-a) and sea surface temperature (SST). These changes are crucial in understanding the biogeochemical and ecological dynamics of marine environments, especially in regions affected by upwelling. This study aims to monitor upwelling events on Satonda Island, a volcanic island with unique central lake and status as a protected area using remote sensing. Utilizing Landsat-8 imagery and machine learning regression techniques—Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART)—this research evaluates the water quality in Satonda waters over a decade (2013– 2023). The RF method emerged as the most accurate in estimating Chl-a and SST, indicating its efficacy in monitoring marine ecosystems with the result (RMSE = 0.309 and 0.274). The analysis reveals seasonal upwelling patterns, characterized by decreased SST and increased Chl-a concentration, with peaks varying annually between June and November. This study highlights the crucial role of remote sensing and machine learning in monitoring the effects of climate change on marine biodiversity. It provides valuable insights into the temporal dynamics of upwelling in the shallow waters of Indonesia.

Selection of suitable fusional band combination from Sentinel-2A and UAV imagery for agricultural applications

ABSTRACT This study explores the fusion of Sentinel-2A (S2) and different bands of UAV imagery (Blue, Green, Red and Panchromatic (PAN)) to enhance spatial and spectral information for crop health monitoring. It assesses the quality of fused images, accuracy of the classified fused images and statistical analysis between the fused and S2 vegetation indices. Various fusion techniques are examined to select an optimal combination of bands. The quality of the fused images is evaluated using image quality assessment metrics and classification employs the Random Forest algorithm, assessing accuracy with metrics like F-score and Kappa Coefficient. Statistical analysis involves comparing vegetation indices from fused and S2 imagery. Notably, the fusion of UAV Green and Red bands with S2 imagery, using BT and PCA techniques, emerges as an effective combination for plant-level agricultural health monitoring. This research contributes to advancing precision agriculture techniques by leveraging multispectral imaging fusion for enhanced crop monitoring and management.

Motor Imagery Performance through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment.

This study introduces an innovative framework for neurological rehabilitation by integrating brain-computer interfaces (BCI) and virtual reality (VR) technologies with the customization of three-dimensional (3D) avatars. Traditional approaches to rehabilitation often fail to fully engage patients, primarily due to their inability to provide a deeply immersive and interactive experience. This research endeavors to fill this gap by utilizing motor imagery (MI) techniques, where participants visualize physical movements without actual execution. This method capitalizes on the brain's neural mechanisms, activating areas involved in movement execution when imagining movements, thereby facilitating the recovery process. The integration of VR's immersive capabilities with the precision of electroencephalography (EEG) to capture and interpret brain activity associated with imagined movements forms the core of this system. Digital Twins in the form of personalized 3D avatars are employed to significantly enhance the sense of immersion within the virtual environment. This heightened sense of embodiment is crucial for effective rehabilitation, aiming to bolster the connection between the patient and their virtual counterpart. By doing so, the system not only aims to improve motor imagery performance but also seeks to provide a more engaging and efficacious rehabilitation experience. Through the real-time application of BCI, the system allows for the direct translation of imagined movements into virtual actions performed by the 3D avatar, offering immediate feedback to the user. This feedback loop is essential for reinforcing the neural pathways involved in motor control and recovery. The ultimate goal of the developed system is to significantly enhance the effectiveness of motor imagery exercises by making them more interactive and responsive to the user's cognitive processes, thereby paving a new path in the field of neurological rehabilitation.

Application of High-Resolution Satellite Imagery Techniques in the Assessment of Urban Park Green Cover

Application of High-Resolution Satellite Imagery Techniques in the Assessment of Urban Park Green Cover