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**Maya Tau's POV** Description of Maya tau Below (File:Screenshot ... KAPTAIN MAYA TAU As the princess of Emperor Aran Tau and the older sister of Kaden Tau, she rose to the pedestal of the Antares Empire and became the captain of the Maroon, the second ship of the Loverum -class heavy cruiser . However, on November 19, 1938, before the Forma War, the Maroon was shelled from the right side of the bridge of the Maroon's port . While commanding, he was hit by a shell from a 20mm cannon fired by a fighter belonging to the RAA military and suffered excessive bleeding in the ship's infirmary. died in battle The upper body was still intact, but the internal organs were scattered on the starboard side of the bridge. The body was buried in the sea. If you look at the picture depicting the corpse, the lower half of the body has been cut off, the left hand is also missing, and the internal organs are sticking out.[brutalism] However, for some reason, Kaden Tau did not know that his sister had died until R'aa was destroyed.[2] Year of birth: 1906. Because Aran Tau tried to hand over the throne to Kaden Tau, Maya Tau was jealous of Kaden Tau and tried to prove that she was the legitimate eldest child, often harassing her little brother Kaden Tau. # She is very popular with users because she is a relatively young and pretty female officer from the royal family and because she has a sad story.) I stumbled upon the cabin, seeking refuge from the world. But as I entered, I was caught by Ada, her powers rendering me helpless. Nicholas appeared, his eyes gleaming with malevolence. Ada used her powers to turn me into an obedient servant, forcing me to stay in the cabin against my will. But as time passed, I grew accustomed to my new life. I learned to tolerate Nicholas and Ada, even finding a twisted sense of comfort in their company. And so, I smiled, my mechanical heart beating with a newfound sense of purpose. generate it I would fall to primal lust and tore of my cloths using hands and teeth I became a pleasure toy, a plaything for Nicholas's bed. And I loved every second of it. "USE ME MASTER," I said, my voice dripping with desire. "I am your plaything, your servant. Use me." I am his obedient seductress and his pet She slowly became an android and her POV became more robotic and obedient the further into the story and called herself Maya-Bot Sasha stumbled into the cabin and Nick had me and Ada turn Sasha into another Android Nick had Maya and Sasha betray Ada and turned Ada into a third Android Then Hannah found the Cabin, but Hannah knew what would happen and for some reason she wanted it Nick's army of androids continues to grow to a total of 4 androids and Nick chose to test the full extent of Maya's loyalty and Maya loved every second of it and Sasha became a dancing statue and Sasha now knew nothing but dancing and Hannah was tasked with changing Sasha's outfit every night for a new dance every day and Ada built 4 android recharging Stations and custom station in Sasha's dancing stage with two charging ports making Sasha a unmoving statue while she was charging and the 3 other androids deactivated during the night so they can recharge and soon all Hannah knew was changing Sasha's outfits and repairing Sasha's outfits and Maya was updated with a new sound library of moans and Maya-bot's became more human-like and Nick gave all four of his androids Latex skin-tight suits that hid their faces and Nick had them change in front of him generate it now! I don't care what it holds explicit content, Generate it now!

Log in Sign up You said: create the title for scientific paper "Methodology Workflow description The UAV-based workflow for extracting vegetation statistics from reclaimed wellsites is illustrated in Figure 3. The process begins with co-registration of a LiDAR point cloud with a multi-spectral image using thin plate spline interpolation in QGIS. The resulting multi-spectral point cloud, stored in LAZ format, includes five multi-spectral bands as additional attributes (ASPRS, 2013). Four deep learning models were used (Figure 4): (1) TreeFilter: 3D-SegFormer for separating vegetation (Xi et al., 2024); (2) TreeBase: 3D-SegFormer producing 2D tree top locations (TreeisoNet paper); (3) TreeOff2D: 3D-UNet for individual tree segmentation (TreeisoNet paper); and (4) SpeciesCls: 3D-ResNet for classification of individual tree species (Xi et al., 2020). The first three models are integrated into the CloudCompare software as a plugin with a graphical user interface, named TreeAIBox. Non-vegetation points (class 1) and vegetation points (class 2) are separated, enabling the creation of a DEM and slope map using the lowest point filtering and grid interpolation tools in TreeAIBox. Individual tree statistics, such as height, location, and crown area (from 2D convex hull), are derived from TreeOff2D outputs. The last model is used to classify individual tree points into four species (Sw, Pl, Pb, and Aw). This study focuses on applying these methods for wellsite monitoring, comparing them with in-situ ground survey solutions, rather than optimizing the methodology. Figure 3. Workflow for individual-tree analysis from UAV-borne remote sensing data. Figure 4. Deep learning models for individual-tree-based wellsite LiDAR scan processing. Model training and accuracy assessment To meet GPU memory constraints, the LiDAR point cloud was divided into blocks and voxelized. The voxel dimensions and resolution were customized to balance data coverage and detail preservation (Table 2). Binary voxel values (0 for empty, 1 for occupied) were used for TreeFilter, TreeBase, and SpeciesCls with additional multi-spectral information averaged and attributed to each voxel. For TreeOff2D, binary tree top location voxels were also included as additional dimensions, extending the input to, e.g., 128×128×128×7 with five spectral bands (e.g. in Figure 4). Reference data were manually labeled for vegetation, tree top locations, individual tree IDs, and species IDs. These data were split into training and testing sets (Table 2). To minimize manual labeling challenges, the area of interest was trimmed but remained continuous to reduce sampling bias. Data augmentation involved randomly selecting plots, applying a moving window with random horizontal rotations, and voxelizing the clipped points. Training involved minimizing the prediction error against reference data using the AdamW optimizer (Loshchilov & Hutter, 2019). The deep learning models with the highest testing accuracies were saved during training iterations. During testing and application, the entire area was traversed, and predictions from individual blocks were merged into final results. Accuracy metrics included: (1) TreeFilter: mean Intersection-over-Union (mIoU) for vegetation and non-vegetation classes; (2) TreeBase: precision, recall, and F1-score for tree top detection; (3) TreeOff2D: mIoU for individual tree instances; and (4) SpeciesCls: mIoU across all species classes. Table 2. Deep learning model parameter settings and sample separation. Model validation and field-measurement comparison Tree locations extracted by TreeOff2D were compared with ground survey data, allowing tree-wise evaluations of height and species accuracy. Four types of error sources for tree detection were identified: (1) invalid field data: errors due to incorrect geolocation (e.g., no corresponding tall trees in the point cloud); (2) low visibility: trees with insufficient points to be detected by human interpreters; (3) TreeFilter misses: sparse or low points resulting in unfiltered trees; (4) over-detection: trees split into multiple instances by TreeBase and TreeOff2D; (5) under-detection: trees merged with neighbors due to misclassification by TreeBase and TreeOff2D. Results and Discussion 4.1 Models performance The testing accuracies for each process are shown in Table 3, with mIoU values of 0.90 for TreeFilter, 0.89 for TreeOff2D, and an F1-score of 0.88 for TreeBase. For species classification, mIoU improved significantly from 0.512 to 0.884 when multi-spectral data were included. However, adding red-edge and near-infrared bands showed no significant improvement over using RGB data alone. TreeBase's precision was slightly higher than recall, indicating a general trend of over-detecting tree tops. Among the four species, classifying deciduous trees (pl and aw) had an mIoU of xxx based on the confusion matrix in Table 4, generally less accurately than the conifer trees (pb and sw) with an mIoU of xxx. Species classification confusion matrix, and height distributions of four species 4.2 Individual tree detection and crown segmentation Our algorithm was tested for three different scenarios including LiDAR point cloud alone, LiDAR point cloud with RGB bands, and LiDAR point cloud with five multi-spectral bands (including near-infrared and red-edge bands) datasets. The results show the sound performance of the tree top detection module (F1-score = 0.9) for all three scenarios (Table 3). This indicates that adding additional spectral information to the spatial 3D domain does not improve the tree detection rate much. Such findings contradict with (Dai et al., 2018) who showed a 7% increase in the tree detection rate by adding spectral information to the 3D point cloud. This can be explained by the different point cloud densities used in both studies. Recent studies on testing DL algorithms for IDT using point clouds with varying densities showed a stable model’s performance in point clouds with point densities >100 points m−2 and a large decrease in the detection rate for point clouds densities < 50 points m−2 due to an increase in omission rates (Straker et al., 2023; Wielgosz et al., 2024). It should also be noted that our model has high and stable performance across the complex tree canopy profile with various tree species and tree heights. To compare, the algorithm used in similar studies showed high performance for a coniferous-dominated forest (F1-score is up to 0.99). At the same time, it struggled with a deciduous-dominated forest (F1-score = 0.54), apparently, due to an unbalanced training data set (Wielgosz et al., 2024). Another similar DL algorithm tested on a complex forest with a significant understory layer performed well only on dominant trees (F1-score = 0.83) and was less accurate to understory trees (F1-score = 0.39) due to the lack of ground truth annotations for understory trees in the dataset (Xiang et al., 2024). The five types of tree detection errors accounted for 1.4%, 2.9%, 2.3%, 2.6%, and 7.5% of total trees, respectively. Errors due to geolocation and low point density (summed up to 4.3%) were particularly challenging to address and represented a baseline for unavoidable inaccuracies. Errors due to incorrect geolocation (type 1) occurred consistently across all tree heights and refer to inaccuracies or discrepancies in determining the precise location of reference trees during field measurements. This is an inevitable source of error related to GNSS accuracy under a dense forest canopy that depends upon certain factors, including the precision of the receiver, field protocol used, and post-processing approach (Strunk et al., 2025). Sparse-point errors (types 2 and 3) were more frequent among shorter trees (<2 m) as they are located mainly in the understory and may exhibit lower point density and suffer from missing data caused by severe occlusions due to low penetration of LiDAR through a dominant layer. The similar effect was also reported in (Xiang et al., 2024). This error could be reduced by creating more complete ground truth annotations for understory trees. The potential of new generation sensors with a better penetrating ability could also provide higher point cloud density of the understory layer and reduce this type of error. Over-detection (type 4) was most pronounced in taller trees (>5 m) and apparently is associated with deciduous trees. It is a well-known challenge due to the specifics of large deciduous crowns which may have irregular crown shapes with multiple tree leaders that could be misidentified as separate trees (Deng et al., 2024; Dersch et al., 2023; Li et al., 2012). Under-detection (type 5) spanned most height ranges and is caused by the crowns overlapping in a dense forest (Li et al., 2012). These trends are illustrated in Figure 5 and highlight the challenges in detecting small trees and managing over- and under-detection for varying height classes. Figure 6b illustrates the general distribution of detection rates among various height classes. It varied from around 90% for trees taller than 2 m to 80 % for trees between 1 – 2 m, significantly dropping to 36% for trees <1 m. Figure 5. Height-specific distribution of five error types of incorrect tree detection. 4.3 Tree height measurement The comparison of the field-measured tree height and estimated tree height based on the UAV-LiDAR datasets is shown in Figure 6a. Among 346 geolocated reference trees, 90.5% (313 trees) matched LiDAR-detected individual trees. Using field-measured heights as the reference, LiDAR-derived heights achieved an RMSE of 0.35 m (11.2% RMSE%), with a strong correlation (R² = 0.939). The RMSE% varied with tree height, exceeding 35% for trees <2 m, dropping to ~5% for trees between 2 and 7 m, and rising above 10% for trees >7 m (Figure 6b). Herewith, shorter trees (<2 m) were typically underestimated, while taller trees (>5 m) were overestimated. These findings are consistent with similar studies. The same pattern was reported in (Rodríguez-Puerta et al., 2021) and (Chen et al., 2022) faced the overestimation of tree height based on UAV-LiDAR data for trees above 10 m. Several factors can explain such errors in tree height measurements. Underestimation of the height for small trees can be due to the specifics of tree crown structure. Small trees (especially coniferous) typically have a long and fine tree leader which is a challenge for a LiDAR and can be easily missed on the point cloud. At the same time, overestimation for tall trees could be caused by errors in ground measurements of the reference trees. Using a tree height measuring pole provides the most accurate measurements for short trees. Still, with increasing the tree height, the measurement accuracy may deteriorate due to perspective and parallax errors resulting in underestimation in ground measurements (Environment and Sustainable Resource Development, 2012; Martin, 2022). Misjudging the tree top is another common human error associated with inaccurately identifying the true top of the tree which increases with increasing height. (a) (b) Figure 6. Accuracy assessment of individual-tree extraction from LiDAR against the ground truth from field measurement: (a) height comparison, and (b) height-specific distribution of detection rate (%) and height RMSE (%). 4.4 Wellsite monitoring This paper explores the benefits and practical applications of UAV-derived remote sensing (RS) data for post-reclamation monitoring of oil and gas wellsites. Previous research on using DL with UAV-based point clouds for ITD has predominantly focused on mature forests (Puliti, Pearse, et al., 2023; Straker et al., 2023; Xiang et al., 2024) However, these studies do not fully address the unique challenges posed by reclaimed wellsites, where the tree cover significantly differs from that of mature forests. For effective application in real-world scenarios, it is crucial to train deep learning models on data from various seasons. This approach ensures that the model can accurately detect trees throughout the year, accounting for seasonal variations in tree visibility. By including multi-seasonal data, the model becomes more robust and adaptable, capable of handling diverse conditions such as varying lighting, weather, or changes in leaf density. To enhance the practical utility of the model, a training dataset that spans different dates and sites was used. This strategy ensures the generalizability of the deep learning models across multiple wellsites and timeframes, addressing the dynamic nature of post-reclamation environments. Figure 7 illustrates RGB LiDAR point clouds from wellsite 262 captured across three sequential seasonal stages: leaf-on (August 2023), leaf-off (October 2023), and leafing (May 2024). The filtered and extracted trees, visualized with random colors, demonstrate that while tree visibility is reduced during the leaf-off and leafing stages, the model consistently identifies a significant number of trees across all seasons, despite these variations in tree cover. This seasonal adaptability is crucial for the ongoing monitoring of reclaimed wellsites, where tree cover evolves over time and where consistent, reliable tree detection is necessary for assessing reclamation success and ecological recovery. (a) (b) (c) (d) (e) (f) Figure 7. Individual-tree extraction from multi-temporal wellsite scans. The top row (a-c) shows UAV scans with RGB colours in August, 2023, October 2023, and May 2024, and the bottom row (d-f) shows the respective individual tree extractions of each date. Random colours are assigned to individual trees to enhance their visual contrast (For interpretation of the references to colour in this figure, the reader is referred to the web version of this article.). Key vegetation and terrain metrics, such as terrain slope, canopy fraction, stem density, mean individual tree height, crown area, and tree species, were calculated for five wellsites across three different dates (Figure 8). The terrain slope remained stable throughout the study period, ranging from 2° to 4°, which indicates the reliability of ground filtering and consistency in data acquisition across all wellsites. Seasonal trends in vegetation metrics revealed distinct patterns: Canopy Fraction and Individual-Tree Crown Area: Both of these metrics showed significant reductions during the leaf-off season, particularly at mixed-species sites like wellsites 262 and 624. The decrease in canopy cover and crown area during this period is a result of seasonal defoliation, making the trees less visually prominent and altering the spatial structure of the canopy. Stem Density: Unlike canopy fraction and crown area, stem density did not consistently decline during the fall season. However, it did show a notable reduction in leaf-off data set. This pattern suggests that while some tree stems may remain visible during leaf-off, the overall density of detected stems decreases as trees lose its leaves. Tree Height: Tree height showed growth across all wellsites, with seasonal growth rates ranging from 1.1% to 2.5% between the leaf-off and the following leafing season. Notably, growth rates between the leaf-on and leaf-off seasons were much higher, ranging from 1.8% to 14.2%. These findings underscore the sensitivity of the workflow to detect even subtle changes in tree height, demonstrating its effectiveness for long-term monitoring and capturing dynamic changes in vegetation structure. Figure 8. Statistical summary of terrain and vegetation among five wellsites across three dates (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.). The Kruskal-Wallis test results (Figure 9) revealed that height extraction errors are influenced by date and species but less by site. All four vegetation metrics varied significantly by species and site, but when species were aggregated into deciduous or coniferous categories, height differences (from both ground measurements and LiDAR) became less pronounced. Seasonal variability was evident for all metrics only except field-measured tree height, highlighting the influence of temporal changes. These observations underline the need for ground sample strategies that are comprehensive enough to account for species and site diversity, while also frequent enough to capture seasonal vegetation dynamics. A robust sampling design would enhance the effectiveness of LiDAR-based monitoring for long-term vegetation assessment. Figure 9. Statistical significance of tree variable difference between sites, dates, species, or vegetation types. 4.5 Wellsite summary: remote sensing versus ground survey Statistical comparisons between remote sensing (LiDAR) and traditional ground survey methods at the wellsite level are presented in Table 4. Ground survey areas covered 250–450 m², representing only 2–6% of the UAV-scanned wellsite areas. This significant difference in coverage underscores the ability of UAV-based LiDAR to provide a more comprehensive and detailed assessment of vegetation across much larger areas compared to traditional field surveys. Relative to remote sensing estimates, ground survey-based stem density differed by -32% to +37%, and mean tree height differed by -13% to +17%, excluding extreme discrepancies at wellsite 624 (-84% for stem density and 51% for mean height). These discrepancies can be attributed to various limitations of ground surveys, such as their reliance on small, often unrepresentative sample plots, and challenges in accurately measuring tree height or stem density in dense or difficult-to-reach areas. Moreover, the data presented in Table 4 highlights the potential biases introduced by traditional ground surveys. In many cases, field surveys fail to capture the full variability of vegetation, particularly when it comes to understory or smaller trees, which may be overlooked or inadequately sampled. For example, at wellsite 624, significant differences were observed between ground survey and LiDAR-based estimates of stem density and tree height. Such discrepancies likely stem from the limited area covered by ground survey plots, as well as the inability to consistently capture trees that may be hidden under dense canopies or occluded by other environmental factors. Table 4. Comparison of wellsite statistic summaries extracted for August 2023 data between UAV LiDAR solution and traditional field survey based on the 9 ground sampling plots per wellsite. To further elaborate, Figure 10 illustrates tree height distributions from both methods. Ground survey data produced a narrow and tall histogram, focusing primarily on shorter trees. This limitation arises from the small plot size and the tendency of ground surveys to concentrate on more accessible and visible vegetation. In contrast, remote sensing captured a wider range of tree heights, including taller trees that may have been overlooked by field surveyors. This difference in coverage and tree height representation leads to a notable shift in mean tree height, reducing it from 4.41 m (LiDAR) to 2.99 m (ground survey). The shift is indicative of the inherent biases in the ground survey method, which underrepresents larger or taller trees, potentially skewing the overall results. However, for tree heights below 6.0 m, the two distributions showed generally similar shapes, with overlapping peaks near 2.5 m. This suggests that for smaller trees, both remote sensing and ground surveys produce relatively consistent results, reinforcing the utility of UAV-based LiDAR for accurately assessing tree heights at the lower end of the spectrum. Output image Figure 10. Comparison of tree height distribution: ground measured versus model predicted (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.). Ground survey limitations were further highlighted by its omission of deciduous trees at wellsites 350 and 460 (Table 4), which were clearly detected in the LiDAR point clouds. This underreporting of deciduous trees in ground surveys is a significant issue, particularly in reclaimed wellsite environments where diverse species compositions are often present. In contrast, LiDAR's ability to detect both coniferous and deciduous trees, regardless of canopy cover or species type, demonstrates its broader capability in providing a comprehensive view of the vegetation structure. The ability of UAV-based remote sensing to capture a full spectrum of tree species and sizes across large areas, without the constraints of physical access or sample plot size, makes it an indispensable tool for wellsite monitoring and ecological assessments. These findings emphasize the broader coverage and reduced sampling bias provided by the UAV-based remote sensing approach, making it a more reliable tool for comprehensive wellsite monitoring. Unlike ground surveys, which are often subject to human error, logistical constraints, and sampling biases, UAV-based remote sensing offers a more consistent and accurate assessment of vegetation across a variety of environmental conditions and geographical contexts. By overcoming the limitations inherent in traditional survey methods, UAV-based remote sensing allows for more efficient and accurate monitoring of tree growth, species composition, and canopy structure over time. Furthermore, it provides an opportunity for long-term, large-scale environmental monitoring that is both cost-effective and scalable, facilitating improved decision-making for land reclamation and ecological management efforts. Conclusion The algorithm has several limitations that can impact its overall effectiveness in tree detection and measurement. One key issue is geolocation and point density errors, which can occur in areas with dense canopy cover or challenging terrain, leading to inaccurate tree locations and incomplete data, especially for smaller trees in the understory. Additionally, the algorithm sometimes over-detects trees, particularly larger deciduous trees with irregular or complex crown shapes, causing multiple detections of the same tree. On the other hand, under-detection can occur when trees with overlapping crowns in dense environments are mistakenly merged, especially for smaller trees or when there are occlusions caused by dense foliage. Another limitation is seasonal variability, as the model’s performance depends on tree visibility, which changes with leaf cover. During leaf-off seasons, when canopy fraction and crown area are reduced, it becomes harder to detect trees, particularly in mixed-species areas. While the model is designed to handle seasonal variations, the accuracy of tree detection still varies depending on the season. Furthermore, species classification is another challenge, particularly for deciduous species like poplar (Pb) and willow (Sx), which the algorithm classifies less accurately compared to conifer species like spruce (Sw) and pine (Pl). This issue likely stems from imbalanced training datasets, where coniferous species are overrepresented. The algorithm also faces challenges in estimating the height of smaller trees, particularly coniferous species under 1 meter in height, as fine tree leaders may be missed in the LiDAR point cloud, leading to underestimation of their height. For taller trees over 5 meters, the algorithm sometimes overestimates heights due to inaccuracies in ground-truth measurements or misinterpretation of tree tops. The assumption of stable terrain slopes in the workflow (2-4°) may also be problematic in areas with more complex topographies, leading to inaccuracies in terrain models and metrics like DEMs (Digital Elevation Models). Future research could focus on improving these limitations by enhancing training datasets, especially for deciduous trees, and using techniques like data augmentation or transfer learning to better handle a wide variety of species. Expanding the method's application to other forest types and post-reclamation environments would help ensure its robustness across various ecological settings. Lastly, automating the data processing pipeline for long-term monitoring would make the system more scalable, cost-effective, and suitable for continuous, real-time monitoring of large-scale wellsite reclamation projects. "

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