dataesr/scientific-paragraphs-categorization huggingface dataset:
We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.
Kelbe et al. [25]  proposed an approach to detect stem points from the low-resolution single-scan TLS datasets based on planar projection and the planar point density distribution. The classification accuracies (i. e. , the percentage of correctly detected stem points) reported in this study varied from 8% to 83% depending on the plot condition. Liang et al. [26]  proposed a method to identify stem points using the local geometrical features, i. e. , the flatness and normal direction. A point was recognized as a stem point if it has a low variance in one direction in the local coordinate system and has a nearly horizontal normal vector in the real-world coordinate system. Lalonde et al. [27]  introduced a method that characterizes a point cloud using three salient features, i. e. , scatter, linear, and surface.
The reported overall classification accuracy in their study ranged from 80% to 90%. Chen et al. [32]  also applied the SVM classifier along with various features, such as local geometric features, intensity value, and normal vectors. The previous methods based on the local geometric features have achieved satisfactory classification accuracies, although there is still room for improvement. A common classification strategy of the previous methods is to identify the stems from TLS data point by point. Compared with the segment-wise classification, these point-based methods require more computing resources  [9]. These computational burdens are mainly due to two reasons. First, the geometric features need to be calculated at each point by its surrounding points within a neighborhood. Second, the geometric features are varying with the radius of the neighborhood.
In a forest environment, Polewski et al. [38]  proposed an approach that combined a point-based and a segment-based method to detect fallen tree stems from TLS datasets. Amiri et al. [39]  applied Polewski 's method to detect tree stems from very-high density airborne laser scanning datasets. However, they employed a supervised method in the stage of point-based classification by using multiple features, which increased the complexity of the algorithm and turned out to be a time-consuming approach. Olofsson et al. [40]  recognized stem points by calculating the flatness feature of a subset of a point cloud within a voxel cell instead point by point, which has a much higher computational speed, but they voxelized the point cloud rather than segmented the point cloud. In summary, the segment-based method for point cloud classification was widely used in urban scenes.
Terrestrial Laser Scanning Data The TLS datasets used in this study were obtained from seven sample forest plots, of which six sample plots were public datasets and provided by the international TLS benchmarking project  [41]. The TLS benchmarking project was conducted by the Finish Geospatial Research Institute to comprehensively evaluate the capacity of the TLS data for forest inventory and the algorithm performances of the data processing for tree attribute extraction. Eighteen partners from Asia, Europe, and North America participated in the project and submitted their retrieved tree attributes for evaluation. The TLS data of the TLS benchmarking project was collected from twenty-four sample plots with different tree species, developing stages, stem densities, and abundance of understory vegetation in southern boreal forests in Evo, Finland (61.
19 • N, 25. 11 • E), and six of them with field reference data are open for nonprofit research purposes. These sample plots were classified into three complexity categories, i. e. , easy, medium, and difficult, according to the density of stems and understory vegetation, which also reflect the level of complexity in TLS data processing. The category "easy" represented sample plots that had clear stem visibility with minimal understory vegetation and low stem density, "medium" represented sample plots with moderate stem densities and sparse understory vegetation, and "difficult" represented sample plots that had high stem densities and dense understory vegetation. These sample plots were all located in flat areas, each of them had a fixed size, 32 × 32 m, and was scanned both in single-and multi-scan mode using a Leica HDS1600 (Leica Geosystems AG, Heerbrugg, Switzerland) terrestrial laser scanner.
The multi-scan TLS datasets consist of five scans located at the center and in four quadrantal directions of each sample plot, and the single-scan datasets were just the center scan of multi-scan datasets. The dominant tree species are Scots pine (Pinus sylvestris L. ), Norway spruce (Picea abies L. Karst. ), silver birch (Betula pendula Roth), and downy birch (Betula pubescens Ehrh. ). The trees in the six public sample plots usually had straight up stems. To verify the applicability of the proposed method to various stem forms, we introduced another 50 × 50 m sample plot located in a pure Chinese scholar tree (Styphnolobium japonicum) forest in Beijing, China (39 • 46 N, 116 • 13 E), hereafter referred to as plot 7. The Chinese scholar tree generally had irregular and diverse stem forms, as shown in Figure  1.
The slope of this forest area was less than 5 •. The stem density of plot 7 was about 366 stems/ha. The mean diameter at breast height (DBH) and tree height was 28. 1 cm and 12. 7 m, respectively. Also, plot 7 had sparse understory vegetation. TLS dataset of the plot 7 was acquired in the multi-scan mode using a Riegl VZ-1000 (Riegl GmbH, Horn, Austria) terrestrial laser scanner; five scans were implemented at the center and at the center and in four quadrantal directions. There were about 128 million points contained in the TLS dataset of plot 7. Table  1  displays the characteristics of the seven sample plots and TLS datasets. The trees in the six public sample plots usually had straight up stems. To verify the applicability of the proposed method to various stem forms, we introduced another 50 × 50m sample plot located in a pure Chinese scholar tree (Styphnolobium japonicum) forest in Beijing, China (39°46'N, 116°13'E), hereafter referred to as plot 7.
The Chinese scholar tree generally had irregular and diverse stem forms, as shown in Figure  1. The slope of this forest area was less than 5°. The stem density of plot 7 was about 366 stems/ha. The mean diameter at breast height (DBH) and tree height was 28. 1cm and 12. 7m, respectively. Also, plot 7 had sparse understory vegetation. TLS dataset of the plot 7 was acquired in the multi-scan mode using a Riegl VZ-1000 (Riegl GmbH, Horn, Austria) terrestrial laser scanner; five scans were implemented at the center and at the center and in four quadrantal directions. There were about 128 million points contained in the TLS dataset of plot 7. Table  1  displays the characteristics of the seven sample plots and TLS datasets. Note: The complexity of forest plots was defined in the TLS benchmarking project according to the stem density and abundance of understory vegetation.
The Reference Data for Validation Along with the TLS data of the six sample plots, the TLS benchmarking project also provided the reference data of stem locations, diameter at breast height (1. 3m, DBH), and tree heights, which was used in this study to validate the accuracy of stem detection at the attribute level. The referential tree stem locations were gathered by integrating manual measurements from the multi-scan datasets and in the field. First, a preliminary stem location map was manually measured from multi-scan TLS data for trees having a complete point cloud. Then the preliminary stem location map was verified in situ, and the location of omitted stems was determined via the distances and directions of the omitted stem to its four neighboring known stems. The DBH was measured for each tree using steel calipers in the field.
The Reference Data for Validation Along with the TLS data of the six sample plots, the TLS benchmarking project also provided the reference data of stem locations, diameter at breast height (1. 3 m, DBH), and tree heights, which was used in this study to validate the accuracy of stem detection at the attribute level. The referential tree stem locations were gathered by integrating manual measurements from the multi-scan datasets and in the field. First, a preliminary stem location map was manually measured from multi-scan TLS data for trees having a complete point cloud. Then the preliminary stem location map was verified in situ, and the location of omitted stems was determined via the distances and directions of the omitted stem to its four neighboring known stems. The DBH was measured for each tree using steel calipers in the field.
Tree height was measured with Vertex 3. 0 (Haglöfs, Långsele, Sweden) to a resolution of 0. 1 m. The expected accuracy of tree-height measurement was 0. 5 m. For more details about the method of collecting referential tree level attributes, please refer to Liang et al. [41]. In order to validate the accuracy of stem point extraction at the point level, we used the manually extracted stem points as reference. The reference point clouds were classified into two parts, i. e. , the stem points and non-stem points. It should be noted that the ground points in the reference TLS datasets have been filtered using an automated method, the cloth simulation filtering (CSF) proposed by Zhang et al. [42]. The details for the implementation of the CSF will be described in the following section. In the referential TLS data, the stems of understory trees that the DBHs were not greater than 5 cm and shrubs were considered as non-stem points to match the demand for forest inventory.
Remote Sens. 2018, 10, x FOR PEER REVIEW 5 of 19 In order to validate the accuracy of stem point extraction at the point level, we used the manually extracted stem points as reference. The reference point clouds were classified into two parts, i. e. , the stem points and non-stem points. It should be noted that the ground points in the reference TLS datasets have been filtered using an automated method, the cloth simulation filtering (CSF) proposed by Zhang et al. [42]. The details for the implementation of the CSF will be described in the following section. In the referential TLS data, the stems of understory trees that the DBHs were not greater than 5 cm and shrubs were considered as non-stem points to match the demand for forest inventory.
Finally, the ground points were detected through a pre-set distance threshold between points and the final settled cloth. The CSF has been integrated into CloudCompare  [43]  as a third-party plug-in. Therefore, we implemented the CSF in CloudCompare in this study. First, the TLS point clouds were imported into CloudCompare, and the CSF was directly executed without any preprocessing steps. The cloth resolution and distance threshold (classification threshold) of the CSF were both set to 0. 1 m, and the max iterations were set to 50 for all sample datasets. Then, the off-ground point clouds produced by the CSF were delivered to the next step as the inputs.
Finally, the ground points were detected through a pre-set distance threshold between points and the final settled cloth. The CSF has been integrated into CloudCompare  [43]  as a third-party plug-in. Therefore, we implemented the CSF in CloudCompare in this study. First, the TLS point clouds were imported into CloudCompare, and the CSF was directly executed without any preprocessing steps. The cloth resolution and distance threshold (classification threshold) of the CSF were both set to 0. 1 m, and the max iterations were set to 50 for all sample datasets. Then, the off-ground point clouds produced by the CSF were delivered to the next step as the inputs. The B&F points thinning procedure aims to reduce their point density through removing a part of B&F points and, in the meantime, retaining the stem points as much as possible.
After the B&F points thinning, the point clouds were more likely to be separated into segments that contained points belong to a single class, as shown in Figure  6. In addition, the grid size of the CC segmentation should be small enough for the generation of segments. However, too small of a grid size may lead to producing too many small segments that would impact not only the time efficiency but also the classification accuracy of the proposed method. As a tradeoff, the grid size was set as 1 cm and applied for all datasets in this study. We implemented this step using a batch script written in MATLAB (R2017b, The MathWorks, Ins. ) with the connected component segmentation function provided by CloudCompare (2. 10. alpha). In particular, we modified the source code of connected component segmentation function of CloudCompare to make the calculation of the H-W ratio possible.
After the B&F points thinning, the point clouds were more likely to be separated into segments that contained points belong to a single class, as shown in Figure  6. In addition, the grid size of the CC segmentation should be small enough for the generation of segments. However, too small of a grid size may lead to producing too many small segments that would impact not only the time efficiency but also the classification accuracy of the proposed method. As a tradeoff, the grid size was set as 1 cm and applied for all datasets in this study. The CC segmentation could not be directly applied to the TLS data of forest stands for the classification purpose because of the high connectivity between the stem and B&F points. Therefore, the step of B&F points thinning was regarded as a prerequisite that makes the CC segmentation effective for the stem point extraction.
After the B&F points thinning, the point clouds were more likely to be separated into segments that contained points belong to a single class, as shown in Figure  6. In addition, the grid size of the CC segmentation should be small enough for the generation of segments. However, too small of a grid size may lead to producing too many small segments that would impact not only the time efficiency but also the classification accuracy of the proposed method. As a tradeoff, the grid size was set as 1 cm and applied for all datasets in this study.
For the multi-scan datasets, stem segments usually contain hundreds of thousands of points; we set 1000 as the NoP threshold. For the single-scan datasets, stem segments usually contain tens of thousands point, so the NoP threshold was set as 100 for them. The NoP thresholds were set as ten times smaller than the NoP of regular stem segments because the occlusions in forest stand may lead to fragmentation of the stem segments. A larger NoP threshold can filter out more B&F segments but also some small stem segments.
Segmentation of Individual Stems and Stem Mapping To obtain the individual stems, we used the CC segmentation algorithm again on the point clouds of the detected stems from previous steps. However, in this step, the grid size for the CC segmentation was set based on the distance between the two closest stems. Therefore, a larger grid size (0. 1 m) than the previous step was adopted for all TLS datasets. It was easy to segment the stem point cloud into individual stems since the vast majority of the non-stem points were removed. For each stem, the stem diameters were measured at the different target heights above the ground, starting at 0. 65 m and then by 1. 3 m, 2 m, and then for every next meter, until the maximum measurable height was reached  [26]. A randomized Hough transform (RHT) for the circle detection  [50]  was applied to measure the diameters at different heights along the stem.
Methods of Accuracy Evaluation The accuracy of stem-point extraction was assessed by counting the percentage of error-classifying points against the reference TLS datasets. Three types of error were quantified in this study: type I error, type II error, and total error. The type I error (T. I) was defined and calculated using the number of stem points that were wrongly removed divided by the number of referential stem points, which is also known as the omission errors. The type II error (T. II) was the number of B&F points that were wrongly classified as stem points divided by the number of referential B&F points, which is also known as the commission errors. The total error (T. E. ) was the number of wrongly classified points divided by the total points. Finally, the total accuracy (T. A.
The Accuracy of Stem Point Extraction Table  2  shows the accuracy of the stem point extraction, i. e. , the type I error (T. I), type II error (T. II), total error (T. E. ), and total accuracy (T. A. ), which have been defined in Section 2. 5. The results showed high accuracies of the stem point extraction across all sample plots both for single-scan and multi-scan TLS datasets. The mean total accuracy of the multi-scan datasets was 96. 29%, and the accuracies of different plots were very consistent as the standard deviation of the total accuracies was only 0. 53%. The mean total accuracy of the single-scan datasets was 95. 81%, which was slightly lower than that of multi-scan datasets, so does the consistency of the total accuracy between different plots (2. 16%). Since the sample plots had different complexities and stem forms, the consistency of the total accuracy between them indicates that the proposed method was adapting well to the different forest conditions and stem forms.
The Accuracy of Stem Mapping Table  3  shows the accuracy of stem mapping of the sample plots using both multi-and single-scan TLS datasets. The results demonstrated that the accuracy of stem mapping using multi-scan TLS datasets was distinctly superior to using single-scan TLS datasets from all accuracy indicators. The complexity of the forest stand, i. e. , the stem density and abundance of understory vegetation, severely affected the accuracy of stem mapping regardless of the scan mode of TLS data collection. The completeness of stem detection consistently decreased with the increasing of forest stand complexity, and so did the mean accuracy of stem detection. However, the correctness of stem detection remained high and not relevant to the scan mode and forest stand complexity. This was mainly because the non-stem points had been accurately removed, and the correctness of stem detection was not likely influenced by the non-stem components in the forest plots.
The correctness was not 100% in some plots because a few of the stems of understory vegetation were wrongly recognized as tree stems. The results also showed accurate estimates of stem locations and DBHs. The mean bias of stem location was 2. 22 cm and 4. 12 cm for the multi-and single-scan datasets, respectively. The DBH was underestimated in all sample plots using both scan modes. The mean underestimation was 1. 42 cm and 2. 85 cm for the multi-and single-scan datasets, respectively.
Conclusions In this study, we proposed a novel and simple segment-based approach to detect tree stems from plot-level TLS data. The proposed method achieved comparable accuracies not only for the stem point extraction but also for the stem attribute estimation. Our method was applicable in various forest conditions with different species, stem densities, the abundance of understory vegetation, and stem forms. Our work verified the effectiveness of connected component segmentation for the classification of dense point cloud with the assistance of points thinning using the curvature feature in forest scenes. The method we proposed in this study is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data. However, the variety of TLS data and forest plots were still limited in this study.
19 Figure 7. 197 Figure 7. Flowchart of the stem point extraction based on connected component (CC) segmentation, taking the multi-scan dataset of the plot 1 as an example. (A) The preprocessed point cloud after ground filtering, and branch and foliage thinning. The following CC segmentation was implemented based on it. (B) The removed small segments that the number of points (NoP) per segment less than the threshold. (C) The high-probability stem segments with a NoP per segment larger than the threshold. (D) The removed segments with a height-to-width ratio less than the threshold. (E)The finally detected stem points by the CC segmentation method. In (A), (B), (D), and (E), the colors were randomly assigned to the points according to their X coordinate. In (C), the colors were randomly assigned to each segment, and the black boxes were the boundaries of each segment.
Finally, a classification of the scene is obtained as output. Trainable weights in our hybrid analog-digital ANN pipeline are both in the analog and the digital layers and highlighted in green. During training, these are jointly optimized via error back-propagation. the use of a temperature parameter that supervises the training, gradually driving the physical weights from a continuous to a discrete binary distribution  [12]. The detailed implementation thereof is outlined in Section B. 2 of the Supplemental Material. Note that we ultimately only have to formulate an analytical forward model; the fact that this model contains coupling effects and binary weight constraints does not bring about any further complications in our scheme. The weights are trained using 60, 000 sample scenes from the reference MNIST dataset, as detailed in Section B.
A. Sensing Performance We begin by considering a single realization with M = 4 measurements and N = 64 metamaterial elements per metasurface. The dipole moments and scene illuminations corresponding to the four learned metasurface configurations are displayed in Fig. 3(a). The performance metric to evaluate the sensing ability is the average classification accuracy on a set of 10, 000 unseen samples. The confusion matrix in Fig. 3(b ) specifically shows how often a given digit is classified by the ANN as one of the ten options. The strong diagonal (corresponding to correctly identified digits) reflects the achieved average accuracy of 92. 5%. Note that the diagonal entries are not expected to be equally strong (e. g. the (1, 1) entry is a bit stronger), since the test dataset does not include the exact same number of samples from each class.
Then experimentally the radiated fields for a few different metasurface configurations are measured and a cost function is defined to minimize during training the difference between the measured and expected radiated fields. By the end of the optimization, a set of parameter values will have been learned that optimally matches the experiment. Furthermore, for practical applications it may be of interest to achieve a certain robustness to fluctuations in the calibration parameters such as geometrical details of the experimental setup  [14, 74, 75]. By including random realizations of major calibration parameters within the expected range of fluctuation during the training, the network can learn to be invariant to these variations  [14]. Ultimately, this enables the transfer of knowledge learned on synthesized data to real-life setups without additional training measurements.