dataesr/scientific-paragraphs-categorization huggingface dataset:
For the time being, this is the first international benchmarking of TLS-based forest inventories. The investigation on TLS performance is carried out from two different perspectives: first, from the TLS data point of view, i. e. , the impact of the forest stand conditions and the data acquisition methods on the accuracy and completeness of the point cloud of a sample plot and, consequently, on the results of attribute extraction of an algorithm; and second, from the aspect of the algorithms, i. e. , to what extent can the recent algorithms reach the best extraction of essential forest attributes from TLS data. The forest sample plots in the benchmarking project are selected by foresters to reflect different stand conditions in boreal forests. Considering the development stage, stem density, and density of the sub canopy vegetation, as well as the species composition in the forest stands, sample plots are classified into three complexity categories, i.
e. , "easy", "medium", and "difficult", which also reflects the level of complexity in the TLS data processing. Both single-and multi-scan approaches are employed to acquire the sample plot TLS datasets. The TLS dataset was disseminated to all the benchmarking project partners, who processed the data utilizing their own algorithms and delivered the required products. All of the partners results were then evaluated using a standard evaluation procedure, so a comprehensive understanding can be achieved on the capacity of recent algorithms for extracting important forest attributes from TLS data. In particular, the influence of forest conditions and data acquisition methods on the algorithm performance can be investigated and interpreted from a practical perspective. Eighteen partners from Asia, Europe and North America delivered the required results after processing the single-and multi-scan TLS datasets of the 24 sample plots.
, 2013; Hackenberg et al. , 2015; Ma et al. , 2016; Yang et al. , 2016; Wang et al. , 2016a; Xi et al. , 2016; Koreň et al. , 2017; Trochta et al. , 2017). This paper summarizes the benchmarking project's conceptual schema and reports the findings of the project. Section 2 describes the benchmarking project's main objectives and fundamental concepts to support the main benchmarking objectives. Descriptions about the common TLS datasets of the forest sample plots representing different forest stand situations for the benchmarking are given in Sections 3. 1 and 3. 2, which explain the variety in the accuracy of tree attribute estimations across forest complexity categories, across algorithms and across the TLS-measurement approaches. The reference information and the evaluation procedures, which lay down the bases of the benchmarking, are detailed in Sections 3.
3 and 4. New evaluation criteria for tree attribute estimates are established to analyze the TLS performances. Section 5 overviewed participants' algorithms, which were shortly summarized in Appendix A. Section 6 of this paper illustrates the evaluation results of the algorithms on the criteria utilizing the single-and multi-scan TLS data of the sample plots. In-depth analyses comparing the results in Section 7 reveals the achievements and remaining challenges of recent studies, providing recommendations and paving the way for further studies and applications in the field. Section 8 looks into the future of appling TLS in forest modelling and recommonds the road map of the algorithm design. Section 9 gives the conclusion. Furthermore, since static TLS provides the plot-level point cloud with spatial precision and detailed richness that surpasses all other contemporary terrestrial point cloud technologies, e.
The project The TLS benchmarking project was launched in 2014 by EuroSDR and led by the Finnish Geospatial Research Institute (FGI). The FGI is responsible for the benchmarking processes' architecture; the development of the evaluative criteria and procedures; the collection of participants; and the implementation, coordination and dissemination of the project. Forest researchers from the University of Helsinki (UH) selected the sample plots and measured tree attribute using calipers and clinometers in the field. The TLS data and field measurements were a joint effort of FGI and UH. The benchmarking project's targeted participants include national mapping agencies, companies, universities and research organizations, who develop their own processing methods or modify existing methods. Meanwhile, the project is open for techniques that are in the development phase.
Conceptual schema The benchmarking is carried out from two equally important perspectives: the capacity of the TLS data to digitize the forest plots and the performance of the data processing algorithms for attribute extractions. TLS digitization capacity in recording forest is influenced by the stand condition and scanning pattern, which determine what can best be achieved by a particular feature extraction method. In evaluating an algorithm's performances, two major tasks are to establish a standardized criterion and to develop an evaluation procedure. This section details the benchmarking design.
To evaluate the capacity of the TLS to digitize a forest plot The strength of TLS in forest field inventory lies in its capacity to record the forest environment automatically, accurately and rapidly. Two essential factors addressing the TLS-data quality are the spatial precision and completeness of tree information in the point-cloud data. The spatial precision is determined by the system calibration and by the registration of multi-scan data if applicable, which are typically sufficiently accurate for forest applications. The tree-information completeness is determined by the forest conditions and the field-inventory design but is however not guaranteed. The forest stands' complexity, the scanning patterns applied in the field and the distance/geometry between a tree and the scanning position(s) are the issues that determine the completeness of trees in the point-cloud data of a forest sample plot.
Datasets The data acquisition approaches are designed to support the main objectives of the benchmarking project. Thus, 24 sample plots were selected from varying forest-stand conditions representing different developing stages, stem densities and abundance of sub canopy growth in boreal forests and classified into three complexity categories. The amount of plots balanced the requirement of large amount of experimental data and the costs of data collection. The forest plots are scanned from 5 positions and the data for processing are delivered in both single-and multi-scan format. Reference datasets for the benchmarking were collected by integrating manual measurements from the TLS data and the conventional field measurements.
The category "Easy" represents a clear visibility with minimal understory vegetation and low stem density (∼600 trees/ha); "Medium" represents sample plots with moderate stem densities (∼1000 trees/ha) and sparse understory vegetation; and "Difficult" represents those plots having high stem densities (∼2000 trees/ha) and dense understory vegetation. TLS data completeness in the three categories is expected to decrease as the complexity increases. Reference data of the sample plots were collected through an integration of field inventories and manual measurements from TLS data, which was conducted between May and August 2014 and detailed in Section 3. 3. The sample plots' statistics are summarized in Table  2 , where the plot attributes' mean and standard deviation values are presented by complexity categories.
The TLS data of the sample plots The sample plots were scanned in April/May 2014, using a Leica HDS6100 (Leica Geosystems AG, Heerbrugg, Switzerland) terrestrial laser scanner. The scanner measure distances with a continuous wave of 650-690 nm. The field of view is 360°× 310°and the distance measurement accuracy is ± 2 mm at 25 m from the scanner. Data acquisition used a "High Density" mode. The angle increment is 0. 036°in both horizontal and vertical directions, which gives a point spacing of 15. 7 mm at 25 m from the scanning location in both horizontal and vertical directions. A full-field-of-view scan takes approximately 3 min. The data acquisition speed is highly relevant to the forest structure. Per day, 3-7 sample plots were measured including scanning setup, 5 scans per plot and transportation between plots.
Therefore, TLS data were acquired using multi-scan approaches, and the project's partners had the option to process the registered multi-scan data and the single-scan data from the plot center. According to practical experience, the number of TLS-acquisition positions is a trade-off between the cost of field work (e. g. , time and expense) and the data quality. In this project, five scanning positions, which is a typical setup in the multi-scan approach, was employed, because it normally leads to a merged TLS point cloud covering all trees within a forest plot and balances the completeness of tree information with cost and labor intensity. Artificial spheres, i. e. , approximately six in each plot, with a constant radius of 198 mm were set up as reference targets throughout the plot for data registration.
For each sample plot, all five scans were registered using targets and merged as multi-scan TLS data with an average registration accuracy of 2. 1 mm; the center scan was employed as single-scan TLS data. Examples of test data in the single-and multiscan TLS in the three complexity categories are presented in Fig. 7. In the TLS point cloud, the complexity category 'Easy' typically has good visibility for both single-and multi-scan TLS data. The visibility or completeness of trees in the point cloud of the complexity category 'Difficult' can be low, even in multi-scan TLS, due to the heavy occlusion effects created by the dense stands. TLS and reference data from six test plots, i. e. , two from each complexity categories in both single-and multi-scan, are open for nonprofit research purposes.
Methods of evaluation The partners of the benchmarking project are required to deliver their extraction results for the criteria, i. e. , the DTM, tree locations, tree height, DBH, and stem curve, of each sample plot utilizing the TLS datasets of the 24 sample plots. Partners' results are evaluated respecting the relevant reference data described in Section 3. 3 and utilizing standardized evaluation procedures defined in this section. 4. 1. The accuracy of the DTM Partner DTMs were evaluated using the 'Output control report' tool of the TerraScan software, which is designed for elevation comparison between laser ground points and the known ground control points. For each sample plot, the reference DTM was employed as the ground control data, and the DTM from partners was compared against the reference DTM.
Without extrapolation, the best achievable RMSE values of the DTM (FGI) are 0. 10 m (92. 5% coverage), 0. 14 m (87. 5% coverage), and 0. 16 m (66. 4% coverage) in easy, medium and difficult plots, respectively, with the single-scan data. In contrast, when high DTM coverage is pursued, the best achievable RMSE (RILOG) values are 0. 12 m (99. 7% coverage), 0. 24 m (99. 8% coverage), and 0. 27 m (95. 9% coverage) in easy, medium and difficult plots, respectively, as shown in Fig. 11. The application of the multi-scan approach can reduce shadows on the ground; therefore, high accuracy can be expected without losing the coverage of the DTM. Seven out of sixteen algorithms, e. g. , TUWien, Table  3  Brief summary of algorithms in the international TLS benchmarking for forest inventories. Refers to the TLS dataset, which has been processed for the benchmarking; "both" means both single-and multi-scan data are processed, "single" means only single-scan data are processed.
2 The level of automation of the algorithm: "A" is fully automated; "S-A" is semi-automated; "M" is manual. 3 The parameter settings for different sample plots and different TLS datasets: "U" means the universal parameter setting for all sample plots and all datasets; "D" means different parameters are applied for different sample plots, and single-and multi-scan datasets. ∖indicates that no relevant processing is applied. provide similar DTM results in terms of accuracy and coverage from the multi-scan data. The average RMSE and coverage of DTM across the seven algorithms with similar accuracies are 0. 05 m (99. 7% coverage), 0. 08 m (99. 6% coverage), and 0. 10 m (99. 7% coverage) in easy, medium and difficult plots, respectively. For the seven methods, the differences in the DTM accuracies between different stand-complexity categories are moderate, indicating that the algorithms for DTM generation are well designed.
Once a tree is correctly detected, which implies that the stem is recorded with a satisfactory TLS data (related to point coverage, distribution, number), the estimation of the DBH is reliable. For the tree height estimation, the results are at the 3-5 m RMSE level, and there is no clear difference between the methods. The results of this benchmarking also confirm that being limited by the view-point, TLS has limited capacity for measuring tree heights in forested conditions, except in simple cases. The accuracy of tree-wise stem curve estimations is at the 1-3 cm RMSE level for the best results, and the level is the same for both singlescan and multi-scan datasets. The bias of the tree-wise stem curve estimation is stable in different stand complexity categories and data acquisition approaches, which are close to zero for the best results.
In such a context, the international TLS benchmarking project was launched in 2014. The benchmarking project was designed to inspect TLS performance from the perspectives of the data acquisition, the algorithm development, and the forest stand conditions. The benchmarking data include 24 sample plots selected by foresters and classified into three complexity categories, i. e. , easy, medium, and difficult, considering the complexity of forest conditions. The TLS data were collected utilizing both single-scan and multi-scan approaches on each plot, representing the highest field measurement efficiency and the best data quality of the terrestrial point cloud. The bechmarking project assembled eighteen partners worldwide to participate. Algorithms for TLS-based forest modeling from the partners, including two commercial software, were required to process identical TLS datasets and to deliver a common set of results, including the DTM, the tree map, the height, and the DBH as well as the stem curve of each individual tree at the plot level.
Points whose mean distance to the centroid of the neighborhood was larger than an internal defined threshold were considered as outliers and removed from the dataset. The threshold was derived from the global mean distance and standard deviation. After noise removal, the tree height was calculated from the lowest to the highest point. DBH was estimated from the points of an individualized tree by circle fitting in a 20 cm height bin. Points that deviated most from a fitted circle were considered noise and removed for DBH estimations through RANSAC. The tree location was the center of the circle at breast height. A stem curve was calculated by fitting a circle to the projected trunk points at 20 cm height bins at different heights again using RANSAC. The stem curve was estimated until the end of the trunk.
Horizontal cross-sections with a 5 cm thickness were extracted for all given heights above the ground. For all datasets, points within cross-sections that do not represent stems were manually filtered out, and then the points in cross-sections were grouped. Tree positions and diameters were estimated using a least-square circle-fitting algorithm  (Koreň et al. , 2017). Tree diameters were calculated in horizontal cross-sections at all heights above the ground. The stem curve was constructed by connecting cross-sections of each single tree; the cross-sections at other heights were assigned to the nearest crosssection at the breast height. Position of a tree was defined by the circle center in the cross-section at the breast height. Tree height was calculated as the difference between DSM and DTM at the tree position.
Acknowledgements The authors would like to thank financial aid from the  Finnish Academy projects "Centre of Excellence in Laser Scanning Research (CoE-LaSR)  ( 272195) " and from  European Community 's  Seventh Framework Programme  ( [FP7/2007([FP7/ -2013]])  under grant agreement No. 606971. Jan Hackenberg  would like to thank the financial support from the  DIABOLO  project from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  633464. Group University of Lethbridge would like to thank the contribution of  Prof. Cristopher Hopkinson.
Results Identification of a MtSUNN-related LRR-RLK showing an enhanced expression in symbiotic nodules, MtNRLK1. The full amino acid sequence of MtSUNN was blasted against the predicted proteome of M. truncatula (BlastP against the Mt4. 0 database, John Craig Venter Institute [JCVI], http://jcvi. org/medicago/). For nine of the ten best hits, the expression pattern was available at the Medicago Gene Expression Atlas (MtGEA) (http://mtgea. noble. org/v3/) and amongst these, only one gene (Medtr5g090100, M. truncatula genome v4. 0), showed a nodulation-enhanced expression profile and was therefore designated MtNRLK1 (for Noduleinduced RLK1). A similarity tree was designed using MtNRLK1 and the ten most closely related proteins from Arabidopsis and M. truncatula (Fig. 1 ), which all belonged to the subclass XI.
MtNRLK1 spatial expression pattern is divergent from MtSUNN in nodules. Spatial expression patterns of MtNRLK1 in uninoculated roots and developing nodules were investigated by promoter:GUS analyses and in situ hybridizations. A 2-kb region upstream of MtNRLK1 was fused to the uidA gene to monitor transcriptional regulations using a GUS assay. In uninoculated roots, MtNRLK1 showed a very low expression in the stele, an expression domain similar to the one of MtSUNN as previously reported  37  (Fig. S3 ). At early stages after rhizobial inoculation, pMtNRLK1:GUS activity was observed within the incipient nodule primordia (Fig. 3A, D ) but not in infected root hairs, in agreement with Breakspear et al. 38  and Jardinaud et al. 39  transcriptomic datasets. Root sections revealed an expression at the base of the nodule primordium (Fig.
3B, E ). In elongated nodules, a pMtNRLK1:GUS activity was detected in the apex, with a higher expression in the proximal part of the meristem and in the differentiation/rhizobial infection zone (Fig. 3C, F ). In situ hybridizations were alternatively used to detect MtNRLK1 transcript accumulation in differentiated nodules, revealing an expression pattern similar to the one obtained with the promoter:GUS fusion (Fig. S4 ). The MtNRLK1 pattern is therefore divergent from the MtSUNN pattern initially reported in Schnabel et al. 37  and that is fully consistent with our observations linking MtSUNN expression to root (Fig. 3J ) and nodule vascular tissues (Fig. 3K ), but not to central or apical tissues of round-shaped young nodules (Fig. 3G, H, J, K ) or of elongated nodules (Fig. 3I, L ). Importantly, the divergence between these two expression patterns is independently demonstrated by "digital in situ" data generated from RNAseq transcriptomes of laser dissected nodule central tissues  36  (Fig.
Discussion The aim of this study was to identify and characterize subclass XI LRR-RLKs closely related to SUNN showing an enhanced expression during M. truncatula nodulation. Amongst the 10 closest SUNN proteins, only MtNRLK1 showed a symbiosis-related expression pattern. MtNRLK1 is most closely related to the A. thaliana AtBAM3 receptor while MtSUNN is the closest M. truncatula homolog of AtCLV1  21. As both AtBAM3 and AtCLV1 function in SAM homeostasis by regulating stem cell identity  [40] [41] [42]  , we have made the hypothesis that MtNRLK1 might be involved in controlling the balance between cell proliferation and differentiation within the nodule potentially in relation with MtCLE12 and MtCLE13 peptides previously shown to be expressed in differentiated nodules  16. MtNRLK1 transcripts were detected at the base of nodule primordia, which accordingly to the nodule fate map proposed by Xiao et al.
13  may correspond to cells derived from pericycle and inner cortex root layers, whereas MtCLE12 and MtCLE13 are expressed in whole primordia  16. In addition, no pMtNRLK1:GUS activity was observed in infected root hairs, consistently with recent RNAseq analyses focused on the root epidermis or root hairs that revealed no expression of either MtNRLK1 or MtSUNN  38, 39. In differentiated nodules, MtNRLK1 is mostly expressed in the differentiation/rhizobial infection zone, in agreement with laser dissection transcriptomic datasets of Roux et al. 36  , which corresponds to cells derived from the nodule central meristem. This expression domain therefore partially overlaps with the expression pattern of MtCLE12 and MtCLE13 GUS transcriptional fusions reported by Mortier et al. 16  in the nodule meristem and in the distal infection/differentiation zone.
As in Arabidopsis CLE peptides are known to act as short distance signals that are perceived by neighboring cells 10,43-46 , the MtCLE12/MtCLE13 and MtNRLK1 expression domains could be consistent with a short-distance function of MtCLE12 and MtCLE13 peptides in the nodule apex. No significant nodulation phenotype could be however identified in MtNRLK1 RNAi, mutant or overexpression lines. This suggests that MtNRLK1 may act redundantly with other RLKs still to be discovered. In Arabidopsis, bam3 single mutants also have a WT phenotype suggestive of a functional redundancy  9. MtSUNN is most likely not a candidate RLK functionally redundant with MtNRLK1 in the nodule apex as no SUNN transcripts were detected in the nodule meristem or differentiation/rhizobial infection zones. In Arabidopsis, the expression pattern of AtBAM3 and AtCLV1 is also divergent, even though both receptors were associated to AtCLV3 peptide signaling in the SAM  9.
For the nrlk1 phenotype analysis, plants were grown in a mixture of perlite/sand (3/1) and watered with the nitrogen-poor solution 'i'. Inoculation with Sm1021 was performed 7 days post-germination and nodules were scored 15 dpi. To evaluate the nodule nitrogen-fixation capacity and histology, a more efficient nitrogen-fixing strain was used, S. medicae WSM419. Sections were obtained using a vibratome (Leica VTS1200) on agarose 1% embedded nodules (3 weeks post-inoculation) and imaged with a Fluo AZ100 macroscope (Nikon). Acetylene reduction assays were used to evaluate the nitrogen fixation capacity, as previously described  31. Protein similarity tree analysis. To identify MtSUNN-related LRR-RLKs, the full amino acid sequence of MtSUNN was blasted on the Medicago truncatula Proteome v4 database using the BLASTP algorithm (http://www.
jcvi. org/medicago/). In addition, the 10 proteins most closely related to MtNRLK1 in A. thaliana were retrieved from the TAIR10 protein database (https://www. arabidopsis. org/) by using the BLASTP algorithm. The MtCRA2 LRR-RLK, previously functionally characterized in M. truncatula regarding symbiotic nodulation, was also included. To generate a similarity tree using the SeaView version 4 program  55  , complete amino acid sequences of MtSUNN and MtNRLK1 as well as the A. thaliana and M. truncatula closest related sequences previously identified were used to generate a multiple alignment using MUSCLE  56. Poorly aligned positions and divergent regions were eliminated with the G-blocks software  57. A maximum-likelihood phylogenetic tree was obtained using the PhyML program  58  as well as an aLRT (approximate Likelihood-Ratio Test) test for branch support.