dataesr/scientific-paragraphs-categorization huggingface dataset:
Both criteria entail the computation of the distance between the two models. For the accuracy assessment the distance is computed from the submitted data (D) to the ground truth (GT). For the completeness evaluation, it is the opposite, i. e. , from GT to D. The computed distances can be signed (Middlebury MVS and 3DOMcity) or unsigned (Tanks and Temples). A threshold distance is usually adopted to find the fraction or percentage of points falling within the allowable threshold, which is decided according to the data density and noise. As additional accuracy parameters, DTU Robot Image Data Set and 3DOMcity characterize the distance distributions with statistics, such as the mean and median values, also performing some outlier removal. ETH3D and Tanks and Temples combine the accuracy/precision p and completeness/recall r values into a single score, i.
Curvature is also adopted as a measure to indicate structure and noise, well correlating with visual experience  [65]. Databases comprising reference and distorted meshes are publicly available for assessing MVQ degradation introduced by geometrical processing  [66]  (subjective quality assessment of 3D models). However, since MVQ metrics are computationally expensive, the 3D models contained in these databases are of lower resolution (up to 50k vertices and 100k triangles) than typical models for photogrammetric applications. Lately, machine learning tools have been applied to estimate visual saliency and roughness of meshes without any reference model  [67].
Evaluation Approach and Criteria To enable the evaluation approach, a series of steps was undertaken. First, for the datasets for which the ground truth is available in the form of a point cloud, a surface was reconstructed using the same approach as in Section 4. 2, preserving the original point cloud resolution. The meshing result was evaluated by computing the distance between the original point cloud and the derived mesh. Only the vertices that fall within a defined threshold (i. e. , three times the average point cloud resolution) were retained. Moreover, interpolated triangles with a side length greater than about ten times the average mesh resolution and small disconnected components were eliminated from the mesh models generated by the three methods described in Section 4. Finally, a common datum was defined for the reference and evaluated meshes.
The co-registration between the reference mesh and surfaces to be compared (called "data") was performed in a two-step procedure: (i) an absolute orientation through reference points or image exterior orientation parameters where available; and (ii) 7-DoF spatial transformation refinement through ICP between the photogrammetric dense point cloud and the reference mesh. The following metrics were used to evaluate the results: • Accuracy was evaluated as the signed Euclidean distance between the vertices of the (photogrammetric) data mesh and the (scanner) reference mesh. The signed Euclidean distance was chosen instead of the Hausdorff distance to highlight any possible systematic error. For this, both CloudCompare and Meshlab  [85]  implementations were tested, providing equivalent results.
Evaluation without a Reference Mesh: The Aerial Case Study For the aerial dataset, no reference model is available. Thus, the quantitative evaluation is reported in terms of plane fitting RMS in two different areas, P1 and P2 in Figure  3a. The difference in the surface reconstruction approaches is also qualitatively shown in the section profiles S1 and S2 (Figure  3b ) and normal rate change (Figure  3c ). High noise and discrepancies can be observed while comparing the three methods. All methods present topological errors, with non-manifold vertices as well as self-intersecting faces for M2 and M3. M1 appears less noisy, in terms of both plane fitting RMS (Table  3 ) and normal change rate (Figure  3c ). The section profiles also show a less bumpy pattern. High noise and discrepancies can be observed while comparing the three methods.
Evaluation with a Reference Mesh Figure  5  shows the surface models for the datasets where a reference surface model is available. The related analyses are summarized in Tables  4 5 6 7 (see Section 5. 2 for the definition of the metrics) and visually shown in Figures  6 7 8 9 for the datasets Fountain, Modena, Ignatius and Wooden ornament, respectively. The roughness map provides information on the geometric non-smoothness of the surface. The normal change rate map highlights high geometric details (e. g. , 3D edges), which appear in the images as sharp green to red contours, and high frequency noise, shown as scattered green to red areas. The accuracy, completeness and F-score values (Tables  4 5 6 7 ) reveal that the three investigated approaches perform similarly, with M1 and M2 usually outperforming M3.
Evaluation with a Reference Mesh M1 also exhibits the best metrics in terms of roughness for all the datasets. The visual inspection and metric values for the section profiles point out that all three methods tend to over-smooth the geometric details compared to the ground truth and that the sections from M1 are usually less noisy. All the investigated mesh models present non-manifold vertices, and, other than the Modena's bas-relief (Table  5 ), M2 (Tables  4, 6  and 7 ) and M3 (Tables  4  and 7 ) are also characterized by self-intersecting faces. The normal change rate maps convey additional insight on the different performances of the three surface generation methods. It is evident that none of the approaches can reproduce the geometric details of the reference mesh. However, M3 and especially M2 are more affected by high frequency noise, easily distinguishable in the green to red spots spread over the models (Figures  6  and 8  from the Fountain and Wooden ornament datasets).
For Ignatius, due to the significantly lower resolution of M3, a different kernel size is adopted for the normal change rate estimation, clearly implying an over-smoothed geometry with respect to the other approaches. In the Modena's bas-relief, the normal change rate does not highlight significant differences among the investigated methods. Sensors 2020, 20, x FOR PEER REVIEW 17 of 25 relief (Table  5 ), M2 (Tables  4, 6  and 7 ) and M3 (Tables  4  and 7 ) are also characterized by selfintersecting faces. The normal change rate maps convey additional insight on the different performances of the three surface generation methods. It is evident that none of the approaches can reproduce the geometric details of the reference mesh. However, M3 and especially M2 are more affected by high frequency noise, easily distinguishable in the green to red spots spread over the models (Figures  6  and 8  from the Fountain and Wooden ornament datasets).
For Ignatius, due to the significantly lower resolution of M3, a different kernel size is adopted for the normal change rate estimation, clearly implying an over-smoothed geometry with respect to the other approaches. In the Modena's bas-relief, the normal change rate does not highlight significant differences among the investigated methods. From the analysis of the roughness and normal change rate maps, it can be deduced that the methods do not show significant differences, when the starting data (dense point cloud or depth maps) are not heavily affected by high frequency noise (Modena dataset, Figure  7 ). When noise From the analysis of the roughness and normal change rate maps, it can be deduced that the methods do not show significant differences, when the starting data (dense point cloud or depth maps) are not heavily affected by high frequency noise (Modena dataset, Figure  7 ).
An overview of surface assessment criteria adopted in computer graphics was also provided, with a focus on those considered in this study to quantify the reconstruction noise and geometric details. The three considered methods were introduced, and the selected datasets and evaluation metrics were described. Drawing a definite conclusion was out of the scope of the paper. The results of the investigation show that, in experiments with a reference model, M1 and M2 performed similarly in terms of accuracy and completeness. However, the surface generation method integrated into the image-based reconstruction workflow (M1) generally outperformed the other two approaches in recovering geometric details and reducing the noise in all the considered case studies, regardless of the characteristics of the given images (scale, resolution, texture, etc.
). Although relevant for some applications, especially in real time, computational efficiency was not included in this evaluation, because the main interest was to test the best achievable quality even at the expense of long calculation times. However, it should be mentioned that M3 proved to be computationally more efficient than M1 and M2, i. e. , on average 5-10 times faster than M1 and 2. 5-5 time faster than M2, differences that can get larger as the complexity of the dataset increases in terms of resolution and noise. The presented study also emphasized the lack of benchmarks and assessment criteria specifically addressing the surface reconstruction problem for applications where metric accuracy matters. Our future work will include the expansion of the current investigation to further MVS approaches and integrating perceptual evaluation metrics into rigorous accuracy assessment procedures.
Figure5shows the surface models for the datasets where a reference surface model is available. The related analyses are summarized in Tables4-7(see Section 5. 2 for the definition of the metrics) and visually shown in Figures6-9for the datasets Fountain, Modena, Ignatius and Wooden ornament, respectively. The roughness map provides information on the geometric non-smoothness of the surface. The normal change rate map highlights high geometric details (e. g. , 3D edges), which appear in the images as sharp green to red contours, and high frequency noise, shown as scattered green to red areas. Sensors 2020, 20, x FOR PEER REVIEW 11 of 26.
The project participants were provided with a common database of in situ-observed CO 2 concentrations (including the observation sites that are now part of the ICOS network) and were tasked with providing their best estimate of the net terrestrial carbon flux for that period, and for a large domain covering the entire European Union. The inversion systems differ by the transport model, the inversion approach, and the choice of observation and prior constraints, enabling us to widely explore the space of uncertainties. This paper describes the intercomparison protocol and the participating systems, and it presents the first results from a reference set of inversions, at the continental scale and in four large regions. At the continental scale, the regional inversions support the assumption that European ecosystems are a relatively small sink (-0.
, 2005; Maignan et al. , 2008)  or land use change  (Naudts et al. , 2016) , and to forecast the evolution of the land sink in Europe, in the context of global climate change. Despite the large uncertainties, there is a growing demand from the policy makers and the society in general for more accurate and relevant numbers, such as estimates of the national budgets of CO 2 fluxes, these demands being reinforced by the Paris Agreement. For instance, the European Commission (under the VERIFY and CHE H2020 projects) is supporting the development of observation-based monitoring systems for estimating CO 2 fluxes at national to sub-national scales, with a clear interest in both land ecosystem fluxes and anthropogenic emissions. Atmospheric transport inversions rely on transport models and statistical methodologies to derive the most likely estimates of CO 2 fluxes given large datasets of observed atmospheric CO 2 concentrations and prior information provided in general by ecosystem models.
In addition to in situ data, the development of satellite observations of CO 2 following the launch of GOSAT  (Kuze et al. , 2009)  in 2009 and OCO-2 in 2014  (Crisp et al. , 2004)  should further densify the observation coverage, in particular with the foreseen European constellation of CO 2 high-resolution imagers of the Copernicus Anthropogenic CO 2 Monitoring mission (CO2M;  Pinty et al. , 2017) , starting from 2025. The use of mesoscale transport models will then become necessary to fully exploit the potential of these large datasets with observations at high spatial and temporal resolution. In this context, the EUROCOM (EUROpean atmospheric transport inversion COMparison) project aims to coordinate a European effort to improve the knowledge on the NEE based on an ensemble of long-term European-scale inversions (i.
Protocol and participating models Given the overall objective of the study (to assess the robustness of inversion-derived European flux estimates), we deliberately opted for a relatively loose protocol: the participants were requested to use a common set of anthropogenic CO 2 emissions (fossil fuel combustion, cement production and large-scale fires) and to use only atmospheric observations from a common dataset, prepared specifically for the EUROCOM project (Sect. 3. 1). They were requested to provide a monthly gridded estimate of the net land-atmosphere CO 2 exchange (net ecosystem exchange, NEE) over the period 2006 to 2015, covering at least the area 15 • W-35 • E by 33-73 • N, at a 0. 5 • by 0. 5 • spatial resolution (independently of the actual resolution of the inversions). A set of fluxes (prior NEE, anthropogenic and ocean fluxes) was made available to the modellers through a data repository hosted at the ICOS Carbon Portal, along with the common observation database, but except for the imposed anthropogenic emissions, the participants were essentially free to choose the characteristics of their inversions.
In particular they could perform further selections on the observations database (selection of observation sites and selection of observations to use at each site) and choose their prior NEE and ocean flux estimates. The treatment of boundary conditions and the precise specification of uncertainties (prior and observation uncertainties) were also left to the modellers. The inversions in our ensemble are constructed with the aim to maximize the diversity of the systems, and hence to obtain a better estimate of the overall uncertainty of inversion results. A stricter protocol, for example fixing the prior fluxes and the prior uncertainties, would facilitate the interpretation of results but would artificially decrease their spread. Furthermore, some parameters should not be prescribed. For instance, the observations are selected based on the capacity of the underlying transport model to reproduce them, and the type of boundary condition is also dependent on the transport models, which differ across the systems.
Common atmospheric observation database A comprehensive dataset of atmospheric CO 2 concentration observations in Europe was compiled as input for the inversion systems, on the basis of the GLOBALVIEWplus v3. 2 Observation Package (ObsPack), a product compiled and coordinated at NOAA's Earth System Research Lab together with the ICOS Carbon Portal (Cooperative Global Atmospheric Data Integration Project, 2017). The dataset was further extended by including measurements that had been collected in several national and EU-funded projects, like CarboEurope-IP and GHG-Europe, and during the preparatory phase of the Integrated Carbon Observation System (ICOS) Research Infrastructure. Finally, for two stations, the data were obtained from the World Data Center for Greenhouse Gases (https://gaw. kishou.
go. jp/, last access: 6 August 2020). Compared to the original GLOBALVIEWplus product, we added time series from nine measurement stations and partly complemented time series at two stations. The datasets were harmonized with respect to format and sampling interval and provided in the ObsPack format  (Masarie et al. , 2014). The original datasets and data providers of the time series are reported in Table  1 , and the locations of the observation sites are also shown in Fig. 1. The majority of sites (35 out of 39) sample concentrations continuously (i. e. hourly or more frequently); 18 sites are tall towers (intake height > 50 m), some with observations available at different levels, in which case only the upper level was used (as it is more difficult for the transport models to represent concentration gradients close to the ground).
The model computes the gross primary productivity with the assimilation of carbon based on  Farquhar et al. (1980)  for C 3 plants. Land cover changes (including deforestation, regrowth and cropland dynamic) were prescribed using annual land cover maps derived from the harmonized land use dataset  (Hurtt et al. , 2011)  combined with the ESA-CCI land cover products. The ORCHIDEE simulation used here has been produced at a global, 0. 5 • resolution with 3-hourly output. -LPJ-GUESS (used by LUMIA): LPJ-GUESS  (Smith et al. , 2014)  combines process-based descriptions of terrestrial ecosystem structure (vegetation composition, biomass and height) and function (energy absorption, carbon and nitrogen cycling). Vegetation is dynamically simulated as a series of replicate patches, in which individuals of each simulated plant functional type (or species) compete for the available resources of light and water, as prescribed by the climate data.
LPJ-GUESS includes an interactive nitrogen cycle. The simulation used here is forced using the WFDEI meteorological dataset  (Weedon et al. , 2014)  and produces a 3-hourly output of gross and net carbon fluxes at a 0. 5 • horizontal resolution. -SiBCASA (used by CTE): SiBCASA  (Schaefer et al. , 2008)  combines the parameterization of the Simple Biosphere model (SiB) with the biogeochemistry of the Carnegie-Ames-Stanford approach (CASA) calculating the exchange of water, carbon and energy between 25 soil layers, plants and the atmosphere. The rate of photosynthesis is found using the Ball-Berry-Woodrow model of stomatal conductance  (Ball et al. , 1987) , and C 3 and C 4 vegetation types are treated separately in the kinetic enzyme model of  Farquhar et al. (1980). The simulation used here is forced using meteorological inputs from ERA-Interim and run with a 10 min time step https://doi.
Anthropogenic emissions The anthropogenic emissions from combustion of fossil fuels and biofuels and from cement production are based on a pre-release of the EDGARv4. 3 inventory for the base year 2010  (Janssens-Maenhout et al. , 2019)  and were provided as a 0. 5 • , hourly resolution product. This specific dataset includes additional information on the fuel mix per emission sector (Janssens-Maenhout, personal communication, 2017) and thus allows for a temporal scaling of the gridded annual emissions for individual years  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  according to year-to-year changes of fuel consumption data at national level  (BP, 2016) , following the approach of Steinbach et al. (2011). A further temporal disaggregation into hourly emissions is based on specific temporal factors (seasonal, weekly and daily cycles) for different emission sectors (Denier van der  Gon et al.
, 2011). The seasonality and inter-annual variability of this anthropogenic emissions prior are also reported in Fig. 2  (in black). Agricultural waste burning is already included in the version of the EDGAR v4. 3 anthropogenic emission inventory that we are using. Also, large-scale biomass burning emissions are negligible in Europe (of the order of 0. 01 Pg C yr -1 ), and therefore we decided that no extra biomass burning emission dataset should be used in the inversions. Nevertheless, two models (LUMIA and FLEXIN-VERT) included a prescribed biomass burning source, based on the Global Fire Emission Database v4  (Giglio et al. , 2013).
Ocean fluxes The role of the ocean flux in causing spatial CO 2 gradients between stations at the European scale is very minor in regard to the magnitude of other fluxes (below -0. 1 Pg C yr -1 ). Therefore modelling groups were free to choose which ocean fluxes to use. Two groups (LUMIA and FLEXINVERT+) used ocean fluxes from the CarboScope surface-ocean pCO 2 interpolation (oc_v1. 6 and oc_v1. 4 respectively)  (Rödenbeck et al. , 2013). The CarboScope interpolation provides temporally and spatially resolved estimates of the global sea-air CO 2 flux. Fluxes are estimated by fitting a simple data-driven diagnostic model of ocean mixed-layer biogeochemistry to surface-ocean CO 2 partial pressure data from the SOCAT database. NAME-HB used a climatological prior from  Takahashi et al. (2009) , which is based on a climatology of surface ocean pCO 2 constructed using measurements taken between 1970 and 2008.
, 2011). The observation errors are set up as a function of sta-tions, of the height of the station level above the ground and of the season, following the estimates by  Broquet et al. (2011 Broquet et al. ( , 2013)) , based on comparison of simulations and measurements of radon. Their standard deviation for the 1 h averages ranges from 3 to 17 ppm. -In LUMIA, observations from sites with continuous observations are selected based on the ''dataset_time_window_utc" flag in the metadata of the observation files. That corresponds, for most sites, to a 11:00 to 15:00 UTC time range, and to a 23:00 to 03:00 UTC time range for mountain sites. At sites with only flask observations, all samples were used. The observation uncertainties are set as the quadratic sum of the measurement uncertainties, of the uncertainty associated with the foreground transport model (i.
e. FLEXPART) and to the background concentrations. The measurement uncertainties are taken from the data files when available, and a minimum uncertainty of 0. 3 ppm is enforced. Foreground transport model uncertainties are computed by performing two similar forward model runs, with TM5 and LUMIA (i. e. FLEXPART + background concentrations from TM5), configured such that the only difference is the model used to compute the transport within the EUROCOM domain (since the two models run at very different resolutions, this provides a reasonable proxy for the representation error). The uncertainties on the background concentrations are set as the standard deviation of the vertical profile of background CO 2 concentrations around each observation (see  Monteil and Scholze, 2019 , for details about the approach).
Annual to multi-annual budgets are an important measure to quantify the impact of environmental conditions such as ecosystem management, disturbances and climate extremes on the terrestrial carbon cycle. The annual budget has notably been synthesized in  Reuter et al. (2017) : on the one hand, global inversions that assim-ilate only surface observations showed geographical Europe as a moderate to rather small carbon sink (≈ -0. 4 Pg C yr -1 ) on multi-annual scales and, on the other hand, inversions constrained by satellite retrievals of total column atmospheric CO 2 (XCO 2 ) consistently infer that it is a much larger sink, of the order of -1 Pg C yr -1. More recent studies suggest a smaller uptake:  Scholze et al. (2019)  find a mean sink of -0. 3 ± 0. 08 Pg C yr -1 by assimilating three datasets (namely in situ atmospheric CO 2 and remotely sensed soil moisture and vegetation optical depth) into their carbon cycle data assimilation system.