dataesr/scientific-paragraphs-categorization huggingface dataset:
Method This paper used three photogrammetric software programmes for data processing. The SfM-based Agisoft PhotoScan was used, with comparisons to be performed with Trimble Inpho and ERDAS IMAGINE Photogrammetry. The last two programmes have been used extensively for mapping purposes. As shown in Figure  3 , data processing in the three software solutions was performed using the same data set. Furthermore, identical a priori weighting for the bundle adjustment was used for all solutions. In this regard, the measurement standard deviations were used as weightings. For the GCPs, the a priori standard deviation was set to 15 mm in planimetry and 25 mm in altitude. The manual GCP and CP marking standard deviation was set to 0. 1 pixels, while automatic tie point marking used 1 pixels as a priori value.
The Inpho and ERDAS results served as a reference data to compare the feasibility of PhotoScan projects for mapping purposes. Metrics generated by DBAT was compared to the metrics from Inpho and ERDAS, in order to assess its quality. In order to analyse the results, the residual values of GCP from each solution were compared. Due to the fact that all GCPs were selected as control points, additional points were measured on the Lidar data acquired in the same time as the photo acquisition. These check points (CPs) were determined manually by choosing nine objects of interest from the Lidar data. However, this entrains a different standard deviation value for the CP measurements. Indeed, due to the Lidar data's resolution of 50 cm, the CP's precision cannot be higher than this value. An error on the posterior CP residuals of this order is therefore to be expected.
The validation was performed by comparing the CP coordinates acquired at the end of the aerotriangulation process and the ones measured from the ground truth Lidar data. Another criterion of quality which will be discussed in this paper is the standard deviation of the exterior orientation parameters. Since the onboard GNSS and IMU data was not used in the computation, no residuals could be computed. The standard deviation of each exterior parameter (X, Y, Z, ω, φ, κ) for each camera was therefore compared between the three solutions in order to see the quality of each camera pose. The calculated GCP 3D residuals showed that Inpho and ERDAS produced similar results in terms of precision (Figure  4 ) where the residual values range from 0. 3 to 1. 2 cm. However, PhotoScan produced markedly worse results; with residuals ranging from 0.
7 to 6. 4 cm. The root mean square (RMS) value of GCP residuals was 0. 6 cm for Inpho, 0. 8 cm for ERDAS, 3. 7 cm for PhotoScan, and 4. 4 cm for DBAT. However, it should be noted that in general the difference between measured and calculated GCP coordinates is below the theoretical average GSD (approximately 11 cm). Admittedly, the small number of available GCPs presents a weak source of interpretation. However, this gives an idea on the overall internal precision of the aerotriangulation process. The comparison of CP residuals as represented in Figure  5  shows the quality of the result. A total of nine objects, such as the edge of buildings or swimming pools, were chosen as points of interest. Due to the lack of GNSS-measured points, these CPs were measured on the Lidar data with lower resolution (50 cm).
GCP and CP Assessment Note that the higher order of residuals is to be expected due to the circumstances in their measurement. Some significant differences can be seen from points C3, C6, and C8 where the residuals difference between Inpho/ERDAS and PhotoScan reach up to 104. 1 cm. Some errors might reflect the difficulty in pinpointing a particular object in the Lidar data. The GCP and CP residual values indicate, respectively, the internal precision and external accuracy of the bundle adjustment process in each software solution. Although PhotoScan can produce more tie points than Inpho/ERDAS, these tie points are generally not of high precision. This produces noises on its bundle adjustment process, and may well be an important factor in explaining its results. Also, it is likely that the tie points are connected to only two to three photos, weakening its geometric strength.
It is also worth noting that both ERDAS and Inpho displays a more stable standard deviation variations, while those from DBAT is more volatile. The average standard deviation of κ was however similar in all software, that is 2. 2 mdeg, 1. 8 mdeg, and 1. 6 mdeg each for Inpho, ERDAS, and DBAT respectively. In this rotational aspect, similarly DBAT generated worse values except for κ. This may be due to the amount of tie points scattered on a relatively flat terrain, which means that rotations around the z-axis is less likely. On the other hand, the quality of tie points may once again contribute to the error on ω and φ. These results also show the stability of the bundle adjustment results from the three tested algorithms. As illustrated in Figure  7 , in the case of standard deviations of the planimetric exterior parameters, results from Inpho and ERDAS are relatively stable throughout the whole dataset.
CONCLUSION AND FURTHER WORK In this paper, a comparison of the bundle adjustment results in the case of large scale aerial mapping was addressed. Results generated by the SfM-based software PhotoScan and two photogrammetric software solutions (Inpho and ERDAS) were discussed and compared. The selected criteria of quality to compare them were GCP and CP residuals, as well as exterior orientation standard deviations. In the latter case, DBAT was used to reprocess the PhotoScan project in order to extract useful additional bundle adjustment diagnostics. Table  2. Comparison of the bundle adjustment quality Based on the analysis, PhotoScan still falls short in terms of the generated RMS. Analysis on the exterior orientation also shows that PhotoScan's solution is less stable than the other two photogrammetric algorithms.
While it may be enough for fast visualisation purposes, its use in topographic large scale mapping still requires further improvements. A caveat to this conclusion is that in this study, only a minimal amount of GCP was used. However, while the project is not perfect, it represents real world conditions, in which not only budgetary but often times also topographical limitations must be taken into consideration in performing mapping projects. Further work still needs to be done in order to perform tests using more control points, preferably also incorporating GNSS/IMU data. Some of our observations regarding the use of the software solutions include the fact that photogrammetric programmes are much more complicated to understand and therefore to master compared to SfM solutions. This complexity often translates to further costs, such as training of users.
Literature review Researchers have addressed the problem of a manual road infrastructure monitoring and present concepts and methods for an automated data acquisition and analysis to estimate the road roughness or to detect road hazards. For example,  Eriksson et al. (2008)  introduces a system to autonomously detect potholes. They equipped seven taxicabs in Boston with accelerometers and GPS. The authors applied several filters on the data to recognize potholes. The accuracy of the training data was 92. 4% and 39 out of 48 potholes from the testing data were correctly classified. Based on this approach,  Chen, Lu, Tan, and Wu (2013)  proposes a method to both detect potholes and rate the quality of the road surface. For this purpose, the authors collected acceleration and GPS data from 100 taxicabs.
They distinguished between anomalies and normal road segments using a threshold on the vertical acceleration of the vehicle body. The value of the threshold was calculated separately for each vehicle with a Gaussian mixture distribution. The data with the identified anomalies were sent to a server to apply the same filters as described in  (2008). In addition, the overall road roughness was determined with the standard deviation of the vertical acceleration. The accuracy to detect potholes was 90%. Seraj et al. (2014)  collected acceleration, gyroscope and GPS data in two cities from five different vehicles. The authors distinguished between three classes, namely severe (e. g. potholes, heavily patched road sections), mild (e. g. cracks) and span (e. g. speed bumps, pedestrian crossings) events.
Our contribution Previous studies have shown, that it is possible to learn a classifier to predict the current state of the road with vehicles, using sensor data (Section 1. 2). Connected vehicles can transmit the classification result over the air to a global data base. Here, the information is collected to create an overall representation of the road network. The disadvantage of a machine learning approach is the uncertainty of the prediction since the classifier is usually not perfectly trained. It means, trusting the prediction of only one vehicle could lead to a wrong representation of the current road infrastructure condition. In this paper, we present a novel solution to merge the output of multiple vehicles to provide a robust estimation of road condition. The paper addresses the question of how often and by how many vehicles a road section needs to be passed, to be confident about the combined prediction.
Especially in real-time applications fast decisions are desirable but only reliable ones are valuable. Therefore, the paper has the aim to provide accurate estimates of the road condition while requiring as few observations as possible. For the investigation of our proposed combination strategies, we developed a simulation in Matlab to analyze the amount of vehicles or overruns, which is needed to provide a prediction with a sufficient high accuracy. Furthermore, we identify the combination strategy with the most precise estimate. We also simulate different scenarios, for example, in which one sensor of a vehicle breaks down and the classifier outputs a lot of misclassifications or, in which the ground truth changes from one point of time to an other, e. g. when the road is maintained or the road condition gets worse after winter.
Methodology Modern vehicles are equipped with multiple sensors to measure their operation condition as well as the surrounding of the vehicles, for example the road condition. The relevant sensors to measure the road condition are inertial sensors, which determine the motion and vibration of the vehicle, and cameras, which scan the road. Compared to physical modelling, a condition estimation based on machine learning has the advantage that it does not require extensive system characterization. Therefore, a classifier can be trained for each vehicle to predict the road condition based on vehicle sensor data. Our data processing method in the vehicle is shown in Figure  1. The measurement data are acquired and processed in the vehicle. From the time series data, features are calculated and selected to reduce the complexity and dimension of the data-set.
Experiment We have performed experiments with vehicles to estimate the road condition and to determine a precision matrix for our simulation. We use an inertial sensor to measure the vehicle motion and vibration due to road damages. Modern vehicles have inertial sensors on-board. The goal is to distinguish between classes listed in Table  3. The road classes were selected in consultation of local civil and transportation engineering departments to support the current process of the manual inspection of the road infrastructure. However, the classes can be customized and the proposed methods to combine the output of multiple vehicles are independent from the classes. The data processing of the inertial sensor data to classify the road condition is applied according to Figure  1. The sensor data are annotated with the class of the road damage (Table  3 ), which is necessary for a supervised machine learning approach.
Simulation We have developed a simulation environment in Matlab to calculate and compare the proportion of wrong predictions of different methodologies of the described scenarios. In our simulation, the road consists of s segments and segment represents one of l classes as ground truth with l = 5 for all scenarios. In reality, theses classes could respond to types of damages, such as potholes. Therefore, the number of wrong predictions ranges from 0 to s for the created road. We set the values of the global precision matrix as shown in Table  4. The method to determine the precision matrix is explained in Section 3. Each vehicle drives over the road and classifies the segments with its individual precision matrix, which is unknown to the back-end. For classification in the simulation, a multinomial random number is generated.
The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1. 0 includes the dense media radiative transfer theory (DMRT), the improved Born approximation (IBA), and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between the IBA and DMRT theories has been shown here.
Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e. g. , sea ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models.
SMRT version 1. 0 is written in Python (https://www. python. org/, last access: 2 July 2018) and released as open source under the LGPLv3 license (https://www. gnu. org/licenses/lgpl-3. 0. en. html, last access: 2 July 2018). The paper is organized as follows. The next section gives an overview about the model architecture, the most important formulations, the code structure, and basic usage. In the third section we present an intercomparison of SMRT with other models and explore the equivalence between different microstructures. The fourth section is dedicated to the discussion of limitations and perspectives. The last section concludes the paper.
Model architecture The model is centered around the radiative transfer equation to compute the propagation of radiative energy in the medium produced by thermal emission in the medium (passive mode) and received from the sky (radar beam in active mode and sky thermal emission in passive mode). In addition to the radiative transfer equation, the other main components include the electromagnetic model that describes electromagnetic behavior of snow (i. e. , the effective refractive index or permittivity, absorption and scattering coefficients, and phase function) and the boundary conditions between layers (called interfaces hereinafter) and at the bottom interface (called substrate hereinafter). All these components are well isolated in the code and various formulations from the literature are available.
Improved Born approximation The implementation of the IBA in SMRT closely follows the original work of Mätzler (1998) with slight differences. The phase function in the 1-2 frame  (Mätzler, 1998; Ding et al. , 2010)  is calculated for a two-phase medium (subscript 1 denotes the host constituent and subscript 2 denotes the scattering constituent, e. g. , air and ice are used for light snow) as p (ϑ, ϕ) 1-2 frame = f 2 (1 -f 2 )( 2 -1 ) 2 Y 2 ( 1 , 2 ) (6) k 4 0 M(|k d |)sin 2 χ , where the angles (ϑ, ϕ) denote the scattering direction if the incident direction is taken as the polar axis. The free-space wave number is denoted by k 0 = 2π ν/c with the wave frequency ν. The volume fraction of constituent 2 is denoted by f 2 and related to the medium density ρ by f 2 = ρ/ρ 2. The relative permittivities of phases 1 and 2 are denoted by larization angle χ , which is the angle between the incident electric field and scattering direction.
Overall, the microstructure representation in SMRT closely follows a library concept as commonly employed for small angle scattering software such as in  Breßler et al. (2015). In version 1. 0, five different microstructure models are implemented as a starting point. Some microstructure models are defined by the Fourier transform of the ACF, and some by the real-space ACF. The most convenient characterization of a microstructure is in terms of the Fourier transform of the ACF. Presently the following models are implemented: exponential: C ex (k d ) = 8π l 3 ex f 2 (1 -f 2 ) [1 + (k d l ex ) 2 ] 2 , (19) Teubner-Strey: C TS (k d ) = (20) 8π ξ 3 TS f 2 (1 -f 2 ) [1 + (2π ξ TS /d TS ) 2 ] 2 + 2[1 -(2π ξ TS /d TS ) 2 ](k d ξ TS ) 2 + (k d ξ TS ) 4 , independent spheres: C sph (k d ) = f 2 (1 -f 2 )v(a)P (k d a), (21) sticky hard spheres: C shs (k d ) = f 2 v(a) P (k d a)SF shs (k d a) (22) in terms of the sphere volume v(a) = 4/3π a 3 , the spherical form factor P (X) defined by P (X) = 3 (sin(X) -X cos(X)) (X) 3 2.
To illustrate the mode of operation of the model it is instructive to relate the instructions of a tiny but fully functioning code snippet to the model operations carried out in the background: # o u t p u t s p r i n t ( r e s u l t. TbV ( ) , r e s u l t. TbH ( ) ) In the code snippet first a snowpack is built (function make_snowpack) by providing the defining properties of each layer, interface, and the substrate. Layer characteristics always include density and a microstructure model to use (e. g. , microstructure using exponential autocorrelation or SHS). The specification of temperature is optional, mostly relevant for the passive mode. Additional parameters depend on the selected microstructure model. For instance, the exponential function requires the exponential correlation length while SHS requires the sphere radius and stickiness.
g. , brightness temperature, backscattering coefficient, or Müller matrix). The result of this code shows a brightness temperature of 268. 2 and 251. 7 K at V and H polarization, respectively. The model is implemented in Python (2. 7+ and 3. 4+), which makes it easy to implement switchable formulations with default and extensible behaviors. This also avoids the cumbersome step of code compilation, though at the cost of a computational overhead compared to compiled languages. To limit this drawback, the model uses common numerical libraries, such as NumPy and SciPy extensively, allowing fast and numerically accurate calculations. The code is fully documented. It also entirely uses SI units without prefix to avoid any ambiguity. In addition, we provide different tools for convenience: (1) to facilitate convenient computation of time series or sensitivity study by a few, clear-cut lines of code the model can be run on lists of different snowpacks.
(2) To foster comparisons between SMRT and other common existing models (MEMLS, DMRT-QMS, and HUT), we provide language bindings to seamlessly run these models within SMRT, which use the prescribed snowpack in SMRT and collect results as if they were produced by SMRT. This requires that the source code of these models is separately installed (they are not distributed with SMRT for licensing reasons). Note that this feature is currently limited to the passive mode. microstructure As SMRT seeks to unify formulations from other models, a natural starting point for the validation of SMRT is a model comparison (namely with DMRT-ML, DMRT-QMS and MEMLS) to assess the validity of the implementation. This is conducted in Sect. 3. 1. Note that the performance of various models against observations has been extensively evaluated in the past and is not repeated here.
Instead, we exploit the fact that SMRT offers the opportunity to compare different microstructure formulations within the same electromagnetic framework to investigate equivalent aspects of different microstructure models. This is addressed in Sect. 3. 2. For the sake of readability, all comparisons are carried out, unless otherwise stated, for a single snowpacksensor configuration of 37 GHz in a semi-infinite medium. The code used to build the following figures is provided as open source (see code availability section) to let the reader explore other frequencies or more complex snowpacks.
Comparison of SMRT to DMRT-based models We compare SMRT to results produced from original code of several DMRT variants. Figure  4  shows the angle dependence of the brightness temperature and backscattering coefficient for SMRT DMRT compared to other models for a semi-infinite medium with a sphere radius of 0. 1 mm, density of 300 kg m -3 , stickiness of τ = 0. 5, and temperature of 256 K. The results reveal that the closest implementation to SMRT DMRT is the model DMRT-ML  (Picard et al. , 2013). Both use exactly the same formulation for the scattering and absorption coefficient, namely DMRT QCA-CP with small, monodisperse spheres in the short-range approximation (requiring moderate stickiness, i. e. , stickiness parameter should not be small). They also use a similar method to solve the radiative transfer equation, which explains the small rootmean-square difference in brightness temperature of about 0.
, 2013). For the active mode, the DMRT QCA short range does not significantly depart from the reference simulations (less than 1 dB) but the code fails to run for a radius above 280 µm and for stickiness lower than 5. This is due to an unrealistically large scattering coefficient compared to the absorption coefficient, leading to non-real eigenvalues in the diagonalization for the DORT method. To overcome the restrictive range of validity of the DMRT QCA short range, and considering that SMRT version 1. 0 does not provide DMRT QCA in the long-range approximation, an alternative strategy is to combine IBA with the SHS microstructure model. Figure  5  shows the results, which are much closer to DMRT QMS than DMRT QCA. The difference always remains lower than 5 K for the brightness temperature and 0.
, 2011). Implementing atmospheric layers in SMRT would be of interest to deal with cases of strong surface-atmosphere coupling as observed around 60 GHz near the oxygen absorption band. A simple non-scattering bulk atmosphere can be prescribed in the current SMRT version; however this requires the down-and upwelling brightness temperatures and transmittance to be calculated externally. Accurate simulations of snow on the ground in active mode would require more advanced surface scattering models than implemented in the current version. SMRT inherits from the soil modules implemented in DMRT-ML and previously in HUT and MEMLS, which were tailored to the passive mode. These modules mainly compute a specular reflection while a faithful backscatter computation is required for the active mode. DMRT-QMS includes an advanced rough surface treatment from independent numerical simulations  (Zhou et al.