Xiang Fang

Wei Wang

François Brisset

Anne-Laure Helbert

Thierry Baudin

Microstructure and texture evolution of the non-oriented silicon steel during punching

Keywords: Microstructure, Texture, Non-oriented silicon steel, Punching

établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Non-oriented silicon steels are still the main manufacturing materials of motor core. This type of materials has excellent properties as low iron loss and high magnetic permeability are required to ensure performance of motors. It is known that the crystal of silicon steel exhibits magnetic anisotropy. The <100> crystallographic directions are the most easily magnetized ones, the <110> directions being the seconds and the most difficult magnetized directions are the <111> [START_REF] Honda | On magnetization of single crystals of iron[END_REF]. Therefore, the {100}<uvw> fiber is the optimal grain orientation, in which the other two <100> directions could be uniformly distributed in the rolling plane of the material.

As mentioned above, the texture of silicon steels has an important influence on its magnetic properties. For this reason, there are many studies on texture evolution of silicon steel sheets. Some studies are focused on the initial texture, controlled during the casting process [START_REF] Liu | Solidification structure and crystallographic texture of strip casting 3 wt.% Si non-oriented silicon steel[END_REF][START_REF] Xu | Evolution of cube texture in stripcast non-oriented silicon steels[END_REF]. Some other researches aims to at improve the texture with optimization of the process and related parameters after casting, as rolling direction of cold rolling [START_REF] Mehdi | Effect of skin pass rolling reduction rate on the texture evolution of a non-oriented electrical steel after inclined cold rolling[END_REF][START_REF] Sanjari | Texture evolution during skew cold rolling and annealing of a non-oriented electrical steel containing 0.9 wt% silicon[END_REF][START_REF] Mehdi | The evolution of cube ({001}< 100>) texture in non-oriented electrical steel[END_REF], reduction rate of hot rolling [START_REF] Jiao | Influence of hot deformation on texture and magnetic properties of strip cast non-oriented electrical steel[END_REF], temperature of rolling process [START_REF] Li | Characterization of microstructure, texture and magnetic properties in twin-roll casting high silicon non-oriented electrical steel[END_REF], annealing temperature after cold rolling [START_REF] Liu | Formation of {001}< 510> recrystallization texture and magnetic property in strip casting non-oriented electrical steel[END_REF][START_REF] Sha | Strong cube recrystallization texture in silicon steel by twin-roll casting process[END_REF], two-stage cold rolling process and related parameters [START_REF] Zhang | Through process texture evolution of new thin-gauge nonoriented electrical steels with high permeability[END_REF]. There are also studies dealing with the effects of material composition on texture control [START_REF] Sahoo | Recrystallization behaviour and texture of non-oriented electrical steels[END_REF]. However, all these studies are mainly conducted to either enhance the texture components as {001}<uvw>, and especially {001}<100>, {001}<uv0> and {0kl}<100> orientations, or to reduce the {111}<uvw> components and thus improve the material magnetic properties.

At present, considering the efficiency factor, the factories mainly use the punching process to manufacture iron core. This process can lead to a strong plastic deformation zone of 0.3 to 0.5 mm width at the cutting edge [START_REF] Ossart | Effect of punching on electrical steels: experimental and numerical magneto-mechanical analyses[END_REF][START_REF] Weiss | Influence of shear cutting parameters on the electromagnetic properties of non-oriented electrical steel sheets[END_REF]. Meanwhile, Saleem et al. [START_REF] Saleem | Microstructure and mechanical property connections for a punched non-oriented electrical steel lamination[END_REF] research shows that the microstructure at cutting edge is heterogeneous with formation of ribbon grains and shear bands. All these studies reveal that there should be a change of local texture at the cutting edge of material, thus affecting the performance of iron core. According to this assumption, the deformation mechanisms and related texture evolution of non-oriented silicon steels at the cutting edge after punching is studied in this paper.

Materials and Methods

The non-oriented silicon steel produced by China Baowu Steel Group Corporation Limited was used during this study. The main chemical composition of the material is shown in Table 1 and the thickness of the sheet is 0.5 mm. Circular samples of 30 mm in diameter are obtained from the material sheet using a punching process with 4% as the clearance. The punching experiments were performed by the help of a hydraulic press with 5 tons as the maximum force. During the punching, the displacement of the press head was of about 13 mm/s. Generally, the cutting surface after punching with sharp tools is almost plat with a small burr. The area near the cutting edge with strong deformation is very narrow. On the contrary, the punching with blunt tool introduces a wider deformation area and a larger burr at the cutting edge [START_REF] Weiss | Influence of shear cutting parameters on the electromagnetic properties of non-oriented electrical steel sheets[END_REF]. For a detailed study of the texture evolution from the cutting edge to the inner of the material, a worn punch tool was selected. The punching process geometry and the dimensions of mold are described in Fig. 1. Two positions of the circular samples at cutting edge were studied, as shown in Fig. 2a. One position corresponds to the point indicated by the rolling direction (RD) and the other is that indicated by the transverse direction (TD). For each position, some specimens are cut from the circular samples, as shown in Fig. 2b, and the related surface RD (or TD)-ND (normal direction) is studied. In the present paper, the studied positions will be named as specimen-RD or specimen-TD. During this study, microstructure and texture of cutting edge are analyzed by electron backscatter diffraction (EBSD) using a field emission gun-scanning electron microscope (FEG-SEM, ZEISS Supra 55 VP) fitted with an EDAX Velocity EBSD system. Before the EBSD analysis, specimens with burrs were grounded with SiC paper. Then, they were polished gradually with diamond polishing fluids of 3 µm, 1 µm and 0.5 µm. Finally, they were polished and etched with an oxide polishing suspension (0.02 µm, OPU, Struers). The resolution of most EBSD analyses is of 1 µm as the step size. To obtain more detailed information on the substructure inside grains, some EBSD analyses were carried out with a 0.2 or 0.5 µm step size.

Microstructures and textures of samples were analyzed using TSL-EDAX OIM™ Analysis software.

In order to prevent symmetry errors during the calculation of the orientation distribution function (ODF), the intervals of φ1, Ф and φ2 were chosen as 360°, 90° and 90°, respectively (triclinic symmetry). In addition, the harmonic method was used and the series rank of calculation was set as 34 [START_REF] Penelle | Determination of the total texture[END_REF].

Results and discussion

Microstructure evolution during punching

The initial microstructure of the material before punching is shown in Fig. 3. Grains of this material are equiaxed and the average grain size is 25.7 µm. There is no color gradient inside the grains (Fig. 3a).

The average value of grain orientation spreads (GOS), which is a quantitative estimation of intragranular misorientation [START_REF] Field | The role of annealing twins during recrystallization of Cu[END_REF], is 0.45 for the initial material. All these reveal that this material is completely recrystallized before the punching process. Moreover, some grain boundaries with low misorientation angle could be observed in the grains (Fig. 3b). The fractions of low angle grain boundaries (LAGB) 2°-5°, 5°-15° and high angle grain boundaries (HAGB) are 4.6%, 11.5% and 83.9%, respectively. The Mackenzie random distribution describes the misorientation distribution of grain boundaries for a randomly oriented cubic material [START_REF] Mackenzie | Second paper on statistics associated with the random disorientation of cubes[END_REF], and it is always applied as a reference. Compared to this random distribution, the smaller the average misorientation of grain boundaries is, the sharper the texture of material is [START_REF] Mason | The generalized Mackenzie distribution: Disorientation angle distributions for arbitrary textures[END_REF]. Therefore, as shown in Fig. 3c, the initial material used in this study should have a certain texture and not be random. After punching, the deformed microstructure of the material was analyzed. Fig. 4 shows the microstructure of the cutting edges for the specimens-RD and TD. Macroscopically, the upper surface of both specimens shows an arc-shape as shown with yellow dotted lines shown in Fig. 4a andb. This indicates the existence of bending for both specimens during punching. According to the coordinate system of the initial sheet, three areas could be observed in both specimens. The area 1 is almost 200 µm away from cutting edge. The bottom surface of this area is horizontal and the bent angle of its upper surface is less than 5°. Therefore, it can be considered that the bending in this area is negligible. Microscopically, this area contains mainly equiaxed grains as shown in initial material. The area 2 corresponds to the bending zone with a rotation around the bend point. In this area, grains are slightly elongated and rotated. Using high resolution analysis (Fig. 4c), the presence of color gradient is observed in the grains of area 2. In addition, compared to the initial material, the fraction of LAGBs with 2-5° misorientation increases for the areas 1 and 2 (Fig. 4d ande).

The area 3 is close to the cutting edge with a straight bottom surface, whose tilt angle is about 45° relative to the horizontal direction. This angle corresponds to a cumulative bending angle of area 2 and equals to the chamfer angle of the punch. Moreover, the ribbon grains could be observed at the extremity of the cutting edge as in another study [START_REF] Saleem | Microstructure and mechanical property connections for a punched non-oriented electrical steel lamination[END_REF]. These grains were mainly deformed by shear effect at cutting edge during punching. Therefore, in this area, there are a non-negligible bending effect and a shearing effect.

Texture evolution during punching

As mentioned earlier, the bending of area 1 could be considered negligible. On the contrary, the areas 2 and 3 of both specimens are bent from initial sheet.

The bending has two effects on the grain orientation. Firstly, the bending can rotate the grain through deformation. Secondly, the bending effect rotates the specimen along the bending axis. This rotation changes the orientation of the initial sample system relative to the detector system, and has obviously an influence on the orientation analysis [START_REF] Jin | Evolution of the texture and mechanical properties of 2060 alloy during bending[END_REF]. Therefore, the EBSD data of areas 2 and 3 need to be corrected before studying the texture evolution.

As the bending angle varies for all pixels in the area 2 (Fig. 5), the pixel rotation method (PRM)

proposed in the literature [START_REF] Jin | Evolution of the texture and mechanical properties of 2060 alloy during bending[END_REF] was applied for the EBSD data correction in this area. For every single pixel, the bending angle θ as shown in Fig. 5 is calculated. Subsequently, a reverse rotation of θ is applied to correct the orientation of the corresponding pixel. The bottom surfaces of area 3 are straight for both specimens, and the bent angles are 45° relative to initial direction. This indicates the sample system of the whole area 3 rotated 45° without difference between pixels. Therefore, the EBSD data are corrected with a direct rotation of whole area for both specimens.

The analysis of GOS parameter demonstrates that the deformation of area 2 is higher than that of area 1, for both specimens. However, the difference of average values between the two areas is small (Fig. 6). Moreover, these values are less than 5° which is consistent with the results of Fig. 4c and indicates a weak deformation and so the main deformation mechanisms for both areas should be dislocation slip. In order to have a statistical analysis of texture, the texture evolution of areas 1 and 2 will be studied together from the combination of EBSD data.

Texture evolution of areas 1 and 2

The initial material has a typical rolled texture of BCC materials, with γ fiber as the main texture component (Fig. 7a). The γ fiber contains mainly the and orientations {111}〈112〉 {111}〈110〉

knowing that the intensity of is lower than that of .

{111}〈110〉 {111}〈112〉

Compared to the initial material, the γ fibers presented in the areas 1 and 2 of both specimens become incomplete with the disappearance of the components and the decrease of the {111}〈110〉 spreading of the orientations (Fig. 7b andc). This should be attributed to the statistical {111}〈112〉 problem without enough grains presented in the analysis areas but also to the texture evolution. In addition, some orientations with the formation of a small part of {221} fiber are observed. {221}〈uvw〉

In the areas 1 and 2, it exists also the and orientations. All these newly {323}〈533〉 {112}〈110〉

observed orientations correspond to a rotation around the <110> axis from or {111}〈110〉 {111}〈112〉 component of γ fiber. Indeed, it is known that the <110> axes are the normal directions of the slip planes in bcc materials. Therefore, the formation of new orientations is mainly due to the dislocation slip.

Moreover, and as already discussed above, some components of γ fiber presented in these areas of both specimens have a small orientation spread compared to the initial material. Most of the components have a spread towards the orientations instead of a complete spread all around them. This {221}〈uvw〉 corresponds to a deformation with few number of activated dislocation slip systems, which depends on the stress/strain state. At the same time, the average GOS of areas 1+2 is of about 4.0° and 3.0° for specimen-RD and TD, respectively. These values are still less than 5° which indicates a weak deformation associated with a few number of slip systems.

Texture evolution of area 3

For both specimens, incomplete γ fiber could be observed in area 3, and this corresponds to a statistical problem (Fig. 8a andb). In addition, the incomplete fiber which is not found in initial {110} material and in areas 1+2, is observed in the area 3 of both specimens. This fiber starts from {110} to Goss for specimen-RD, and from to for {110}〈225〉 {110}〈001〉 {110}〈110〉 {110}〈111〉

specimen-TD. For the two samples, the misorientations between the main components of the γ fiber and of the fiber correspond to a rotation of 35° around axis (Fig. 8c andd). {110} 〈110〉

Fig. 8: ODF section plots (φ 2 =0° and φ 2 =45°) for area 3 of a) specimen-RD and b) specimen-TD.

Illustration of rotation relationships between the main components of the γ fiber and of the fiber {110} with the pole figures, for c) specimen-RD and d) specimen-TD. {111}

For detailed study and verification, areas 3 of other specimen-RD and specimen-TD were analyzed with high resolution (0.5 µm as the step size). At the extremity of cutting edge where shear effect from punch and die is strong, most of the ribbon grains have a orientation (Fig. 9a). However, {110}〈𝑢𝑣𝑤〉 it exists always some trace areas with the orientations around area.

{111}〈𝑢𝑣𝑤〉 {110}〈𝑢𝑣𝑤〉

Meanwhile, the formation of some micro-shear bands in the grains were observed in area 3 of both specimens (Fig. 9b, e andf). The angles between the elongated direction of original grain and tilted direction of shear bands are about 35°.

The texture of grains including shear bands inside were also analyzed by ODF. For specimen-RD, the formation of shear bands correspond to the formation of Goss orientation from the {111}〈112〉 grains (Fig. 9b andc). The presence of shear bands with Goss orientation inside the were {111}〈112〉 also observed in the cold rolled steels as a geometric softening effect [START_REF] Nguyen-Minh | Shear banding and its contribution to texture evolution in rotated Goss orientations of BCC structured materials[END_REF]. For specimen-TD, the formation of shear bands with orientation in the grains was also observed {110}〈110〉 {111}〈110〉

(Fig. 9d ande). All these shear bands lead to the formation of orientations from the {110}〈𝑢𝑣𝑤〉 {111}

ones. Meanwhile, the inverse evolution of texture from to grain 〈𝑢𝑣𝑤〉 {110}〈𝑢𝑣𝑤〉 {111}〈𝑢𝑣𝑤〉 could be also observed with the formation of shear bands (Fig. 9f andg). This inverse change of texture by formation of shear bands was also observed in the cold rolled silicon steel [START_REF] Dorner | Microstructure and texture of shear bands in cold rolled silicon steel single crystals of Goss orientation[END_REF]. The relationship between the orientation of original grains and that of observed shear bands in this study always correspond to a rotation of 35° around the 〈110〉 axis between them. Therefore, the formation of shear bands is one of the main deformation mechanisms presented in the area 3 with a sudden change of grain orientation. In the area 3 after punching, the relatively higher fraction of orientations {110}〈𝑢𝑣𝑤〉 compared to the orientations, should be attributed to the higher fraction of {111}〈𝑢𝑣𝑤〉 {111}〈𝑢𝑣𝑤〉 orientations in initial material, which leads, with a greater probability, to the formation of {110}〈𝑢𝑣𝑤〉 shear bands in grains. {111}〈𝑢𝑣𝑤〉 Moreover, the continuum orientation spread between and as shown in {110}〈𝑢𝑣𝑤〉 {111}〈𝑢𝑣𝑤〉

the ODF sections (Fig. 9c, d andg) should correspond to the texture evolution, by dislocation slip, within grains and shear bands. This is in agreement with the existence of color gradient in both original grains and their shear bands. In addition, the existence of dislocation slip in the shear bands has been also observed in the cold rolled silicon steel [START_REF] Dorner | Microstructure and texture of shear bands in cold rolled silicon steel single crystals of Goss orientation[END_REF].

Conclusion

With a worn punch tool, the cutting edges of samples are strongly bent, and could be divided into three areas.

The bent effect is negligible in the area 1. The bent angle of area 2 varies continuously, and the EBSD data were corrected with PRM. The whole areas 3 of specimens-RD and TD are bent with a cumulative angle (45°), and the EBSD data were corrected with a direct rotation of the sample system.

Then, the deformation mechanisms and the texture evolution at the edges of the punched silicon steel were studied. The observations could be summarized as follow:

In area 1 and 2 of specimens-RD,-TD, the grains with the orientation rotate towards {111}〈𝑢𝑣𝑤〉 dislocation slip leads to the orientation spread around these two main orientations. All these observations illustrate that the formation of shear band is the main deformation mechanism and that results in the formation of the fiber from the γ fiber. {110}

Fig. 1 :

 1 Fig. 1: Geometrical description of the punching process and dimensions of the mold.

Fig. 2 :

 2 Fig. 2: a) Illustration of the positions analyzed (red points); b) surface studied for each position.

Fig. 3 :

 3 Fig. 3: a) Inverse Pole Figure (IPF) map of the initial non-oriented silicon steel before punching; b) Blank

Fig. 4 :

 4 Fig. 4: Blank map with grain boundaries (misorientation >15°) of a) specimen-RD and b) specimen-TD,

Fig. 5 :

 5 Fig. 5: Illustrations of the sample geometry, and of the correction of the sample system by the pixel

Fig. 6 :

 6 Fig. 6: Grain Orientation Spread for areas 1 and 2 of a) specimen-RD and b) specimen-TD.

Fig. 7 :

 7 Fig. 7: ODF section plots (φ 2 =45°) for a) initial material, b) areas 1+2 of specimen-RD, and c) areas

Fig. 9 :

 9 Fig. 9: a) Crystal Direction map (tolerance=15°) of area 3 cropped from another specimen-RD with a

the orientations by dislocation

  slip. {211}〈𝑢𝑣𝑤〉 Areas 3 of the two specimens contain a large number of ribbon grains at the extremity of the cutting edge. Most of ribbon grains have a or a orientation. These grains with {110}〈𝑢𝑣𝑤〉 {111}〈𝑢𝑣𝑤〉 different fiber-orientations appear alternately and cross with each other. Moreover, the main deformation mechanisms in these areas are dislocation slip and formation of shear bands. The formation of shear bands leads to a change of texture between the and the orientations, and the {111}〈𝑢𝑣𝑤〉 {110}〈𝑢𝑣𝑤〉

Table 1 :

 1 Chemical composition of the material.

	Elements	Si	Al	Fe
	Fractions (wt%)	0.63	0.23	Bal.

Acknowledgments: This work was supported by the National Natural Science Foundation of China (Grant No. 51801141) and the Fundamental Research Funds for the Central Universities (WUT: 2019IVB015). The authors would also acknowledge the support from 111 Project (B17034) and Innovative Research Team Development Program of Ministry of Education of China (IRT_17R83).


Jérémy Bernard
email: jeremy.bernard@zaclys.net

Erwan Bocher

Elisabeth Le Saux Wiederhold

François Leconte

Valéry Masson

Estimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0.1

Abstract. Information describing the elements of urban landscapes is required as input data to study numerous physical processes (e.g., climate, noise, air pollution). However, the accessibility and quality of urban data is heterogeneous across the world. As an example, a major open-source geographical data project (OpenStreetMap) demonstrates incomplete data regarding key urban properties such as building height. The present study implements and evaluates a statistical approach that models the missing values of building height in OpenStreetMap. A random forest method is applied to estimate building height based on a building's closest environment. A total of 62 geographical indicators are calculated with the GeoClimate tool and used as independent variables. A training dataset of 14 French communes is selected, and the reference building height is provided by the BDTopo IGN. An optimized random forest algorithm is proposed, and outputs are compared with an evaluation dataset. At building scale for all cities, at least 50 % of the buildings have their height estimated with an error of less than 4 m (the cities' median building heights range from 4.5 to 18 m). Two communes (Paris and Meudon) demonstrate building height results that deviate from the main trend due to their specific urban fabrics. Putting aside these two communes, when building height is averaged at a regular grid scale (100 m × 100 m), the median absolute error is 1.6 m, and at least 75 % of the cells of any city have an error lower than 3.2 m. This level of magnitude is quite reasonable when compared to the accuracy of the reference data (at least 50 % of the buildings have a height uncertainty equal to 5 m). This work offers insights about the estimation of missing urban data using statistical methods and contributes to the use of open-source datasets based on opensource software. The software used to produce the data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2021b), and the dataset can be freely accessed at https://doi.org/10.5281/zenodo.6855063 [START_REF] Bernard | Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)[END_REF].

Introduction

The topography -defined as the spatial distribution of natural and artificial land use features -has a significant influence on the microclimate. This is clearly visible in urban areas, where the great heterogeneity of forms, materials and land uses induces high variability in temperature, wind speed and humidity [START_REF] Oke | Boundary layer climates[END_REF]. Thus an in-depth knowledge about the topography of a location would lead to a better understanding and more accurate modeling of its climate as well as of other physical processes, such as noise propagation and air pollution [START_REF] Tang | Influences of urban forms on trafficinduced noise and air pollution: Results from a modelling system[END_REF]Bocher et al., 2019).

There are currently no standard geographical data to study the urban climate worldwide. However, urban data tend to increase under both closed license and open-source. A key open-source data approach is the OpenStreetMap (OSM) 1 project. Data from the latter have several features: they are expected to be available worldwide, and the most important objects needed for urban climate studies (building footprints, isolated tree locations, water, vegetation and impervious patches) are available, well located and described using a great diversity of tags [START_REF] Mocnik | The OpenStreetMap folksonomy and its evolution[END_REF]. Moreover, OSM has a free tagging system that allows users to improve the current tags (key, value) with their own information (an OSM user can describe a building object with the tags such as the following: "building"="house", "height"="10", "building:levels":"2").

However, information concerning building height is not available worldwide, neither in OSM nor in any other database [START_REF] Masson | City-descriptive input data for urban climate models: Model requirements, data sources and challenges[END_REF]. [START_REF] Lao | Is OpenStreetMap suitable for urban climate studies?[END_REF] reported that less than 3 % of buildings globally have a height value, and less than 4 % have a number-of-levels value. For the city of Paris (where this information is known to be quite well informed), the values are only 0.1 % and 51.2 %, respectively. This is a major shortcoming, since the urban climate is often characterized by spatial indicators based on the third dimension:

-The sky view factor (SVF), which is calculated according to terrain level variations, building and tree locations and heights, is related to effective albedo [START_REF] Bernabé | Radiative properties of the urban fabric derived from surface form analysis: A simplified solar balance model[END_REF] and is strongly correlated to temperature [START_REF] Lindberg | Modelling the urban climate using a local governmental geo-database[END_REF] and wind speed [START_REF] Johansson | Towards the modelling of pedestrian wind speed using high-resolution digital surface models and statistical methods[END_REF].

-The building height variability within an area affects the vertical and horizontal wind speed [START_REF] Hanna | Wind flow and vapor cloud dispersion at industrial and urban sites[END_REF].

-The roughness length of an area, often calculated using facade density, is used to estimate the wind speed vertical profile of the urban canopy [START_REF] Hanna | Wind flow and vapor cloud dispersion at industrial and urban sites[END_REF].

The objective of this study is to develop a method to estimate the height of a building from its topographical context using only data available in OSM. Modeling building footprints and their height value has been largely covered by remote sensing. It can cover large areas at once quite efficiently, and the resulting datasets can be updated quite easily with a repetitive coverage. Different techniques for building height extraction have been developed based on photogrammetric processing [START_REF] Fradkin | Surface reconstruction from multiple aerial images in dense urban areas[END_REF][START_REF] Zeng | An elevation difference model for building height extraction from stereoimage-derived DSMs[END_REF], analysis of point clouds from airborne light detection and ranging [START_REF] Sohn | Using a binary space partitioning tree for reconstructing polyhedral building models from airborne lidar data[END_REF][START_REF] Shan | Topographic laser ranging and scanning: principles and processing[END_REF], shadow detection [START_REF] Song | Shadow detection and reconstruction in high-resolution satellite images via morphological filtering and example-based learning[END_REF][START_REF] Shao | Shadow detection and building-height estimation using IKONOS data[END_REF] and, more recently, a deep learning approach [START_REF] Cao | A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities, Remote Sens[END_REF].

In the meantime, the recent and global movement regarding open data -specifically for vector topographic databases such as OSM -offers new opportunities to estimate building height. The geography of a territory and the pattern of the topographic elements are criteria that can be used as a proxy to identify the urban forms and therefore the distribution of building heights. [START_REF] Biljecki | Generating 3D city models without elevation data[END_REF] have used building footprints and their corresponding attributes to derive building heights for the city of Rotterdam, the Netherlands. They have tested several random forest models using building properties characterizing its geometry footprint (size, shape and number of neighbors), other attributes (use, age and number of levels), and information concerning the inhabitants (the number and their levels of income) as independent variables. [START_REF] Milojevic-Dupont | Learning from urban form to predict building heights[END_REF] have proposed a random forest approach to estimate building height using 152 features. These independent variables are related to buildings (e.g., footprint geometry), streets (e.g., closest street, closest intersection), street-based blocks (e.g., number of blocks in a given radius) and cities (e.g., total city area). For the base case, the training dataset includes the building heights of the Netherlands, the region of Friuli Venezia Giulia in Italy and five French urban areas, and the validation dataset includes the building heights of the state of Brandenburg in Germany.

A major limitation of these studies is the obstacle of reproducibility for experts and practitioners. Indeed, the algorithms are not fully available. Moreover, input datasets require many preprocessing steps, since the format and the access differ between city models (e.g., French 3D city models in [START_REF] Milojevic-Dupont | Learning from urban form to predict building heights[END_REF]. The method presented in the next sections uses a random forest approach and can be easily reproduced using the GeoClimate software without any preprocessing steps, since it is based exclusively on OSM data. The main spatial indicators used by the urban climate communities are also calculated using reference and estimated building heights. Compared to each other, they provide urban climate researchers with a good level of magnitude in terms of the impact of the estimated height on these indicators. OSM data do not contain as much detailed information about buildings as that seen in [START_REF] Biljecki | Generating 3D city models without elevation data[END_REF] (number of levels, age, number of inhabitants), but other information describing the environment of the buildings will be used (roads, vegetation, rail, etc.).

In order to make OSM data available to urban climate researchers, the GeoClimate tool has been developed (E. [START_REF] Bocher | GeoClimate: a Geospatial processing toolbox for environmental and climate studies[END_REF]Bocher et al., 2021a). It is an easy way (i) to download most of the information needed for urban climate studies, (ii) to estimate building height from the topographical context, and (iii) to calculate spatial indicators (such as SVF, building height variability or roughness length) that are useful as input for parametric climate models. This paper focuses on the second item, namely how to estimate building height in OSM when the information is missing. First, the data and the methodology used to estimate the building height are presented (Sect. 2), and second, the accuracy of these estimations is analyzed (Sect. 3).

Data and method

This study presents a method to estimate values of building height when the information is missing in OSM. The height of a building is determined according to a regression-based statistical model (e.g., random forest model) using a set of spatial indicators -including the building's shape, the building's relation to its neighbors, and the organization and morphology of the building's environment -as independent variables. The true building height values come from the BD-Topo V2.2 (BDT) provided by the French National Geographic Institute (IGN). These values are defined as reference height. Two datasets have been considered for this study, namely a training dataset to build the random forest algorithm and a validation dataset to compare the outputs of the optimized random forest algorithm with reference heights. The overall methodology is illustrated in Fig. 1 and consists of the following steps:

1. Building characterization. Each building and its environment (limited to the topographical spatial units (TSU) it belongs to -defined in Sect. 2.2.1) are characterized by spatial indicators (building area, number of buildings neighbors, vegetation fraction, etc.). These indicators are the independent variables of the statistical model.

2. Building height attribution. The reference building heights (BDT building heights) are attributed to each OSM building according to their footprints. The resulting height in the OSM dataset is the dependent variable of the statistical model.

3. Statistical analysis. The random forest model is built based on the training dataset. In this step, parameters that maximize the performance of the random forest model are identified.

4. Performance evaluation. Outputs of the optimized random forest model are evaluated against the reference heights of the validation dataset.

Each step is described further in Sect. 2.2.

Study area

Building organization and height may differ a lot throughout the world, limiting the ability to model the height of a building based on the characteristics of its environment. Thus, although the method can be used to estimate the height of any building in the world, the application area of this preliminary work is limited to the French territory. The training and evaluation areas are selected to cover all types of communes (from small villages to large conurbations) a large part of France (Fig. 2), increasing the probability of having cultural and/or historical differences inducing urbanistic heterogeneities the main geographical constraints for construction (nearby mountains -Annecy, La Thuile, Corbonodand nearby sea -La Rochelle).

To fulfill the first need, the communes have been chosen to cover each of the four French commune types defined by the French National Institute of Statistics and Economic Studies (INSEE, 2020): main urban area, secondary urban area, peripheral urban area, rural area. According to the French 2020 census data, the types are defined based on the following commune characteristics: number of inhabitants, density of population, number of employees, and population flow between households and workplaces. They are used to define the urban attraction cluster. The definitions of each type are given in Table 1.

The training and the evaluation datasets contain 14 and 8 communes, respectively. The location of each commune is shown Fig. 2, while further information concerning each territory is in given Tables 2 and3 (for training and evaluation datasets, respectively).

Methodology

Building characterization

Data from OSM are used to characterize the building and its environment: building footprint, vegetation footprint and type, water footprint, impervious footprint, rail and road footprint. The free and open-source GeoClimate software (E. [START_REF] Bocher | GeoClimate: a Geospatial processing toolbox for environmental and climate studies[END_REF]Bocher et al., 2021a) is used to compute the spatial indicators at three different scales:

building scale block scale, defined as the aggregation of all buildings touching each other topographical spatial unit (TSU) scale, defined according to the central lines of roads and rails, commune boundaries, and water and vegetation boundaries when their area is higher than 2500 and 10 000 m 2 , respectively (Fig. 3).

Each building is described by a total of 62 spatial indicators (note that all characteristics are calculated in two dimenhttps://doi.org/10.5194/gmd-15-7505-2022

Geosci. Model Dev., 15, 7505-7532, 2022 sions only, since most OSM data do not have height information). Four types of indicators are used (see Table 4) and the list of all indicators is given in Appendix A. Indicator values calculated at block and TSU scales are then attributed to each building (a building within a given block or TSU embeds the indicator values of the block and the TSU it belongs to).

Reference building heights attribution

In order to train and evaluate the statistical model, a reference height used as a true value should be assigned to each OSM building. Most of the OSM buildings do not have any information concerning their height. The few buildings with height information could have been considered as training and evaluation datasets; however, these buildings are sparse and not representative of common buildings (since most of them are filled out by OSM users because they are wellknown buildings with specificities). Therefore, the training and evaluation datasets are created only with OSM buildings that have no height. The reference height is set using the BDT data. However, a single building in OSM may match with several buildings in the BDT (Fig. 4). Thus, the height of an OSM building used as a reference (H osm,true ) is calculated from the height of all intersecting BDT buildings according to Eq. ( 1). This equation is applied for both the training and the validation datasets.

H osm,true = n i=1 A i • H BDT i n i=1 A i , (1)
with A i being the area of the intersection between an OSM building and a BDT building i, and H BDT i being the height of the BDT building i intersecting the OSM building.

In the example presented in Fig. 4, if BDT building 1 is much taller than the others (BDT buildings 2 and 3), this information is lost (smoothed by the averaging) and could then lead to a bias in the learning process. To keep track of this potential bias, a simple index is proposed to characterize the proportion of the intersection between a BDT building and an OSM building. This index, called uniqueness value (UV), is defined in Eq. ( 2):

UV = max i∈1..n A i n i=1 A i . ( 2
)
The uniqueness value considers only the BDT building that demonstrates the largest intersection area with a given OSM building. The higher the UV, the more unique the BDT building intersecting the OSM building. UV is not impacted by the fraction of the OSM building shared with other BDT buildings. If only one BDT building overlaps only a small fraction of an OSM building, the uniqueness value will be 1.

Design and optimization of the random forest statistical model

For the statistical analysis, the OSM building height (reference height H osm,true ) is defined as the dependent variable, while spatial indicators are defined as independent variables. Only the training dataset (see Table 2) is used for this step.

To obtain an optimal model, the methodology illustrated in Fig. 5 is applied.

The random forest (RF) approach is chosen for several reasons: (i) it is simple to implement, (ii) it deals with quantitative and qualitative variables, and (iii) it is appropriate when using a large number of variables [START_REF] Hastie | Data mining, inference, and prediction[END_REF]. In order to limit overfitting and a high correlation between trees, of trees was lower than 100 and no significant improvement when greater than 650);

minimum node size (minimum fraction of the sample used to create a new node): 0.0001 %, 0.001 %, 0.005 %, 0.01 % (the whole sample size includes 345 418 individuals -note that preliminary analysis showed decreasing performance over 0.01 %);

maximum variables per tree (maximum fraction of variables used in a tree): 20 %, 35 %, 50 % (of a total of 62 variables -note that preliminary analysis showed decreasing performance when the fraction was lower than 20 % and no significant improvement over 40 %);

maximum leaf nodes (maximum number of leaves in a tree): 300, 500, 800, 1100 (note that preliminary analysis showed no significant improvement over 1100 while increasing the complexity and thus potentially the overfitting).

For a default combination of 500 trees, 0.001 % minimum node size and 30 % maximum variables per tree, the effect of UV on the accuracy is studied, keeping only buildings having a UV above 30 %, 70 %, 90 % and 95 %.

A total of 70 % of the training data are randomly drawn to construct the RF. The accuracy is calculated using the remaining 30 % of the data. This process is performed 10 times for each combination C i and uniqueness value UVi. The scikit-learn Python algorithm is used for this investigation.

The optimized combination C opt and uniqueness value UV opt leading to the lowest mean absolute error (MAE) are used to construct the final RF model used in GeoClimate. For this purpose, the entire training dataset is used as input for the Smile library algorithm (since GeoClimate is Java-based).

Performance evaluation

The optimized RF model obtained in the previous step is run over the eight communes of the validation dataset to calculate the missing height values of the OSM buildings. For each building, the heights estimated with the optimized RF model ( ĤOSM,model ) are then compared to the reference height (H OSM,true ). The model error Err model is defined for heights estimated by the random forest model:

Err model = ĤOSM,model -H OSM,true .
(3)

The building height values filled out by the OSM users ( ĤOSM,user ) are also compared to the reference height. If the user filled only the number of storeys, the building height is simply calculated by multiplying the storey number by 3 m. Even though the storey height may vary quite a lot between construction age and building type (see Biljecki et al., 2017 -Fig. 5), 3 m seems to be a reasonable value according to the one observed in the literature (ranging from 2.8 to 3.5 m -see [START_REF] Biljecki | Generating 3D city models without elevation data[END_REF]Sect. 2.2.1). The user error Err user is defined for heights filled by the users (Eq. 4).

Err user = ĤOSM,user -H OSM,true

In parametric urban climate models, parameters such as building height are aggregated within each square cell of a regular grid. Therefore, four indicators are calculated for a grid of 100 m width square: the mean building height and standard deviation, the roughness length (as defined by [START_REF] Hanna | Wind flow and vapor cloud dispersion at industrial and urban sites[END_REF], and the SVF (as defined in [START_REF] Bernard | Sky View Factor Calculation in Urban Context: Computational Performance and Accuracy Analysis of Two Open and Free GIS Tools[END_REF].

Results and discussions

The dataset produced by the methodology described in Sect. 2.2 can be freely downloaded at https://doi.org/10.5281/zenodo.6855063 [START_REF] Bernard | Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)[END_REF]. In this section, cells that have no building with an estimated height are not considered for the statistical calculations.

Optimized configuration of random forest characteristics

Very little accuracy difference is observed between all combinations described in Sect. 2.2.3. For all studied configurations, the median RMSE ranges between 2.05 and 2.2 m. The minimum node size and the number of trees (when greater than 100) have the least significant impact on the accuracy. The highest accuracy is reached when the maximum variables per tree is 50 % and the maximum leaf nodes is 1100. Thus, the RF scenario chosen for GeoClimate has 350 trees, 40 % maximum variables per tree (based on previous results showing little difference between 40 % and 50 %) to minimize the correlation between trees, 0.01 % minimum node size, and 1100 maximum leaf node to minimize the potential of overfitting [START_REF] Hastie | Data mining, inference, and prediction[END_REF]. Since the maximum tree depth obtained in Python for this configuration is 33, this value is also applied to the GeoClimate RF algorithm.

The uniqueness value has an unexpected effect on the accuracy: the MAE decreases when UV value increases up to 70 % and increases for UV values above 90 %, while it could be expected to continue decreasing. However, the difference is slight (0.05 m, 2.5 %) and may be explained by the size of the sample, which is larger (+23 %) for the 70 % scenario than for the 95 % (having 345 418 and 281 081 individuals, respectively). Therefore, the data used to train the GeoClimate model are created with UV = 70 %.

General building height accuracy

For all cities, more than 50 % of the buildings have an estimated height within a ±3.97 m (3.22 m if the 18th district of Paris is excluded) interval around the true building height (Fig. 6a). At cell scale (Fig. 6b), the same statistic is ±4.61 m (2.74 m if the 18th district of Paris is excluded). If cities demonstrating a specific behavior are not considered (Paris and Meudon), the median absolute error at cell scale is always lower than 1.6 m, and 75 % of the buildings or cells of any city have an error lower than 3.2 m. This error is equivalent to the floor height of one building and could appear quite high. However, it seems quite reasonable when compared with the accuracy of the reference data (i.e., the height uncertainty of more than half of the BDT dataset is ±5 m).

Note that we have tested a previous version of the model (independent variables have been updated since then) on several cities far from the ones presented in this manuscript, and the error was almost similar. Surprisingly, the worst results are obtained with communes belonging to the training dataset (the median relative absolute error -MRAE -is 24 % for main peripheral communes -Table 5). Overall, there is almost no accuracy decrease when the model is applied to the validation dataset (Fig. 6). No city type shows a specific pattern; even the rural areas, which are not included in the training dataset, do not show a clearly higher error (MRAE = 23 %) than the overall trend (MRAE = 22 % on average). However, a specific behavior is observed for the city of Meudon and for the 18th district of Paris, which have a higher error than the main trend (Fig. 6b). A part of the explanation can be found in their uncommon urban fabrics, which makes them more difficult to estimate by the RF model:

-Meudon has quite a low median height (Fig. 6b) while also having a high building height variability within a 100 m square (Fig. 6c);

https://doi.org/10.5194/gmd-15-7505-2022 Geosci. Model Dev., 15, 7505-7532, 2022 the 18th district of Paris has the highest average building roof height at grid scale (Fig. 6b) but also the highest sky view factor of the three Parisian districts (Fig. 6d) even though it would be expected to have the lowest.

Accuracy of standard spatial indicators

The building height estimation is slightly improved when averaged at RSU scale (MRAE decreases from about 3 % for all communes types except for rural areas). The variability of height within a cell is very roughly calculated using the estimated height: for more than 50 % of the cells, the MRAE on the standard deviation of the building height is higher than 62 % (Table 5). This behavior is quite understandable, since the RF model smooths the values of the estimated height; it cannot reproduce entirely the complexity of the initial dataset. The roughness length MRAE is about 25 %. This error is slightly higher for secondary peripheral and rural communes (29.5 % and 48 %, respectively -Table 5), while Meudon and the 18th district of Paris show higher values than the MAE trend (Fig. 6e). The sky view factor is quite accurately calculated: more than 50 % of the cells have an absolute error lower or equal to 0.02 (Table 5).

The effect of the building height error is probably limited, be-cause the sky view factor is a 3-dimensional indicator: it also accounts for the horizontal footprint of the building, which is the same between the estimated and observed data.

Limitations of the model for high-rise buildings

The accuracy differs a lot between low-rise and high-rise buildings. For all types of cities, the building height is often overestimated for buildings smaller than 5 m and often underestimated for the taller ones (Fig. 7). The bias for high-rise buildings can be quite high, but it does not affect the general accuracy of the model, since most of the buildings are lowrise (see Fig. 7: 80 % of the buildings are lower than 10 m -even in main cities). A better estimation of the high-rise buildings may be achieved using a training dataset containing an equal number of buildings for all levels. This would allow a better representation of the spatial heterogeneity of the third dimension. However, this would most probably affect the accuracy of the estimation for low-rise buildings. Indeed, in France, low-rise building are much more numerous than the high-rise ones. As previously observed, there is almost no accuracy decrease between the training and the validation estimations, even for high buildings. Only a slight difference can be obhttps://doi.org/10.5194/gmd-15-7505-2022

Geosci. Model Dev., 15, 7505-7532, 2022 served for main urban cities: above 15 m, the training dataset performs better than the validation one (almost 3 m difference -Table 6). This difference is attributed to the Paris buildings dataset: the two curves almost coincide if the latter is excluded. The reason is that Paris buildings are quite accurately calculated and represent a large part of the training dataset (43.2 % of the buildings higher than 15 m). The urban fabric (very dense block of buildings with courtyards) and the building heights are quite homogeneous in Paris, thus being well taken into account by the model. In most other cities, a large amount of the high-rise buildings are isolated buildings (see Fig. 8 for an example with the city of Nantes). These buildings probably have very little shape or environmental differences with smaller, isolated buildings and are probably less numerous. Therefore, most of these buildings are seen as low-rise by the model.

It is interesting to notice that the buildings that already have a building height value in OSM (or at least a numberof-floors value) are, most of the time, slightly higher than the BDT ones (Fig. 7). This is the case for low-rise buildings (lower than 5 m -Table 6) in particular, and it may be explained by the fact that the BDT heights are taken at the lowest part of the roof. The OSM data can take into account the roof height, which is, most of the time, equal to zero for tall buildings but non-negligible for small buildings. The difference between height derived from OSM user filling and the reference data (BDT) is quite low for any building height. This result may be used to improve the model performancewhen estimating the height of a given building, the random forest may take into account the height of a nearby building filled by an OSM user as an extra independent variable.

Spatial distribution of the building height at city scale

While most of the buildings higher than 15 m are underestimated, the model allows one to represent the spatial patterns of the third dimension well; at grid scale, the average building height maps of estimated and reference values look quite similar (Fig. 9; Appendix B for other cities). While the model smooths the values slightly, the city center, first ring and second ring are quite easily distinguishable. Note that this is not the case for cities that have a more homogeneous spatial distribution of the building height values (e.g., Annecy, which is constrained by the topography -see Fig. B1 in Appendix B).

For most of the city pixels, the absolute error is under 2.5 m, and only a small proportion of cells have an error higher than 5 m (Fig. 9). This absolute error magnitude is within the accuracy of the reference building dataset. Indeed, according to the data supplier's (IGN) information (based on a sample of 7 299 422 buildings), 8.2 % of the buildings have an accuracy of 1 m, 13.5 % an accuracy of 2.5 m, and 68.8 % an accuracy of 5 m, while 9.5 % have no accuracy information.

Conclusions

There is a need for a world-wide database of morphological indicators that would be useful for many physical process interests (e.g., parametric urban climate models, noise modeling, urban planning). The GeoClimate tool aims to tackle this issue using the OpenStreetMap data. However, most of the OSM buildings do not have any information concerning their height, which is a crucial parameter for urban climate studies. A random forest model has been integrated within GeoClimate to estimate the height of a building based on spatial indicators describing its shape, its relations to other buildings and the 2D characteristics of its close environment.

This article presents the method for building and evaluating this model. The buildings from 14 French communes have been used to train the model, while the evaluation was based on 8 French communes. Attention was paid to having as many types of territories (based on the French definitions) in the samples as possible, including main urban, main peripheral, secondary peripheral and rural.

The random forest model was tuned according to four parameters: the number of trees (best 350), the minimum node size (best 0.01 %), the maximum variables per tree (best 40 %), and the maximum leaves per tree (best 1100). The reference heights used for the training of our OSM buildings were based on a dataset (French BDTopo -BDT) where buildings could not fit exactly with the OSM ones. Thus, the matching between each OSM building footprint and BDT building footprint has been quantified using the uniqueness value indicator. The latter equals 1 if only one building from the BDT was used to feed the OSM building height; it is otherwise lower than 1 and is best suited to the random forest model when values are higher than 0.7.

Two communes (Paris and Meudon) demonstrate a specific behavior within the analysis. Apart from these, the median absolute error at cell scale was always lower than 1.6 m, and 75 % of the buildings or cells of any city had an error lower than 3.2 m. This level of magnitude is similar to the BDT data used for the training: 68.8 % of the buildings heights demonstrated an uncertainty of 5 m.

Geographical indicators commonly used in urban climate studies have also been calculated at a 100 m grid cell according to the estimated building height. While the building height variability (standard deviation within a grid) is strongly affected by the building height estimation error (50 % of the cells have more than 50 % error in building height standard deviation value), the roughness length and sky view factor have a relative error of about 20 % for 50 % of the cells.

One of the major limitations of the model at the French scale is presented when applied to tall (> 15 m), isolated buildings. However, it does not affect the recognition of the general patterns of a city: most of the high-rise buildings located in the centers of the cities are quite well modeled, though slightly underestimated. ing more accurate reference data and in other countries with any existing reference data;

improving the statistical modeling: (i) selecting a dataset that has a uniform distribution of building height, as described Sect. 3.4; (ii) using the height from OSM buildings that already have this information as an additional independent variable (e.g., the average building height at RSU scale may be used); (iii) investigate other supervised methods; (iv) in the training data, get rid of OSM buildings that have less than a certain fraction of BDT buildings covering them; (v) find more appropriate building properties that can be used as independent variables (e.g., the height of the nearby buildings filled by OSM users); and (vi) identify a subset of the most appropriate variables in order to limit the adverse effects of noisy variables.

Appendix A: List of all spatial indicators used as independent variables A1 Building scale

Figure 1 .

 1 Figure 1. Overall methodology -the use of dashed arrows means that only the training dataset is used.

Figure 2 .

 2 Figure 2. Location of the 22 communes used as training or evaluation data. ©OpenStreetMap contributors 2021. Distributed under the Open Data Commons Open Database License (ODbL) v1.0.

Figure 3 .

 3 Figure 3. Example of topographical spatial unit calculation.

Figure 4 .

 4 Figure 4. Example of the overlap between OSM and BDT buildings.

Figure 5 .

 5 Figure 5. Method to train and optimize the random forest model. Only the training dataset is used at this step.

Figure 6 .

 6 Figure 6. Median absolute error versus median true value for (a) building height, (b) RSU average building height, (c) RSU standard deviation building height, (d) RSU mean ground sky view factor, and (e) RSU effective terrain roughness length. The cross and the dot are the medians, while the whiskers are the first and third quartiles.

Figure 7 .

 7 Figure 7. On the top: building height errors (Err model and Err user ) versus reference building height (H OSM,true ) for each type of city. The dots represent the median, while the whiskers are the 1st and 3rd quartiles. On the bottom: cumulated distribution of reference building height (H OSM,true ) for each type of city. The intervals used for the reference building height (the abscissa) are based on the following values: 0, 5,7.5, 10, 12.5, 15, 20, 30, 50 m (values above 50 m are not considered, since their number is negligible and they affect the reading).

Figure 8 .Figure 9 .

 89 Figure 8. Buildings taller than 15 m for which the height underestimation is higher than 30 %. © OpenStreetMap contributors 2021. Distributed under the Open Data Commons Open Database License (ODbL) v1.0.

Figure B3 .

 B3 Figure B3. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B4 .

 B4 Figure B4. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B6 .

 B6 Figure B6. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B7 .

 B7 Figure B7. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B8 .

 B8 Figure B8. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B10 .

 B10 Figure B10. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B11 .

 B11 Figure B11. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B12 .

 B12 Figure B12. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B14 .

 B14 Figure B14. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B15 .

 B15 Figure B15. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B16 .

 B16 Figure B16. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B18 .

 B18 Figure B18. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B19 .

 B19 Figure B19. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B20 .

 B20 Figure B20. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B22 .

 B22 Figure B22. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Figure B23 .

 B23 Figure B23. Results for the commune at grid cell (upper left panel) reference building height, (upper right panel) estimated building height, (lower left panel) absolute building height error, and (lower right panel) fraction of OSM buildings that have height information. For all panels except the lower right, only cells that have buildings -with at least 90 % of their buildings having no height value in OSM -are displayed.

Table 1 .

 1 The four commune types defined by INSEE (2020).

	Commune type	Definition
	Main urban area	Commune centre of the urban attraction cluster
	Secondary urban area Other commune of the urban attraction cluster
	Peripheral urban area Commune in the attraction area of the urban cluster
	Rural area	Commune outside of any urban attraction area

Table 2 .

 2 Information and statistics about the training dataset.

	Commune type	Commune name Number of buildings 18th districts) Inhabitants (2017) 193 665)	75 118)
		Toulouse	479 553	103 368	31 555
		Nantes	309 346	57 550	44 109
		Annecy	126 924	21 153	74 010
		Avignon	91 921	29 113	84 007
		La Rochelle	75 735	31 194	17 300
	Main peripheral Nanterre	95 105	10 851	92 050
	urban area	Meudon	45 352	5430	92 048
		Blagnac	24 517	9286	31 069
	Secondary	La Haie-Fouassière 4659	2323	44 070
	peripheral	Gratentour	4158	2938	31 230
	urban area	Staffelfelden	3959	2254	68 321
		Bourgneuf	1275	782	17 059
		Lathuile	1016	732	74 147

* INSEE code Main urban area Paris (6th, 11th and 2 187 526 (40 525, 145 903, 15 964 75 056 (75 106, 75 111, * Only buildings that have a height value higher than 3 m in the BDT (cf. Sect. 2.2.2) have been conserved for this evaluation.

Table 3 .

 3 Information and statistics about the validation dataset. Only buildings that have a height value higher than 3 m in the BDT (cf Sect. 2.2.2) have been conserved for this evaluation.

	Commune type	Commune name	Inhabitant (2017) Number of buildings * INSEE code
	Main urban area	Rennes Dijon	216 815 156 920	30 527 23 044	35 238 21 231
	Main peripheral urban area	Charnay-lès-Macon Saint-Nicolas de Redon	7376 3179	3574 2515	71 105 44 185
	Secondary peripheral urban area	Allaire Pont-de-Veyle	3854 1625	2744 729	56 001 1306
	Rural area	Corbonod Saint-Ganton	1264 424	1115 469	1118 35 268
	*

Table 4 .

 4 Types of spatial indicators used to define the main characteristics of each unit scale.

	Indicators type	Scale of application	Examples of indicators
		Building Block TSU
	Type and use

x Building type, building use Form and size x x x Area, form factor, fraction of courtyard, etc. Spatial relations x Minimum distance to another building, fraction of wall shared with other buildings, minimum distance to road, etc. Planar density x Building fraction, vegetation fraction, etc. Aggregated statistics from lower scale x x Mean building area, standard deviation building form factor, etc.

Table 5 .

 5 Summary of the main statistics for each combination of {indicator, commune type, dataset type}. Bold values are the ones described in the text.

	Indicator	Urban class		Training			Validation
			Median Median	Median	Median Median	Median
			absolute		relative absolute median	relative
			error		absolute	error		absolute
					error *			error *
		Main urban	2.13	10.65	0.21	1.45	6.50	0.23
	Building height (m)	Main peripheral Secondary peripheral	1.61 1.14	6.91 5.41	0.24 0.23	0.93 1.19	5.00 5.50	0.17 0.23
		Rural	-	-	-	1.24	5.50	0.23
		Main urban	1.93	10.58	0.19	1.41	7.00	0.20
	RSU average building height (m)	Main peripheral Secondary peripheral	1.61 1.04	7.20 5.43	0.22 0.20	0.75 1.12	5.31 5.47	0.13 0.21
		Rural	-	-	-	1.32	5.42	0.26
		Main urban	0.38	0.75	0.70	0.35	0.62	0.67
	RSU standard deviation building height (m)	Main peripheral Secondary peripheral	0.54 0.26	1.10 0.37	0.62 0.71	0.23 0.23	0.45 0.36	0.73 0.76
		Rural	-	-	-	0.08	0.00	0.76
		Main urban	0.03	0.78	0.17	0.02	0.86	0.20
	RSU mean ground SVF	Main peripheral Secondary peripheral	0.03 0.01	0.86 0.95	0.21 0.26	0.01 0.01	0.95 0.94	0.26 0.28
		Rural	-	-	-	0.01	0.97	0.37
		Main urban	0.23	1.40	0.20	0.17	0.90	0.23
	RSU effective terrain roughness length	Main peripheral Secondary peripheral	0.19 0.11	0.94 0.55	0.24 0.27	0.07 0.11	0.52 0.47	0.20 0.32
		Rural	-	-	-	0.07	0.17	0.48

* Contrarily to the other indicators, the reference value used for MRAE is not the SVF of the grid cell but 1-SVF of the grid cell.

Table A1 .

 A1 List of all building scale spatial indicators used as independent variables.

	Indicator name		Indicator type	Name of the indicator in
		Type	Form	Spatial	the GeoClimate documentation (version 0.0.1)
		and use and size relations
	BUILD_TYPE	x			None (this is an input of GeoClimate)
	BUILD_MAIN_USE	x			None (this is an input of GeoClimate)
	BUILD_PERIMETER		x		PERIMETER
	BUILD_AREA		x		AREA
	BUILD_TOTAL_FACADE_LENGTH		x		TOTAL_FACADE_LENGTH
	BUILD_COMMON_WALL_FRACTION			x	COMMON_WALL_FRACTION
	BUILD_NUMBER_BUILDING_NEIGHBOR			x	NUMBER_BUILDING_NEIGHBOR
	BUILD_AREA_CONCAVITY		x		AREA_CONCAVITY
	BUILD_FORM_FACTOR		x		FORM_FACTOR
	BUILD_PERIMETER_CONVEXITY		x		PERIMETER_CONVEXITY
	BUILD_MINIMUM_BUILDING_SPACING			x	MINIMUM_BUILDING_SPACING
	BUILD_ROAD_DISTANCE			x	ROAD_DISTANCE
	BUILD_LIKELIHOOD_LARGE_BUILDING		x		LIKELIHOOD_LARGE_BUILDING
	A2 Block scale

Table A2 .

 A2 List of all block scale spatial indicators used as independent variables.

	A3 TSU scale
	Indicator name		Indicator type	Name of the method in
		Form	Aggregated statistics the GeoClimate documentation (version 0.0.1)
		and size from lower scale
	BLOCK_BUILDING_DIRECTION_UNIQUENESS x		BUILDING_DIRECTION_UNIQUENESS
	BLOCK_AREA	x		AREA
	BLOCK_BUILDING_DIRECTION_EQUALITY	x		BUILDING_DIRECTION_EQUALITY
	BLOCK_HOLE_AREA_DENSITY	x		HOLE_AREA_DENSITY
	BLOCK_CLOSINGNESS	x		CLOSINGNESS
	BUILD_AVG_PERIMETER		x	None (average from lower scale)
	BUILD_STD_PERIMETER		x	None (standard deviation from lower scale)
	BUILD_AVG_AREA		x	None (average from lower scale)
	BUILD_STD_AREA		x	None (standard deviation from lower scale)
	BUILD_STD_TOTAL_FACADE_LENGTH		x	None (standard deviation from lower scale)
	BUILD_STD_COMMON_WALL_FRACTION		x	None (standard deviation from lower scale)
	BUILD_STD_NUMBER_BUILDING_NEIGHBOR		x	None (standard deviation from lower scale)
	BUILD_AVG_AREA_CONCAVITY		x	None (average from lower scale)
	BUILD_STD_AREA_CONCAVITY		x	None (standard deviation from lower scale)
	BUILD_AVG_FORM_FACTOR		x	None (average from lower scale)
	BUILD_STD_FORM_FACTOR		x	None (standard deviation from lower scale)
	BUILD_AVG_PERIMETER_CONVEXITY		x	None (average from lower scale)
	BUILD_STD_PERIMETER_CONVEXITY		x	None (standard deviation from lower scale)
	BUILD_STD_MINIMUM_BUILDING_SPACING		x	None (standard deviation from lower scale)
	BUILD_AVG_ROAD_DISTANCE		x	None (average from lower scale)
	BUILD_STD_ROAD_DISTANCE		x	None (standard deviation from lower scale)
	BUILD_AVG_LIKELIHOOD_LARGE_BUILDING		x	None (average from lower scale)
	BUILD_STD_LIKELIHOOD_LARGE_BUILDING		x	None (standard deviation from lower scale)

Table A3 .

 A3 List of all TSU scale spatial indicators used as independent variables.

	Indicator name		Indicator type	Name of the method in the
		Form	Planar	Aggregated statistics GeoClimate documentation (version 0.0.1)
		and size density from lower scale
	RSU_HIGH_VEGETATION_FRACTION		x	AREA_FRACTION_x
	RSU_HIGH_VEGETATION_WATER_FRACTION		x	AREA_FRACTION_x
	RSU_HIGH_VEGETATION_BUILDING_FRACTION		x	AREA_FRACTION_x
	RSU_HIGH_VEGETATION_LOW_VEGETATION_FRACTION		x	AREA_FRACTION_x
	RSU_HIGH_VEGETATION_ROAD_FRACTION		x	AREA_FRACTION_x
	RSU_HIGH_VEGETATION_IMPERVIOUS_FRACTION		x	AREA_FRACTION_x
	RSU_WATER_FRACTION		x	AREA_FRACTION_x
	RSU_BUILDING_FRACTION		x	AREA_FRACTION_x
	RSU_LOW_VEGETATION_FRACTION		x	AREA_FRACTION_x
	RSU_ROAD_FRACTION		x	AREA_FRACTION_x
	RSU_IMPERVIOUS_FRACTION		x	AREA_FRACTION_x
	RSU_VEGETATION_FRACTION_URB		x	VEGETATION_FRACTION_URB
	RSU_LOW_VEGETATION_FRACTION_URB		x	LOW_VEGETATION_FRACTION_URB
	RSU_HIGH_VEGETATION_IMPERVIOUS_FRACTION_URB		x	HIGH_VEGETATION_IMPERVIOUS_FRACTION_URB
	RSU_HIGH_VEGETATION_PERVIOUS_FRACTION_URB		x	HIGH_VEGETATION_PERVIOUS_FRACTION_URB
	RSU_ROAD_FRACTION_URB		x	ROAD_FRACTION_URB
	RSU_IMPERVIOUS_FRACTION_URB		x	IMPERVIOUS_FRACTION_URB
	RSU_AREA	x		AREA
	RSU_GROUND_LINEAR_ROAD_DENSITY		x	GROUND_LINEAR_ROAD_DENSITY
	RSU_AVG_NUMBER_BUILDING_NEIGHBOR			x	AVG_NUMBER_BUILDING_NEIGHBOR
	RSU_AVG_MINIMUM_BUILDING_SPACING			x	AVG_MINIMUM_BUILDING_SPACING
	RSU_BUILDING_NUMBER_DENSITY		x	BUILDING_NUMBER_DENSITY
	RSU_BUILDING_TOTAL_FRACTION		x	BUILDING_TOTAL_FRACTION
	RSU_BUILDING_DIRECTION_EQUALITY			x	BUILDING_DIRECTION_EQUALITY
	RSU_BUILDING_DIRECTION_UNIQUENESS			x	BUILDING_DIRECTION_UNIQUENESS

https://doi.org/10.5194/gmd-15-7505-2022

Geosci. Model Dev., 15, 7505-7532, 2022

https://www.openstreetmap.org (last access: 19 Septem-

ber 2022)Published by Copernicus Publications on behalf of the European Geosciences Union.

Geosci. Model Dev., 15, 7505-7532, 2022 https://doi.org/10.5194/gmd-15-7505-2022

https://doi.org/10.5194/gmd-15-7505-2022Geosci. Model Dev., 15, 7505-7532, 2022

Acknowledgements. The method presented in this paper has been integrated in the GeoClimate tool and developed within the following research projects:

2021), part of ERA4CS, a project initiated by JPI Climate and co-funded by the European Union under grant agreement no. 690462 -CENSE (2017-2021), funded by the French National Research Agency (ANR) under grant agreement no. Projet-ANR-16-CE22-0012 https://doi.org/10.5194/gmd-15-7505-2022 Geosci. Model Dev., 15, 7505-7532, 2022

Code and data availability. The major part of this work can be reproduced directly using the Software GeoClimate version 0.0.1 (the source code and executable file of this software version are permanently available on Zenodo at https://doi.org/10.5281/zenodo.6372337, Bocher et al., 2021b); the scripts and data are available on Zenodo at https://doi.org/10.5281/zenodo.6855063 [START_REF] Bernard | Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)[END_REF]. GeoClimate downloads OpenStreetMap data using the overpass API from the end point https://overpass-api.de/ (last access: 28 September 2022), estimates building height when missing and calculates geographical indicators. The resulting datasets presented in this paper have been obtained using the OpenStreetMap data between June and September 2021. It can be freely accessed at https://doi.org/10.5281/zenodo.6855063 [START_REF] Bernard | Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)[END_REF]. The French BDTopo (version 2.2) is used only for training and evaluation purposes. It is a proprietary dataset provided by the French National Geographic Institute (IGN) and is available upon request. Thus it is unfortunately not possible to make this dataset freely accessible. This was one of the major motivations for perform this work, i.e., to create a methodology to automatically create a topographic dataset containing buildings with estimated height.

Care should be taken for territories that have limited OSM data available (which is not the case in this study, since all cities used in this work have a higher building fraction in OSM than in BDT). In this case, the first step before applying our work would be to contribute to OSM and fill the gap in the study area. In our opinion, aside from this issue, the dataset resulting from the optimized random forest could be useful for climate analysis (even though the model is far from being perfect). We recommend a prior evaluation of what the effect of using the output of the RF model compared with the reference data usually employed by urban climate researchers could be. While we do not expect major differences when applied with parametric urban climate models at city scale, the spatial error might be quite high at neighborhood scale. Thus, for researchers and practitioners willing to use GeoClimate at a finer scale (for example, to automatically download land-type and land-use information for explicit modeling purpose), we recommend that they contribute to the OSM project first. Specifying the height of the most important buildings of their studying area in OSM can be done before running GeoClimate. At the end of the day, they can contribute to the improvement of the OSM data and also freely benefit from the GeoClimate tool. Concerning the building height modeling, the work may be continued by identifying the model's sensitivity to a lack of OSM information (for example, removing some or all of the roads, vegetation and water data);

evaluating the accuracy of the estimations using other reference datasets -in France, it could be performed us- Competing interests. The contact author has declared that none of the authors has any competing interests.

Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

-SLIM (2020-2021), a Copernicus project C3S_432 Provisions to Environmental Fore-casting Applications (Lot 2).

Financial support. The article processing charges for this openaccess publication were covered by the Gothenburg University Library.

Review statement. This paper was edited by Richard Mills and reviewed by two anonymous referees.


Benjamin Molinet
email: benjamin.molinet@univ-cotedazur.fr

Santiago Marro
email: santiago.marro@univ-cotedazur.fr

Elena Cabrio
email: elena.cabrio@univ-cotedazur.fr

Serena Villata
email: serena.villata@univ-cotedazur.fr

Tobias Mayer

A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Argumentation is the process by which arguments are constructed, compared and evaluated to establish whether any of them is warranted. Argumentation is an effective approach for solving various theoretical and practical problems [Atkinson et al., 2017], like explaining and justifying decision making outcomes. This is particularly important in evidencebased medicine, where high quality evidence is combined with the individual clinical experience of the practitioner with respect to the patient's values, to achieve the best possible outcome [START_REF] Sackett | [END_REF].

In this paper, we present a new version of ACTA [START_REF] Mayer | ACTA a tool for argumentative clinical trial analysis[END_REF], a web tool designed to assist clinicians in analyzing clinical trials, empowering them with the ability to retrieve the main arguments (i.e., the claims and evidence together with their relations of support or attack) contained in the text. ACTA 2.0 1 proposes several new functionalities compared to the previous version. First, it exploits novel state-of-the-art neural models for the analysis of clinical trials, going beyond the link prediction task by labelling argument relations (i.e., to label as attack or support the relations holding between the identified argument components). Second, it goes beyond PICO element identification by allowing for the automatic analysis of the effect of an intervention on the observed outcome parameters [START_REF] Mayer | [END_REF]. Finally, a refactoring of the ACTA architecture allows now for a modular solution, such that, through a REST API, the different ACTA 2.0 modules are made available to be integrated by external systems. The tool is currently used in the context of some collaborations with hospitals in Nice (France) and for the analysis of the publications of the French National Institute of Health and Medical Research (INSERM).

To the best of our knowledge, ACTA 2.0 is the only automated tool which allows for a deep analysis of clinical text from the argumentative point of view to support evidencebased medicine. Few systems tackle similar tasks, like EVI-DENCEMINER [START_REF] Wang | [END_REF] (which, given a natural language query, automatically retrieves sentence-level textual evidence from a corpora of biomedical literature), RobotReviewer [START_REF] Marshall | [END_REF]Marshall et al., 2017] (which summarizes the key information of a clinical trial, including the interventions, trial participants and risk of bias), and Ex-aCT [START_REF] Kiritchenko | [END_REF] (which extracts information containing PICO elements based on a SVM). Also, [START_REF] Lehman | [END_REF] proposed an approach to infer if a study provides evidence with respect to a given intervention, comparison intervention and outcome. However, none of these systems is able to extract a full argument graph (where evidence and claims are the nodes, and attacks and supports are the labelled edges) from a clinical text. Concerning the identification of PICO elements in text, different approaches are proposed in the literature [START_REF] Dhrangadhariya | [END_REF][START_REF] Jin | PICO element detection in medical text via long short-term memory neural networks[END_REF]Trenta et al., 2015] to identify them in text, but none of these approaches tackles the issue of analysing the effects of an intervention on the outcomes of a clinical trial study, as in ACTA 2.0.

ACTA 2.0 Main Functionalities

ACTA 2.0 provides the following functionalities, visualized in Figure 1: Search on Pubmed. PubMed 2 is a free search engine accessing primarily the MEDLINE database 3 of references and abstracts on life sciences and biomedical topics. Given the importance of this search engine in the health-care domain, ACTA 2.0 maintains the possibility to search for a (set of) abstract(s) directly on the PubMed catalogue, through their API 4 . As in the previous version of ACTA, this API is integrated as a search bar to enter queries in the common PubMed format, similarly to the original PubMed web interface. After the query is executed, when the results are shown, the user can then select one or more abstracts to proceed with the analyses offered by ACTA 2.0. Alternatively, the system accepts raw text as input to be processed via Analyse Custom Text.

Enhanced Argumentative Analysis.

Once the text is uploaded or an abstract is selected from the list of search results, the user can proceed with the argumentative and outcome analyses by pressing the Analyse button. After a short processing time, the result is visualized in the user interface in form of an argumentative graph. There, the nodes are the premises and the claims automatically detected in the abstract, and the labelled edges correspond to the relations among them. In contrast to the previous version, ACTA 2.0 integrates a completely overhauled relation classification module, implementing the methods described by Mayer et al. [START_REF] Mayer | [END_REF]. Besides underlying technical changes regarding architecture, loss function and problem formulation, the most notable difference for the user is the updated linking of the arguments in the graph, which are now not only identified, but also labeled, indicating their argumentative function as either attack or support. For readability purposes, the text of the argumentative components is not shown by default in the argumentation graph. However, the user can unveil it by interacting with the graph, i.e., hovering over the respective argument component. Additionally, argument components are highlighted with different colors (evidence in blue, claims in orange) in the abstract, which is always fully shown on the right side of the window.

PICO Element Detection. The detected PICO elements can be visualized in a similar fashion through the PICO Elements button. Again, each PICO category is highlighted in a different color. For the PICO detection, we rely on the same module employed in the first version of ACTA.

Effects on Outcomes. As one of the major upgrades, ACTA 2.0 implements a new module to analyse the reported effects an intervention has on the outcomes (O of PICO) in the clinical trial abstract. Such as if an intervention increased or decreased the measured outcome, as proposed in [START_REF] Mayer | [END_REF]. The underlying motivation for this is twofold: first, to enrich the arguments with valuable medical information and thus increase versatility of the application; and second, to provide structured and machine-processable data, which can serve as input to a computational model of argument system [Atkinson et al., 2017], for instance. In the web interface of the tool, these effects can also be visualized by pressing the Effects on Outcome button. As a consequence, the outcomes are highlighted in the displayed abstract to the right with different colors according to their predicted effect, i.e., Increased, Decreased, Improved, NoOccurrences or 4 https://pubmed.ncbi.nlm.nih.gov/advanced/ NoDifferences.

ACTA 2.0 Public API. Another major upgrade is the conversion from a static monolithic pipeline to a modular and extendable system to foster versatility and re-usability. In particular, each of the processing steps, i.e., argument component detection, relation classification, PICO and effect prediction, are now independent executable units, which can be called separately via our publicly available REST API5 . Researchers, developers and clinicians can now not only try them all individually, but have also the possibility to replace or add custom modules to the workflow, or build parts into their own projects. The required input and output formats for each module are defined in the documentation of the API.

Data Format Description. Each module takes as input a JSON file, where for the argument components, the PICO elements and Outcome Detection modules, the field "text" must be filled in with the medical text to be analyzed. For the relation classification module, the input JSON file must have the field "candidates" filled with the list of all of the argumentative components text and type (claim or premise) for which the user wants to predict the relation ( support or attack). For the effect prediction module, both the original text and the selected outcomes have to be provided in the "text" and "outcomes" fields respectively. For every module, a JSON file is produced as output with the corresponding results, either being the detected component spans or the predicted labels. All the results, including the argumentative analysis together with PICO elements and effects on outcomes, can be downloaded as a JSON file for each of the processed abstracts.

Experimental Setting and Results

Argumentative Analysis and PICO Extraction. For the argumentative analysis and the PICO element detection, we replicate the same setting of Mayer et al. [START_REF] Mayer | ACTA a tool for argumentative clinical trial analysis[END_REF], evaluating ACTA 2.0 on the same sample of the AbstRCT dataset6 (i.e., 500 abstracts of randomized controlled trials on neoplasm treatment annotated with claim, premise and their relations). We use the BIO-tagging scheme for the sequence tagging problem of the argumentative component detection, based on a pre-trained bidirectional transformer language model [START_REF] Mayer | [END_REF]. We thus keep the BERT base [START_REF] Devlin | [END_REF] model for the token-level representation of contextualized sentences and entirely fine-tune it during three epochs with an Adam optimizer and a learning rate of 2e-5. The sentence representation is passed into a Recurrent Neural Network, here a Gated Recurrent Unit (GRU [START_REF] Cho | [END_REF]) and then to a Conditional Random Field (CRF [Lafferty et al., 2001]). The model archives a f1-score of 85.2 on the neoplasm test set. PICO element detection employs the same methods to train the model on the EBM-NLP dataset [Nye et al., 2018] with coarse labels. The dataset splits are the same than in [START_REF] Mayer | [END_REF] without sentences containing less than 10 WordPiece [START_REF] Wu | [END_REF]. The obtained f1-score on the test set is 73.4. Labeled Relation Classification. For relation classification, we rely on a bi-directional transformer, but we change the representation of the sequence classification problem jointly modelling the relations by classifying all the argumentative component combinations. This new representation is passed to a linear layer with a softmax which classifies it into the three target classes (Support, Attack and NoRelation). The SciBERT [Beltagy et al., 2019] uncased base model with pretrained weights is used for the sentence representations, finetuned with a learning rate of 2e-5, batch size of 8, maximum sentence length of 256 sub-words tokens per input example during 3 epoch. The weight factor for each of the 3 classes in the weight cross entropy loss is the normalized number of training samples of this class. This settings archive respectively a macro f1-score of 0.68, 0.70 and 0.70 for the Neoplasm, Glaucoma and Mixed test sets [START_REF] Mayer | [END_REF].

Effects on Outcomes. To evaluate the Effects on Outcome task, we focus on the sentences of the AbstRCT dataset containing outcomes (i.e., 3351 sentences annotated with five classes, as reported in Table 1). Such sentences are shuf-fled and split respecting the class distributions to build the 80% and 20% train and test sets, respectively. The task is addressed as a two-step pipeline: (i) outcome detection, and (ii) effect prediction [START_REF] Mayer | [END_REF]. The first sub-task is casted as a sequence tagging problem using the 3 class BIO-tagging scheme to detect the outcome boundaries. We use a pre-trained bidirectional transformer language model trained on scientific data, i.e., SciBERT uncased, fine-tuning it for three epochs with the same hyper-parameters we used in the argument component detection module. We then pass the sentence representations across a Gated Recurrent Unit and a Conditional Random Field. We then address the effect prediction subtask as sequence classification, where each outcome together with the component it occurred in is provided as input into the effect classifier. The same pre-trained transformer model types as for relation classification (based on SciBERT combined to a bidirectional GRU and a final CRF) are used to predict one among the fives effect classes in Table 1. The outcome detection and effect classification tasks together reach a macro f1-score of 0.80.

Concluding Remarks

We presented a new version of the ACTA system for the argumentative analysis of clinical trial. ACTA 2.0 allows for the automatic extraction and interactive visualization of the arguments contained in the abstracts of clinical trial articles on PubMed, and the automatic analysis of the effects on the outcomes concerning the extracted PICO elements. A REST API is provided to allow for the embedding of the single services in other systems. Further improvements include the automatic prediction of the link between the detected outcome and the related intervention(s), and the analysis of a set of abstracts together in such a way to identify the relations among different arguments belonging to different abstracts.

Figure 1 :

 1 Figure 1: ACTA 2.0 pipeline (the newly introduced modules are in grey).

Table 1 :

 1 Statistics of the Outcome dataset, showing the numbers of Improved, Increased, Decreased, NoDifference and NoOccurrence classes independent of the disease-based subsets.

https://pubmed.ncbi.nlm.nih.gov/

https://www.nlm.nih.gov/medline/medlineoverview.html Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) Demonstrations Track

https://ns.inria.fr/acta/doc/

https://gitlab.com/tomaye/abstrct/ Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) Demonstrations Track

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Demonstrations Track

Acknowledgements

This work has been supported by the French government, through the 3IA Côte d'Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002. This work was supported by the CHIST-ERA grant of the Call XAI 2019 of the ANR with the grant number Project-ANR-21-CHR4-0002.