Adrien Barbaresi
email: adrien.barbaresi@oeaw.ac.at

Putting Der Brenner on the map

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

Introduction

Although spatial studies have been a trend in the humanities since the 1970s, the interest for mapping techniques and geographic visualization is not self-explaining in a digital, globalized age where the emphasis lies on information, its standardization and its transmission. Space in an actual physical sense may even be considered to shift out of focus: "Globalization can be seen as an extension of modernity, due to its convergence with the Cartesian motif of eliminating the body as a part of human self-reference. Modernity and globalization thus imply a tendency of making us independent from the dimension of space." [START_REF] Gumbrecht | A Negative Anthropology of Globalization[END_REF] That being said, despite of increasing independence from physical space, it seems to become paradoxically significant (ibid.), not least because of its (properly human) anthropological and linguistic dimension which exceeds a mere deictic frame.

In digital text corpora and especially literary works, space can be analyzed as pertaining to patterns to be found across works: "Large source bases are likely to contain 'meaningful patterns,' and the ability to examine them, that is, to gain a bird's-eye view with the assistance of a computer, is tantamount to having a team of readers, even many teams of readers, at one's disposal." [START_REF] Wrisley | Locating Medieval French, or Why We Collect and Visualize the Geographic Information of Texts[END_REF] Looking for patterns is widely considered to be a task where distant reading and computer-based studies can be fruitful. In linguistics, the most common criteria of analysis are frequencies, but even in literary studies, patterns can be seen as "the strongest point of intersection between the computational strictures of text analysis and the open ended landscape of interpretative literary studies." [START_REF] Ramsay | In Praise of Pattern[END_REF] The following approach deals marginally with frequency and more with a research object sometimes neglected by corpus linguistics, proper names and especially placenames. Looking for spatial patterns and displaying them can be assimilated to the recently coined concept of geocriticism, in the sense that it is geo-centered and not character-centered or subjective framework. Under this assumption, the emergence of geographical patterns in texts is explicitly fostered: "Geocriticism is a geo-centered rather than an ego-centered approach; that is, the analysis focuses on global spatial representations rather than on individual ones (a given traveler's, for example)." [START_REF] Westphal | Geocritical Explorations : Space, Place, and Mapping in Literary and Cultural Studies[END_REF] This article documents distant, superficial reading experiments dealing with the detection and projection of spatial patterns in a historical text collection. As such it relates to a "post-disciplinary" research field which can be called digital literary cartography [START_REF] Cooper | Digital Literary Cartographies: Mapping British Romanticism[END_REF]. The resulting outline is a global depiction which only marginally tackles the question of the semantic value of places in literary works1 to offer instead a series of accesses and entry points (Barbaresi, 2018c). First, existing methods are described, then the issues in the context of Der Brenner as a text collection are introduced. Finally, results are presented and commented.

Trends in geocoding and visualization

Among the tendencies in geographic information retrieval and geocoding [START_REF] Melo | Automated Geocoding of Textual Documents: A Survey of Current Approaches[END_REF], the extraction and normalization of named places, itineraries, or qualitative spatial relations, as well as the extraction of locative expressions are particularly relevant to study text collections. The particular task of finding placenames in texts (geoparsing or placenames extraction) involves first the detection of words and phrases that may potentially be proper nouns and second their classification as geographic references [START_REF] Nouvel | Les entités nommées pour le traitement automatique des langues[END_REF]. After the identification of toponyms, a further step (geocoding or toponym resolution) resides in disambiguating and adding geographical coordinates to a place name. Geocoding mostly relies on gazetteers, i.e. geospatial dictionaries of geographic names, mostly names, locations, and metadata such as typological information, variants or dates [START_REF] Hill | Core elements of digital gazetteers: placenames, categories, and footprints[END_REF]. In this respect, knowledge-based methods using additional fine-grained registers have already been used with encouraging results. Especially for historical corpora, researchers face a lack of general-purpose tooling. In order to produce cartographic visualizations, both the capacity to adapt to different contexts [START_REF] Alex | Adapting the Edinburgh geoparser for historical georeferencing[END_REF] and the necessity to complement existing resources with a precise historical gazetteer [START_REF] Borin | Geographic visualization of place names in Swedish literary texts[END_REF] have been highlighted. Such historical gazetteers exist, but their development is challenging [START_REF] Southall | On historical gazetteers[END_REF] even for texts as late as 20th century Europe [START_REF] Plini | One name one place? Dealing with toponyms in WWI[END_REF].

Putting Der Brenner on the dissecting table

What is at stake?

Der Brenner is a periodical issued fortnightly under the direction of Ludwig von Ficker from 1910 until 1954, including several plurennial interruptions. The large majority of issues have been released between its foundation and the early 1930s. It is clearly stated objective at the beginning is to bring culture, literature and fine arts into focus. It soon integrates social criticism, especially until the 1st World War, features expressionism as a common denominator to a majority of texts until the 1920s, before turning more and more to philosophical and theological considerations. The journal starts by drawing mainly authors from Tyrol and increasingly becomes a phenomenon involving nearly all German-speaking regions, whereas the last issues are produced by a small circle of authors. Overall, the Brenner is considered to spatially incline towards the cultural centers of that time and especially Vienna (Brenner-Archiv, 2010).

The present object of scientific inquiry does not simply reside in linking text to space, it is tightly linked to the interpretation of texts and maps. Even if the methodology conveys a feeling of scientific objectivity, the validity of mental and computerized operations described here should always be examined with respect to their relevance. Geospatial analysis and spatial representation may indeed be deficient or inadequate. The anthropological significance of toponyms has been emphasized by testimonies gathered on the field [START_REF] Feld | Waterfalls of song: An acoustemology of place resounding in Bosavi, Papua New Guinea[END_REF], but the symbolic role and the expressive power of placenames do not necessarily coincide with Western instrumental science and cartography, in that particular case the world geodetic system and the chosen map projection. In this sense, putting Der Brenner on the map implies to put in on the dissecting table and bears both a thrill and a risk [START_REF] Gerlach | Lines, contours and legends: Coordinates for vernacular mapping[END_REF]: "adding more to the world through abstraction" and "adding to the riskiness of cartographic politics by proliferating yet more renders of the world".

Tooling

In order to properly observe this research object in a text collection, a proper scientific apparatus has to be put in place. The specific nature of historical literary corpora calls for ad hoc solutions. Three common issues in geographic information extraction are addressed here: detecting geographical references, disambiguating placenames and developing effective user interfaces. Additionally, the approach described here involves the gathering and curation of supplementary information applicable to historical texts.

The extraction of spatial entities or geoparsing can be considered as one of the most important part in spatial analysis [START_REF] Fize | Geodict: an integrated gazetteer[END_REF] It has been shown that drawing information from several sources and planning several corresponding phases is useful for gazetteer creation, as a method for automatically constructing a geographic gazetteer using heterogeneous sources of information available on the Internet, can mitigate coverage issues by combining information as well as improve the resolution and the correctness by validating the datasets against another. It is for example useful to mine Wikipedia for location information [START_REF] Popescu | Gazetiki: automatic creation of a geographical gazetteer[END_REF], which results in the constitution of minimum explicit tuples for geographic names: Entity-Name, Entity-Coordinates, and Entity-Type.

The difficulties related to German include capitalization, genitive forms (typically formed with s), adjectival use (frequently as modifiers), and the fact that inhabited placenames are also frequently found as personal names [START_REF] Volk | Learn-Filter-Apply-Forget. Mixed Approaches to Named Entity Recognition[END_REF]. These reasons make it difficult to build a gazetteer and prompt for the integration of morphological information. As a general principle, it is expected that "named entity recognition in German will profit from precompiled lists as well as from learning and filtering" (ibid.). Indeed, especially for historical texts it is necessary to refine or complement the resources at hand: during the 20th century there have been significant political changes in Central Europe that have severely affected toponyms, so that geographical databases lack coverage and detail.

The toolbox used for the experiments below [START_REF] Barbaresi | Towards a Toolbox to Map Historical Text Collections[END_REF] is currently being developed with historical texts in mind [START_REF] Barbaresi | Toponyms as Entry Points into a Digital Edition: Mapping Die Fackel[END_REF]. Its release under an open-source license is meant to ensure reproducibility and extendability in an open science perspective and can hopefully respond to a growing demand in this field. 2 The operations performed prior to the extraction comprise the bootstrapping, filtering and merging of registers. Both the extraction and the disambiguation are parametrized to suit the particular case at hand. As there is no commonly adopted standard for gazetteers they have to be combined. Consequently, the approach employed here allows for additional input, special sorting and prioritized merging, for example to put historical variants in the foreground. Second, it includes helpers to bootstrap geographical data, as knowledge-based methods using fine-grained data improve the results [START_REF] Vrandečić | Wikidata: a Free Collaborative Knowledge Base[END_REF]. So far, import filters for GeoNames3 and structured data from Wikipedia and Wikidata4 are implemented, with a particular emphasis on data cleaning. Third, an additional layer allows to bypass geocoding for targeted, easily extensible user lists which can operate on token or lemma level (using either linguistic processing or regular expressions and wildcards).

Regarding toponym resolution, two different types of disambiguation methods [START_REF] Buscaldi | Approaches to disambiguating toponyms[END_REF] are included so far in the toolbox: map-based and knowledge-based. It has been shown that an acceptable precision can be reached by including meta-information [START_REF] Pouliquen | Geocoding multilingual texts: Recognition, disambiguation and visualisation[END_REF], which consists here in distance (based on a calculation relative to a contextual setting), type and importance of the entries (as known from information extracted from GeoNames or Wikipedia), as well as immediate context (e.g. the expected range and the last country seen). The process is controlled by parameters such as distance calculations, filter level or size of the search area.

Finally, the results are projected on customized maps, as the toolbox integrates its own visualization component. It is profitable to allow for adaptability of projection and design and to leave it open to the user to refine the map, in a particular emphasis on the concept of visualization.

Results

The present experiments use the same text base from the digital edition of the work (AAC-Brenner, Biber, Breiteneder, Kabas, & Mörth, n.d.).

The streamlined process from text to map involves a series of decisions as well as a critical reading of texts and maps. As user-definable settings make results vary, experiments can lead to diverging realizations. The filtering level affects both the construction of gazetteers prior to geoparsing and the toponym recognition phase in itself. Its purpose is to allow for a tighter or looser control on the data, with either restricted options or opportunistic search. The disambiguation phase is controlled by mapbased parameters, notably the reference point (set here in Vienna) for distance calculations and the countries in the vicinity, which help identifying the most probable candidates. The cartographic processing in itself does not depict a neutral point of view but an active exploratory process.

In order to better assess the impact of filtering and complementary registers, I present and discuss two different comparisons on close reading and on distant reading levels. Specially curated gazetteers are used, while current geographical information is used as a fallback, entries corresponding to European locations are retrieved and preprocessed. Figure 1 displays the results with a minimum filtering on a map showing most of continental Europe. Clusters can be found everywhere, not all of them being either intuitively explainable or justified with respect to the texts. In fact, the map tells more about the gazetteers used for geoparsing as about the work in itself. Current boundaries are retraceable, especially for Germany and Austria, and numerous false positives come from plurilingual countries such as Switzerland or Belgium which are then overrepresented on the map. Figure 2 consists of a similar map featuring the results of maximum filtering level both during the construction of resources and during the extraction process. The map is more easily readable and depicts an accurate centering on Austria and Western Europe. The overall Westward tropism of the mapped locations seems to coincide with the texts. This map is thus well-suited for further analyses.

In a second experiment, filters are used to compare two distinct series of issues. The filtering of result is set to maximum and a time frame is determined. Figure 3 depicts results from the beginning of publication, as issues from 1910 to 1912 have been selected, whereas Figure 4 shows results from the middle of publication in the 1920s when a topical and auctorial shift has taken place. More years are taken into consideration (1923 to 1927) as in the first case, since the journal is more sparsely published and since these years give a good example of the trends during this decade.

Considering that place is "as much a topical consideration as it is a geographical one" [START_REF] Wrisley | Locating Medieval French, or Why We Collect and Visualize the Geographic Information of Texts[END_REF] leads to an interpretation of the maps as topical indexes of the literary work. In this regard, the distributions found in Figures 3 and4 are similar overall, which highlights a greater spatio-topical continuity than could be expected. In the last case the focus on Tyrol is still present although the number of results is smaller, and Berlin is more prominent as a major cultural center of the time. The noticeable shift from Western to Eastern Europe is a result of the change of focus, the comparatively small number of points to be drawn may corroborate the observations by close readers noting a turn toward more abstract topics in the course of time. The progressive focus on major locations is a phenomenon which is also found in the closely related Die Fackel, published by Karl Kraus in Vienna, which echoes the cultural and political reconfiguration of Europe (Barbaresi, 2018c).

Discussion

The status of placenames that are to be found and projected on the map ought to be discussed since there are consubstantial ambiguities on linguistic level that complicate the search [START_REF] Smith | Bootstrapping toponym classifiers[END_REF]. In general, successful detection and disambiguation relies on a smart interplay of resources and tools at different levels. Last, the case of either imprecise, vague or vernacular names [START_REF] Jones | Modelling vague places with knowledge from the Web[END_REF]) is a prominently linguistic issue which can at least be addressed by manual curation and should in any case be attended to. GeoNames, arguably the most commonly used gazetteer, has to be put under scrutiny, as the entries and their classification are subject to numerous problems, mostly unevenly distributed data and sparse metadata, which impact both detection and disambiguation of placenames [START_REF] Acheson | A quantitative analysis of global gazetteers: Patterns of coverage for common feature types[END_REF]. Nevertheless, this resource is still valuable mostly because of its coverage of language variants and thus potentially historical variants.

Additionally, as in any kind of linguistic analysis, the research question is only valid if there are significant phenomena to be found in the text. Concerning spatial analysis and placenames the problem can be more subtle, as the presence of identifiable toponyms is not necessarily conclusive, despite their apparent meaning and the exact linking to a gazetteer. There are "less identifiable" places which are more a "grouping of concepts".5 Let us assume for now that the placenames found in early 20th century Europe are not void of signification. Nevertheless, the design and selection steps leading to a map are all "inherently rhetorical" (Harley, 1989), they are both the foundation and the product of scientific reasoning and they form a "cultural text" (ibid.). There is a necessary distance to be taken in distant or superficial reading, as the product of machine reading can diverge from the expectations. One must bear in mind that "computers are lousy readers" and that "our current digital tools walk a delicate line between analytical power and accessibility" [START_REF] Wilkens | Contemporary Fiction by the Numbers[END_REF] Indeed, geoparsing (especially geocoding) implies a necessary reduction to monosemic interpretations, missing for example the vicinity of Brenner and Fackel as pertaining to the semantic field of fire.

Conclusion

This article introduced theoretical and practical instruments combining philological knowledge, geographic information retrieval and visualization, along with the particular example of the detection and projection of spatial patterns in a historical text collection, Der Brenner. Two different filter settings as well as maps focusing on the 1910s and 1920s have been compared. In a linguistic perspective, the tools allow for the systematization of research, for a critical approach to the extraction, and for an examination of the very concept of placenames. Examples of issues related to historical text corpora have been discussed, they show the advantages of an appropriate methodology and subsequent data cleaning and filtering.

Drawing maps to study texts leads to yet another level of textuality. By accepting it, "we are able to embrace a number of different interpretative possibilities" (Harley, 1989). Rapidly spotting problems in methodology or datasets, customizing gazetteers and maps can hopefully help mitigating the side effects of large-scale analysis and distant reading. The difference between mere data wrangling and research in digital literary cartography resides precisely in the number and diversity of conceptual and technical filters which are repeatedly applied, consciously or sometimes unknowingly. The chosen approach and its inevitable imperfections have to be brought to light, documented and criticized, as the mapping process represents an interpretation in its own right: "We organize information on maps in order to see our knowledge in a new way." [START_REF] Turchi | Maps of the Imagination: The Writer as Cartographer[END_REF] The results lead us to "ask more questions, consider other possibilities" (ibid.) As quantitative and qualitative analysis can go hand in hand, digital literary studies are not mere numeric accounts. They are first and foremost a discovery process, especially considering the complex, intricate nature of such a periodical, which yields maps closer to a deep, ramified rhizome than to a flat constellation (Barbaresi, 2018). In this case, text visualizations are the substrate of interpretable representations which do not follow the underlying data but rather confront them by putting them in perspective.

Figure 1 :Figure 2 :

 12 Figure 1: Results found throughout the collection with a minimum of filtering

Figure 3 :Figure 4 :

 34 Figure 3: Distribution of place names at the beginning of publication (1910-1912)

«Sans qu'il puisse être question d'apporter une réponse à cette énigme que désigne dans notre conscience ou notre inconscience le nom de Rome, de rendre compte même grossièrement de ce foyer capital d'émerveillements et d'obscurités.» Michel Butor, La Modification, Minuit, 1957, p.

https://github.com/adbar/geokelone

http://www.geonames.org a project of Unxos GmbH whose main source is the NGA (National Geospatial-Intelligence Agency, USA).

 4 Respectively wikipedia.org and wikidata.org, both being projects of the Wikimedia foundation.

"Like many named places in California it was less an identifiable city than a grouping of concepts --census tracts, special purpose bond-issue districts, shopping nuclei, all overlaid with access roads to its own freeway." Thomas Pynchon, The Crying ofLot 49, 1966.


Ivana Durickovic

Laura Thiébaud

Patrice Bourson

Thomas Kauffmann

Mario Marchetti

Spectroscopic Characterization of Urea Aqueous Solutions: Experimental Phase Diagram of the Urea-Water Binary System

Keywords: Urea water solutions, Phase transitions, Raman spectroscopy, Phase diagram

Raman spectroscopy was used to analyze mixtures of urea and water in order to identify the influence of the urea concentration on the solution's freezing point. Our approach consisted in the analysis of urea aqueous solutions and the determination of their phase transitions at low temperatures. Hence, Raman spectra of these solutions were acquired in a -30 to 10 °C temperature range. This enabled us to build the experimental phase diagram of the urea-water binary system.

INTRODUCTION

Discovered in 1773, urea, CO(NH 2 ) 2 , was the first organic compound synthesized in a laboratory from inorganic materials by Wohler in 1828. 1 Its synthesis came from the reaction between carbon dioxide and ammonia at high temperature and high pressure. [START_REF] Mazaud | Urée''. Production des gaz de synthèse[END_REF] Nowadays, the reverse reaction of decomposition of urea into ammonia and carbon dioxide is widely used in vehicular applications. In this specific case, its use decreases the release into the atmosphere of some chemical species present in the vehicles exhaust gases. [START_REF] Eichelbaum | The Impact of Urea on the Performance of Metal Exchanged Zeolites for the Selective Catalytic Reduction of NOx. Part I. Pyrolysis and Hydrolysis of Urea over Zeolite Catalysts[END_REF][START_REF] Plassat | Premiers résultats de système DeNox SCR par injection d'urée sur BUS Euro 5[END_REF] It is especially considered to be very effective for the decrease of the NO x emissions from diesel engines and heavy-duty vehicles. [START_REF] Zamfirescu | Utilization of Hydrogen Produced from Urea on Board to Improve Performance of Vehicles[END_REF][START_REF] Koebel | Urea-SCR: A Promising Technique to Reduce NOx Emissions from Automotive Diesel Engines[END_REF] Indeed, the ammonia, formed by the decomposition of urea injected in the catalytic exhaust systems, can react with NO x emissions to form harmless N 2 and H 2 O. An optimization of the injected amounts of urea is necessary in order to avoid any ammonia emission. [START_REF] Moos | Selective Ammonia Exhaust Gas Sensor for Automotive Applications[END_REF] In some countries, urea is also used in winter maintenance as a pavement or airport runway deicing product. Indeed, during winter, chemicals having the property to lower the freezing point are applied on pavements in order to prevent or break the ice-pavement bond. [START_REF] Ketcham | Manual of Practice for an Effective Anti-icing Program: A Guide for Highway Winter Maintenance Personnel[END_REF][START_REF] Blackburn | Snow and Ice Control: Guidelines for Materials and Methods[END_REF] However, these products often have negative environmental impacts. As an example, it was shown that the presence of urea in the environment can induce the eutrophication of lakes [START_REF] Ferguson | Minimizing the Environmental Impact of Urea De-icer from Airport Runways[END_REF] and diminish the diversity of the fauna. [START_REF] Turnbull | The Impact of Airport De-Icing on a River: The Case of the Ouseburn, Newcastle upon Tyne[END_REF] In order to diminish urea's environmental impacts, as well as the cost of its use in winter maintenance, an optimization of the quantities applied is necessary. It is therefore essential to know the concentration of the urea aqueous solution that has the appropriate ''protection temperature'' corresponding to its freezing point.

In this work, the concentration influence on the Raman spectrum was identified by the analysis of several urea aqueous solutions at room temperature. In addition to urea-characteristic peaks, the urea-induced modification can be observed in the 2900-3700 cm -1 spectral range. This zone is of particular interest as it contains the urea's NH 2 stretching vibrations, but also the water's O-H stretching vibrations. Furthermore, the O-H vibrations reflect the hydrogen-bonding network [START_REF] Fayer | Raman and IR Spectroscopy of Liquids[END_REF] and are thus sensitive to the presence of dissolved salts in water, but also at temperature.

For the freezing point monitoring, which is necessary in the winter maintenance context, the phase diagram of the urea-water binary system was experimentally elaborated. The conventional techniques for the phase transition determination are based on the monitoring of heat, volume, or pressure occurring during the phase transition. In this paper, an alternative method based on Raman spectroscopy is proposed, and the urea-water phase diagram is constructed by Raman probing and analyzing in the cooling process of the solution. The temperature range chosen for this study corresponds to the one that can be encountered during the winter season.

EXPERIMENTAL

Measurements on urea aqueous solutions were made with a Horiba-Jobin Yvon HE Raman spectrometer. The spectrometer used a 532 nm laser with a 300 mW maximum output power and a 920 lines/millimeter grating with a 3 cm -1 spectral resolution, covering the 950-3870 cm -1 spectral range. The laser beam was focused on the surface sample with a 50× long-working distance objective. The Raman signal was collected by a CCD camera of 1024 pixels.

The objective was located at 8 mm from the sample surface. Its focal distance being 10.6 mm, the investigated volume (with a depth of field of 20-30 µm) was thus located underneath the liquid's surface, giving an insight into its bulk properties.

The urea aqueous solution was placed in a quartz crucible of a few milliliters. For sample temperature control, the quartz crucible was placed in a thermomicrometric cooling/heating stage at atmospheric pressure. The instrument used was a thermomicrometric stage for geological applications (THMSG600, Linkam Scientific Instruments, Surrey, UK), a device offering a 0.1 °C resolution. Raman spectra were collected at temperatures between -30 and 10 °C with a 0.5 °C temperature step and a 1 s per spectrum integration time. In order to ensure the sample's temperature stabilization, a pause of 30 s was set between each spectrum acquisition. The whole experimental setup is presented on Fig. 1.

Aqueous solutions with urea concentrations between 0 and 603 kg/m 3 were achieved by using the ASTM (American Society for Testing and Materials) D1193 type IV reagent water. A highly concentrated solution at 603 kg/m 3 was prepared by dilution of a 99.9% pure urea (supplied by Fisher Chemical) in the demineralized reagent water. The solutions with lower concentrations were obtained by dilution of this mother solution.

FIG. 1. Schematic representation of the used assembly.

PRELIMINARY CONSIDERATIONS

Raman Spectra of Urea Aqueous Solutions. The urea concentration effect on the spectrum was observed by a comparison between spectra of urea solutions at different concentrations and one of water at room temperature. Normalized Raman spectra of urea aqueous solutions at different concentrations were acquired (Fig. 2a), and the water spectrum subtracted from them (Fig. 2b). The main peaks that are specific of the urea presence can thus be identified at 1006, 1155, 1460, 1590, and 1660 cm -1 . Furthermore, the water's O-H stretching vibrations [START_REF] Auer | IR and Raman Spectra of Liquid Water: Theory and Interpretation[END_REF][START_REF] Sun | The Raman OH Stretching Bands of Liquid Water[END_REF][START_REF] Walrafen | Weak Raman Bands from Water[END_REF] above 3000 cm -1 are overlapped with some urea-characteristic peaks located at 3230, 3380, and 3480 cm -1 . The attributions of all the peaks observed are presented in Fig. 2b. [START_REF] Keuleers | Vibrational Analysis of Urea[END_REF][START_REF] Grdadolnik | Urea and Urea-Water Solutions-An Infrared Study[END_REF][START_REF] Jung | Two-Dimensional Correlation Spectroscopy in Analyzing the Concentration-Dependent IR Spectra of Urea Aqueous Solution[END_REF][START_REF] Jung | Characterization of Concentration-Dependent Infrared Spectral Variations of Urea Aqueous Solutions by Principal Component Analysis and Two-Dimensional Correlation Spectroscopy[END_REF][START_REF] Idrissi | Molecular Structure and Dynamics of Liquids: Aqueous Urea Solutions[END_REF] The main concentration influence is observed on the intensities of the peaks, with no significant peak shift.

The modification observed in the region above 3000 cm -1 induced by the concentration increase has two origins: the increase of the NH 2 stretching vibrations, but also the modification of the O-H stretching vibrations. Indeed, the presence of urea dissolved in water provokes the disruption of some hydrogen bonds, leading to an alteration of the hydrogen-bonding network. This is manifested by the modification of the symmetric/asymmetric stretching vibrations that are present in this spectral region. Method for the Phase Transition Determination. The water spectrum presents a very intense signal in the 2900 to 3700 cm -1 range, which corresponds to the stretching of the OH bond of a water molecule. [START_REF] Sun | The Raman OH Stretching Bands of Liquid Water[END_REF][START_REF] Pereira Neto | The Effect of Temperature and LiClO 4 in the Water Structure: A Raman Spectroscopy Study[END_REF][START_REF] Schicks | Raman Spectra of Gas Hydrates-Differences and Analogies to Ice 1h and (Gas Saturated) Water[END_REF] The phase transition of the liquid into solid water induces a collapse of the region above the 3300 cm -1 signature of the OH antisymmetric stretching vibrations and, by contrast, an intensity increase around 3200 cm -1 , corresponding to the OH symmetric stretching vibrations [START_REF] Durickovic | Experimental Study of NaCl Aqueous Solutions by Raman Spectroscopy: Towards a New Optical Sensor[END_REF][START_REF] Shriver | Inorganic Chemistry[END_REF] (Fig. 3a). The water solidification, linked to an increased number of hydrogen bonds among water molecules, leads to an important increase of the peak at 3150 cm -1 , which can be thus considered as characteristic of the ice presence.

This region is therefore a main key for the phase transition determination of water and aqueous solutions. [START_REF] Durickovic | Process for the Determination of the Solid/Liquid Phase[END_REF][START_REF] Durickovic | Water-Ice Phase Transition Probed by Raman Spectroscopy[END_REF][START_REF] Claverie | Optical Sensor for Characterizing the Phase Transition in Salted Solutions[END_REF] The phase transition was then determined via the calculation of S D , an area ratio of a band relevant of the liquid water (light gray band on Fig. 3a) to the one of the ice (dark gray band on Fig. 3a), as given in Eq. 1:

where (i) represent the wavenumber value and I(i) the intensity at the corresponding (i).

The choices of the spectral bands used for the S D calculation are presented in Fig. 3a, and the resulting S D curve for water on Fig. 3b. The phase transition temperature is determined by the inflection point of this S D curve. For the case of water, an example presented in Fig. 3b, the freezing temperature obtained experimentally is -1 °C. Since the theoretical freezing point of water is 0 °C, we can deduce the influence of the cooling stage on the freezing point obtained experimentally. Indeed, the slight depression in the cooling stage, as well as the size of its quartz crucible, influences the measurement and the sample response. For our experimental setup and procedure, the temperature shift induced by it is therefore -1 °C. As the same method was used in the study of the phase transitions of urea aqueous solutions, a 1 °C correction was implemented.

RESULTS AND DISCUSSION

Phase Diagram Construction. The temperature range of interest in this paper is between 10 and -30 °C. In this temperature range, three phases can be identified, denoted liquid, solid, and mixture in this paper. Figure 4a presents these phases for an arbitrary chosen concentration of 134 kg/m 3 at arbitrary chosen temperatures (-5, -14, and -30 °C). Each spectrum has a different shape depending on the phase of the ura aqueous solution, and the effect of temperature within the same phase does not have a great effect on the structure. [START_REF] Idrissi | The Effect of Temperature on Urea-Urea Interactions in Water: A Molecular Dynamics Simulation[END_REF] Similarly to water, the difference between the liquid and the mixture phases (at -5 and -14 °C) can be observed in two ways. First, by the intensities: the peak related to OH asymmetric stretching vibrations decreases, and the ice peak at 3150 cm -1 increases. Second, these peaks undergo a wavenumber red-shift of about 100 cm -1 showing elongations of NH 2 bonds during the liquid-mixture transition.

However, for urea aqueous solutions, a third phase can be observed at lower temperatures (-30 °C; Fig. 4a). This solid phase is characterized by the appearance of new peaks at 3337, 3355 and 3430 cm -1 . If we draw a parallel between the urea-water binary system and the NaCl-water one, this phase can then be compared to the hydrohalite one (NaCl.2H 2 O), usually observed at temperatures below the eutectic point. The solid phase thus corresponds to the eutectic mixture, the eutectic compound of urea being estimated to be CO(NH 2 ) 2 .8H 2 O. [START_REF]Structure and Properties of Cellulose/NaOH Aqueous Solutions, Gels and Regenerated Objects[END_REF] Using the method developed by Durickovic et al. [START_REF] Durickovic | Water-Ice Phase Transition Probed by Raman Spectroscopy[END_REF] for water, and the one of Claverie et al. [START_REF] Claverie | Optical Sensor for Characterizing the Phase Transition in Salted Solutions[END_REF] for NaCl aqueous solutions, the two urea phase transitions of the 134 kg/m 3 solution were determined. As described in the preliminary considerations, areas corresponding to characteristic parts of the liquid water spectrum, and to ice, were considered (Eq. 1). The liquid-mixture and mixture-solid phase transition temperatures determined using the tangent method (Fig. 4b) were -17 and -9 °C.

The same spectra analysis method for the phase transition determination was applied on seven urea aqueous solutions. This enabled us to build the phase diagram of the urea-water system (Fig. 5) where the zones corresponding to the liquid, mixture, and solid phases can be observed. The eutectic point was determined, and it corresponded to a concentration of 335 kg/m 3 . This would indicate that the corresponding eutectic compound is CO(NH 2 ) 2 .7H 2 O, as the molar urea/water ratio at this concentration is 1/7.

Information given by the Phase Diagram and the Eutectic Point. The knowledge of the eutectic concentration C e and temperature T e permits the identification of the characteristic zones of a corresponding phase diagram, giving an insight into the mixture's precise composition.

The mixture zone, corresponding to the zones contained between the ''Liquidus'' and ''Solidus'' lines, can thus be divided into two parts (Mixture a and Mixture b). Mixture a (corresponding to T > T e and C < C e ) is composed of ice and urea solution (slush), whereas Mixture b ( T > T e and C > C e ) is composed of urea and urea solution (slush). The eutectic concentration C e divides the solid zone, which can be found at T < T e , in two as well. In this case, for C < C e , the system is composed of ice and solid eutectic, and for C > C e , the system becomes composed of urea and solid eutectic.

CONCLUSION

Urea aqueous solutions were investigated in a wide range of concentrations and temperatures by means of Raman spectroscopy in order to build its phase diagram.

Raman spectra were recorded at temperatures ranging from 10 to -30 °C, for urea concentrations up to 600 kg/m 3 . Raman spectroscopy proved to be an appropriate technique for the characterization of urea aqueous solutions by the analysis of the material molecule's bond vibrations, as well as for the determination of their phases. The phase transitions in aqueous solutions were identified using a simple method based on areas of two large bands in the high wavenumber range of the spectra, which allowed us to build the urea-water phase diagram at low temperatures. A eutectic point was identified, along with a specific phase below the temperature of this eutectic point. Some additional investigations will be undertaken to confirm the nature and the molar urea/water ratio of the eutectic mixture at temperatures below the eutectic point by conventional techniques, such as X-ray diffraction.

Phase transitions are difficult to reach and to identify since they highly depend on the conditions at which they occur. It could specifically be the case at low temperatures because of the coexistence of several phases, along with local thermal gradients within the material considered. Raman spectroscopy appeared to be the proper tool to overcome these usual difficulties. It could then be able to analyze not only bond vibrations, whatever the phases are, but also the singularities that a phase transition could induce.

FIG. 2 .

 2 FIG. 2. Normalized Raman spectra of urea aqueous solutions at different concentrations, obtained at room temperature (20 °C) before (a) and after (b) subtraction of the water spectrum (laser power: 300 mW; integration time: 20 s).

FIG. 3 .

 3 FIG. 3. Normalized Raman spectra of liquid water at 10 °C and ice at -10 °C and the area bands used for the calculation of S D (a); evolution of S D according to the temperature from -10 to 10 °C (b).

FIG. 4 .

 4 FIG. 4. Normalized Raman spectra of the different phases of the urea solution at 134 kg/m 3 (a); evolution of S D according to the temperature from -30 to 10 °C for a urea solution at 134 kg/m 3 (b).


Rıza Alp Güler
email: riza.guler@inria.fr

Natalia Neverova
email: nneverova@fb.com

Iasonas Kokkinos
email: iasonask@fb.com

DensePose: Dense Human Pose Estimation In The Wild

Keywords:

Figure 1: Dense pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. We introduce DensePose-COCO, a large-scale ground-truth dataset containing manually annotated image-to-surface correspondences for 50K images, and train DensePose-RCNN to densely regress UV coordinates at multiple frames per second. Left: The image and the regressed correspondence by DensePose-RCNN. Middle: DensePose-

Introduction

This work aims at pushing further the envelope of human understanding in images by establishing dense correspon- 1 Rıza Alp Güler was with Facebook AI Research during this work. dences between a 2D image and a 3D, surface-based representation of the human body. We can understand this task as involving several other problems, such as object detection, pose estimation, part and instance segmentation either as special cases or prerequisites. Addressing this task has applications in problems that require going beyond plain landmark localization, such as graphics, augmented reality, or human-computer interaction, and could also be a stepping stone towards general 3D-based object understanding.

The task of establishing dense correspondences from an image to a surface-based model has been addressed mostly in the setting where a depth sensor is available [START_REF] Taylor | The vitruvian manifold: Inferring dense correspondences for oneshot human pose estimation[END_REF][START_REF] Pons-Moll | Metric regression forests for correspondence estimation[END_REF][START_REF] Wei | Dense human body correspondences using convolutional networks[END_REF]. Instead, we establish dense image-to-surface correspondences using as sole input the RGB values of a single image.

Several other works have recently aimed at recovering dense correspondences between pairs [START_REF] Bristow | Dense semantic correspondence where every pixel is a classifier[END_REF] or sets of RGB images [START_REF] Zhou | Learning dense correspondence via 3d-guided cycle consistency[END_REF][START_REF] Gaur | Weakly supervised manifold learning for dense semantic object correspondence[END_REF] in an unsupervised setting. More recently, [START_REF] Thewlis | Unsupervised object learning from dense equivariant image labelling[END_REF] used the equivariance principle in order to align sets of images to a common coordinate system, while following the general idea of groupwise image alignment, e.g. [START_REF] Learned-Miller | Data driven image models through continuous joint alignment[END_REF][START_REF] Kokkinos | Unsupervised learning of object deformation models[END_REF]. While these works target general categories, ours is focused on arguably the most important one, humans. For humans one can simplify the task by exploiting parametric deformable surface models, such as the Skinned Multi-Person Linear (SMPL) model [START_REF] Bogo | Keep it SMPL: automatic estimation of 3d human pose and shape from a single image[END_REF], or the more recent Adam model [START_REF] Hanbyul | Total capture: A 3d deformation model for tracking faces, hands, and bodies[END_REF] obtained through controlled 3D surface acquisition. Turning to the task of image-to-surface mapping, The surface coordinates of the rendered views are used to localize the collected 2D points on the 3D model.

in [START_REF] Bogo | Keep it SMPL: automatic estimation of 3d human pose and shape from a single image[END_REF], the authors propose a two-stage method of detecting human landmarks and fitting a parametric deformable surface model to the image through iterative minimization. In parallel to our work, [START_REF] Kanazawa | Endto-end recovery of human shape and pose[END_REF] extend [START_REF] Bogo | Keep it SMPL: automatic estimation of 3d human pose and shape from a single image[END_REF] to operate end-to-end, incorporating the iterative reprojection error minimization as a module of a deep network that recovers 3D camera pose and the low-dimensional body parametrization.

Our methodology differs from all these works in that we take a full-blown supervised learning approach and gather ground-truth correspondences between images and a detailed, accurate parametric surface model of the human body [28]: rather than using the SMPL model at test time we only use it as a means of defining our problem during training. Our approach can be understood as the next step in the line of works on human pose estimation [27,[START_REF] Andriluka | 2d human pose estimation: New benchmark and state of the art analysis[END_REF]20,7,[START_REF] Sigal | Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion[END_REF][START_REF] Ionescu | Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments[END_REF][START_REF]Cmu graphics lab motion capture database[END_REF]. Human part segmentation masks have been provided in a number of datasets [START_REF] Yamaguchi | Parsing clothing in fashion photographs[END_REF][START_REF] Chen | Detect what you can: Detecting and representing objects using holistic models and body parts[END_REF][START_REF] Gong | Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing[END_REF]; these can be understood as providing a coarsened version of image-to-surface correspondence, where rather than continuous coordinates one predicts discretized part labels [35]. Surface-level supervision was only recently introduced for synthetic images in [START_REF] Varol | Learning from synthetic humans[END_REF], while in [START_REF] Lassner | Unite the people: Closing the loop between 3d and 2d human representations[END_REF] a dataset of 8515 images is annotated with keypoints and semi-automated fits of 3D models to images. In this work instead of compromising the extent and realism of our training set we introduce a novel annotation pipeline that allows us to gather groundtruth correspondences for 50K images of COCO, yielding our new DensePose-COCO dataset.

Our work is closest in spirit to the recent DenseReg framework [14], where CNNs were trained to establish dense correspondences between a 3D model and images 'in the wild'. That work focused mainly on faces, and provided evaluations on datasets with moderate pose variability. Here, however, we are facing new challenges, due to the higher complexity and flexibility of the human body, as well as the larger scale variation. We address these challenges by designing appropriate architectures (Sec. 3) that yield substantial improvements over a DenseReg-type fully con-volutional architecture. By combining our approach with the recent Mask-RCNN system of [START_REF] He | Mask r-cnn[END_REF] we show that a discriminatively trained model can efficiently recover highlyaccurate correspondence fields for complex scenes involving tens of persons: on a GTX 1080 GPU our system operates at 20-26 fps for a 240×320 image or 4-5 fps for a 800×1100 image.

Our contributions can be summarized in three points. Firstly, as described in Sec. 2, we introduce the first manually-collected ground truth dataset for the task, by gathering dense correspondences between the SMPL model [28] and persons appearing in the COCO dataset. This is accomplished through a novel annotation pipeline that exploits 3D surface information during annotation.

Secondly, as described in Sec. 3, we use the resulting dataset to train CNN-based systems that deliver dense correspondence 'in the wild' by regressing body surface coordinates at any image pixel. We experiment with both fully-convolutional architectures, relying on Deeplab [4], and also with region-based systems, relying on Mask-RCNN [START_REF] He | Mask r-cnn[END_REF], observing a superiority of the latter. We also consider cascading variants of our approach, yielding further improvements over existing architectures.

Thirdly, we explore different ways of exploiting our constructed ground truth information. Our supervision signal is defined over a randomly chosen subset of image pixels per training sample. We use these sparse correspondences to train a teacher network that can inpaint the supervision signal in the rest of the image domain. Using this inpainted signal results in better performance when compared to either sparse points, or any other existing dataset, as shown experimentally in Sec. 4.

Our experiments indicate that dense human pose estimation is to a large extent feasible, but still has space for improvement. Our code and the data will be made publicly available at http://densepose.org.

DensePose-COCO Dataset

Gathering rich, high-quality training sets has been a catalyst for progress in the classification [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF], detection and segmentation [8, 27] tasks. There currently exists no manually collected ground-truth for dense human pose estimation for real images. The works of [START_REF] Lassner | Unite the people: Closing the loop between 3d and 2d human representations[END_REF][START_REF] Varol | Learning from synthetic humans[END_REF] can be used as surrogates, but as we show in Sec. 4 provide worse supervision.

In this Section we introduce DensePose-COCO, a largescale dataset for dense human pose estimation. DensePose-COCO provides ground-truth for 50K humans and contains more than 5 million manually annotated pairs. We first present our annotation pipeline, since our design choices may be useful for general 3D annotation. We then analyze the accuracy of the collected ground-truth, and finally introduce evaluation metrics for dense pose estimation.

Annotation System

In this work we use human annotators to establish dense correspondences from 2D images to surface-based representations of the human body. If done naively this would require manipulating a surface through rotations to find the vertices corresponding to every 2D image point, which is time-demanding and inefficient. Instead, we construct an annotation pipeline through which we can efficiently gather annotations for image-to-surface correspondence.

As shown in Fig. 2, in the first stage we ask annotators to delineate regions corresponding to visible, semantically defined body parts. These include Head, Torso, Lower/Upper Arms, Lower/Upper Legs, Hands and Feet. In order to simplify the UV parametrization we design the parts to be isomorphic to a plane, partitioning the upper and lower limbs and torso into frontal-back parts. For head, hands and feet, we use the manually obtained UV fields provided in the SMPL model [28]. For other parts we obtain the unwrapping via multi-dimensional scaling applied to pairwise geodesic distances. The UV fields for the resulting 24 parts are visualized in Fig. 1 (right).

We instruct the annotators to estimate the body part behind the clothes, so that for instance wearing a large skirt will not complicate the subsequent correspondence annotations. In the second stage we sample every part region with a set of roughly equidistant points obtained by running k-means over the coordinates occupied by each part and request the annotators to bring these points in corre-spondence with the surface. The number of sampled points varies based on the size of the part and the maximum number of sampled points per part is 14. In order to simplify this task we 'unfold' the part surface by providing six prerendered views of the same body part and allow the user to place landmarks on any of them. This allows the annotator to choose the most convenient viewpoint by selecting one among six options instead of manually rotating the surface. As the user indicates a point on any of the rendered part views, its surface coordinates are used to simultaneously show its position on the remaining views -this gives a global overview of the correspondence. We show indicative visualizations of the gathered annotations in Fig. 3.

Accuracy of human annotators

A common concern when gathering ground-truth is the accuracy of the human annotations, which is often seen as an upper bound of what vision algorithms can deliver. In pose estimation one typically asks multiple annotators to label the same landmark, which is then used to assess the variance in position, e.g. [27,[START_REF] Ronchi | Benchmarking and error diagnosis in multi-instance pose estimation[END_REF]. In our case we can directly compare to the true mesh coordinates used to render a pixel, rather than first estimating a 'consensus' landmark location among multiple human annotators.

In particular, we provide annotators with synthetic images generated through the rendering system and textures of [START_REF] Varol | Learning from synthetic humans[END_REF]. We ask the annotators to bring the synthesized images into correspondence with the surface using our annotation tool, and for every image k estimate the geodesic distance d i,k between the correct surface point, i and the point estimated by human annotators îk :

d i,k = g(i, îk ), (1)
g(•, •) is the geodesic distance between two surface points.

For any image k, we annotate and estimate the error on a randomly sampled set of surface points S k and interpolate the errors on the remainder of the surface. Finally, we average the errors across all examples given to the annotators.

As shown in Fig. 4 the annotation errors are substantially smaller on small surface parts with distinctive features that could help localization (face, hands, feet), while on larger uniform areas that are typically covered by clothes (torso, back, hips) the annotator errors can get larger.

Evaluation Metrics

We consider two different ways of summarizing correspondence accuracy over the whole human body, including pointwise and per-instance evaluation.

Pointwise evaluation. This approach evaluates correspondence accuracy over the whole image domain through the Ratio of Correct Point (RCP) correspondences, where a correspondence is declared correct if the geodesic distance is below a certain threshold. As the threshold t varies, we obtain a curve f (t), whose area provides us with a scalar summary of the correspondence accuracy. For any given image we have a varying set of points coming with groundtruth signals. We summarize performance on the ensemble of such points, gathered across images. We evaluate the area under the curve (AUC), AUC a = 1 a a 0 f (t)dt, for two different values of a = 10cm, 30cm yielding AUC 10 and AUC 30 respectively, where AUC 10 is understood as being an accuracy measure for more refined correspondence. This performance measure is easily applicable to both singleand multi-person scenarios and can deliver directly comparable values. In Fig. 5 we provide the per-part pointwise evaluation of the human annotator performance on synthetic data, which can be seen as an upper bound for the performance of our systems.

Per-instance evaluation. Inspired by the object keypoint similarity (OKS) measure used for pose evaluation on the COCO dataset [27,[START_REF] Ronchi | Benchmarking and error diagnosis in multi-instance pose estimation[END_REF], we introduce geodesic point similarity (GPS) as a correspondence matching score:

GPS j = 1 |P j | p∈Pj exp -g(i p , îp ) 2 2κ 2 , ( 2
)
where P j is the set of ground truth points annotated on person instance j, i p is the vertex estimated by a model at point p, îp is the ground truth vertex p and κ is a normalizing parameter. We set κ=0.255 so that a single point has a GPS value of 0.5 if its geodesic distance from the ground truth equals the average half-size of a body segment, corresponding to approximately 30 cm. Intuitively, this means that a score of GPS ≈ 0.5 can be achieved by a perfect part segmentation model, while going above that also requires a more precise localization of a point on the surface. Once the matching is performed, we follow the COCO challenge protocol [27,39] and evaluate Average Precision (AP) and Average Recall (AR) at a number of GPS thresholds ranging from 0.5 to 0.95, which corresponds to the range of geodesic distances between 0 and 30 cm. We use the same range of distances to perform both per-instance and per-point evaluation.

Learning Dense Human Pose Estimation

We now turn to the task of training a deep network that predicts dense correspondences between image pixels and surface points. Such a task was recently addressed in the Dense Regression (DenseReg) system of [14] through a fully-convolutional network architecture [4]. In this Section we introduce improved architectures by combining the DenseReg approach with the Mask-RCNN architecture [START_REF] He | Mask r-cnn[END_REF], yielding our 'DensePose-RCNN' system. We develop cascaded extensions of DensePose-RCNN that further improve accuracy and describe a training-based interpolation method that allows us to turn a sparse supervision signal into a denser and more effective variant.

Fully-convolutional dense pose regression

Since the human body has a complicated structure, we break it into multiple independent pieces and parametrize each piece using a local two-dimensional coordinate system, that identifies the position of any node on this surface part.

Using the surface representation, a simple choice for dense image-to-surface correspondence estimation consists in using a fully convolutional network (FCN) that combines a classification and a regression task, similar to DenseReg. In a first step, we classify a pixel as belonging to either background or one among the surface parts. In a second step, a regression system indicates the exact coordinates of the pixel within the part. Intuitively, we can say that we first use appearance to make a coarse estimate of where the pixel belongs to and then align it to the exact position through some small-scale correction.

Concretely, coordinate regression at an image position i can be formulated as follows:

c * = argmax c P (c|i), [U, V ] = R c * (i) (3)
where in the first stage we assign position i to the body part c * that has posterior probability, as calculated by the classification branch, and in the second stage we use the regressor R c * that places the point i in the continuous U, V coordinates parametrization of part c * . In our case, c can take 25 values (one is background), meaning that P x is a 25-way classification unit, and we train 24 regression functions R c , each of which provides 2D coordinates within its respective part c. While training, we use a cross-entropy loss for part classification and a smooth L 1 loss [START_REF] Girshick | Fast R-CNN[END_REF] for each part-specific regression function. The regression loss for a part is only considered for pixels occupied by that part.

Region-based Dense Pose Regression

Using an FCN makes the system particularly easy to train, but loads the same deep network with too many tasks, including part segmentation and pixel localization, while at the same time requiring scale-invariance, which becomes challenging for humans in COCO. Here we adopt the region-based approach of [START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF][START_REF] He | Mask r-cnn[END_REF], which consists in a cascade of proposing regions-of-interest (ROI), extracting region-adapted features through ROI pooling [START_REF] He | Spatial pyramid pooling in deep convolutional networks for visual recognition[END_REF][START_REF] He | Mask r-cnn[END_REF] and feeding the resulting features into a region-specific branch. Region-based architectures decompose the complexity of the task into controllable modules and implement a scale selection mechanism through ROI-pooling. At the same time, they can be jointly trained in an end-to-end manner [START_REF] Ren | Faster R-CNN: Towards real-time object detection with region proposal networks[END_REF].

We adopt the settings introduced in [START_REF] He | Mask r-cnn[END_REF], involving the construction of Feature Pyramid Network [START_REF] Lin | Feature pyramid networks for object detection[END_REF] features, and ROI-Align pooling, which have been shown to be important for tasks that require spatial accuracy. We adapt this architecture to our task, so as to obtain dense part labels and coordinates within each of the selected regions.

As shown in Fig. 6, on top of ROI-pooling we introduce an FCN that is entirely devoted to these two tasks, generating a classification and a regression head that provide the part assignment and part coordinate predictions, as in DenseReg. For simplicity, we use the exact same architecture used in the keypoint branch of Mask-RCNN, consisting of a stack of 8 alternating 3×3 fully convolutional and ReLU layers with 512 channels. At the top of this branch we have the same classification and regression losses as in the FCN baseline, but we now use a supervision signal that is cropped within the proposed region.

Multi-task cascaded architectures

Inspired by the success of recent pose estimation models based on iterative refinement [START_REF] Wei | Convolutional pose machines[END_REF][START_REF] Newell | Stacked hourglass networks for human pose estimation[END_REF] we experiment with cascaded architectures. Cascading can improve performance both by providing context to the following stages, and also through the benefits of deep supervision [25].

As shown in Fig. 7 we do not confine ourselves to cascading within a single task, but also exploit information from related tasks, such as keypoint estimation and instance segmentation, which have successfully been addressed by the Mask-RCNN architecture [START_REF] He | Mask r-cnn[END_REF]. This allows us to exploit task synergies and the complementary merits of different sources of supervision.

Distillation-based ground-truth interpolation

Even though we aim at dense pose estimation at test time, in every training sample we annotate only a sparse subset of the pixels, approximately 100-150 per human. This does not necessarily pose a problem during training, since we can make our classification/regression losses oblivious to points where the ground-truth correspondence was not collected, simply by not including them in the summation over the per-pixel losses [41]. However, we have observed that we obtain better results by "inpainting" the values of the supervision signal on positions that were not originally annotated. For this we adopt a learning-based ap-proach where we firstly train a "teacher" network to reconstruct the ground-truth values wherever these are observed, and then deploy it on the full image domain, yielding a dense supervision signal.

As shown in Fig. 8, we use human segmentation maps available in COCO in order to get the most accurate supervision signal possible by (a) replacing background structures with a common gray value and (b) ignoring the network's predictions outside the human region. The performance of the teacher network can therefore be understood as an upper bound on what an algorithm can deliver on real data, since we remove false positives, normalize scale and remove background variation during both training and testing.

Experiments

In all experiments we assess the methods on a test set of 1.5K images containing 2.3K humans and use 48K humans in the training set. Our test set coincides with the COCO keypoints-minival partition used by [START_REF] He | Mask r-cnn[END_REF] and the training set with the COCO-train partition.

Before assessing dense pose estimation in the wild (Sec. 4.2), we start in Sec. 4.1 with the 'Single-Person' setting where the input images are cropped around groundtruth boxes. This factors out the effects of detection performance and provides us with a controlled setting to assess the usefulness of the DensePose-COCO dataset.

Single-Person Dense Pose Estimation

In Sec. 4.1.1 we compare the DensePose-COCO dataset to other sources of supervision for dense pose estimation. In Sec. 4.1.2 we compare the performance of the model-based system of [START_REF] Bogo | Keep it SMPL: automatic estimation of 3d human pose and shape from a single image[END_REF] with ours. We note that the system of [START_REF] Bogo | Keep it SMPL: automatic estimation of 3d human pose and shape from a single image[END_REF] was not trained with the same amount of data as our model; this comparison therefore serves primarily to show the merit of our large-scale dataset for discriminative training.

Manual supervision versus surrogates

We start by assessing whether DensePose-COCO improves the accuracy of dense pose estimation with respect to the prior semi-automated, or synthetic supervision signals.

Teacher Network (FCNN)

Figure 8: We train a 'teacher network' with our collected sparse supervision signal and use it to 'inpaint' a dense supervision signal used to train our region-based system.

A semi-automated method is used for the 'Unite the People' (UP) dataset of [START_REF] Lassner | Unite the people: Closing the loop between 3d and 2d human representations[END_REF], where human annotators verified the results of fitting the SMPL 3D deformable model [28] to 2D images. However, model fitting often fails in the presence of occlusions, or extreme poses, and is never guaranteed to be entirely successful -for instance, even after rejecting a large fraction of the fitting results, the feet are still often misaligned in [START_REF] Lassner | Unite the people: Closing the loop between 3d and 2d human representations[END_REF].

Synthetic ground-truth can be established by rendering images using surface-based models [START_REF] Pishchulin | Learning people detection models from few training samples[END_REF][START_REF] Pishchulin | Articulated people detection and pose estimation: Reshaping the future[END_REF][START_REF] Rogez | Mocap-guided data augmentation for 3d pose estimation in the wild[END_REF][START_REF] Ghezelghieh | Learning camera viewpoint using cnn to improve 3d body pose estimation[END_REF][START_REF] Chen | Synthesizing training images for boosting human 3d pose estimation[END_REF][START_REF] Neverova | Hand pose estimation through weakly-supervised learning of a rich intermediate representation[END_REF]. This has recently been applied to human pose in the SUR-REAL dataset of [START_REF] Varol | Learning from synthetic humans[END_REF], where the SMPL model [28] was rendered with the CMU Mocap dataset poses [START_REF]Cmu graphics lab motion capture database[END_REF]. However, domain shift can emerge because of the different statistics of rendered and natural images.

Since both of these two methods use the same SMPL surface model as the one we use in our work, we can directly compare results, and also combine datasets. We render our dense coordinates and our dense part labels on the SMPL model for all 8514 images of UP dataset and 60k SURREAL models for comparison.

In Fig. 10 we assess the test performance of ResNet-101 FCNs of stride 8 trained with different datasets, using a Deeplab-type architecture. During training we augment samples from all of the datasets with scaling, cropping and rotation. We observe that the surrogate datasets lead to weaker performance, while their combination yields improved results. Still, their performance is substantially lower than the one obtained by training on our Dense-Pose dataset, while combining the DensePose with SUR-REAL results in a moderate drop in network performance. Based on these results we rely exclusively on the Dense-Pose dataset for training in the remaining experiments, even though domain adaptation [9] could be used in the future to exploit synthetic sources of supervision.

The last line in the table of Fig. 10 ('DensePose * ') indicates the additional performance boost that we get by using the teacher network settings described in Sec. 3.4. Clearly, the results are not directly comparable with those of other methods, since we use additional information to remove background structures. Still, the resulting predictions are substantially closer to human performance -we can therefore confidently use our teacher network to obtain dense supervision for the experiments in Sec. 4.2.

FCNN-vs Model-based pose estimation

In Fig. 9 we compare our method to the SMPLify pipeline of [START_REF] Bogo | Keep it SMPL: automatic estimation of 3d human pose and shape from a single image[END_REF], which fits the 3D SMPL model to an image based on a pre-computed set of landmark points. We use the code provided by [START_REF] Lassner | Unite the people: Closing the loop between 3d and 2d human representations[END_REF] with both DeeperCut pose estimation landmark detector [START_REF] Insafutdinov | Deepercut: A deeper, stronger, and faster multi-person pose estimation model[END_REF] for 14-landmark results and with the 91landmark alternative proposed in [START_REF] Lassner | Unite the people: Closing the loop between 3d and 2d human representations[END_REF]. Here we compare the performance of our proposed DensePose-RCNN system against the fully-convolutional alternative on realistic images from the COCO dataset including multiple persons with high variability in scales, poses and backgrounds.

Since the whole body is visible in the MPII dataset used for training the landmark detectors, for a fair comparison we separately evaluate on images where 16/17 or 17/17 landmarks are visible and on the whole test set. We observe that while being orders of magnitude faster (0.04-0.25" vs 60-200") our bottom-up method largely outperforms the iterative, model fitting result. As mentioned above, this difference in accuracy indicates the merit of having at our disposal DensePose-COCO for discriminative training.

Multi-Person Dense Pose Estimation

Having established the merit of the DensePose-COCO dataset, we now turn to examining the impact of network architecture on dense pose estimation in-the-wild. In Fig. 11 we summarize our experimental findings using the same RCP measure used in Fig. 10.

We observe firstly that the FCN-based performance inthe-wild (curve 'DensePose-FCN') is now substantially lower than that of the DensePose curve in Fig. 11. Even though we apply a multi-scale testing strategy that fuses probabilities from multiple runs using input images of different scale [START_REF] Zhao | Pyramid scene parsing network[END_REF], the FCN is not sufficiently robust to deal with the variability in object scale.

We then observe in curve 'DensePose-RCNN' a big boost in performance thanks to switching to a region-based system. The networks up to here have been trained using the sparse set of points that have been manually annotated. In curve 'DensePose-RCNN-Distillation' we see that using the dense supervision signal delivered by our DensePose * system on the training set yields a substantial improvement.

Finally, in 'DensePose-RCNN-Cascade' we show the performance achieved thanks to the introduction of cascading: Sec. 3.3 almost matches the 'DensePose * ' curve of Fig. 10. This is a remarkably positive result: as described in Sec. 3.4, the 'DensePose * ' curve corresponds to a very privileged evaluation and can be understood as an upper bound of what one can expect to obtain when operating in-thewild. We see that our best system is marginally below that level of performance, which clearly reveals the power of the three modifications we introduce, namely region-based processing, inpainting the supervision signal, and cascading.

In Table 1 we report the AP and AR metrics described in Sec. 2 as we change different choices in our architecture. We have conducted experiments using both ResNet-50 and ResNet-101 backbones and observed an only insignificant boost in performance with the larger model (first two rows in Table 1). The rest of our experiments are therefore based on the ResNet-50-FPN version of DensePose-RCNN. The following two experiments shown in the middle section of Table 1 indicate the impact on multi-task learning.

Augmenting the network with the mask or keypoint branches yields improvements with any of these two auxiliary tasks. The last section of Table 1 reports improvements in dense pose estimation obtained through the cascading setup from Fig. 7. Incorporating additional guidance in particular from the keypoint branch significantly boosts performance.

Our qualitative results in Fig. 12 indicate that our method is able to handle large amounts of occlusion, scale, and pose variation, regardless of the shape of the clothes.

Conclusion

In this work we have addressed the task of dense human pose estimation using discriminatively trained models. We introduce DensePose-COCO, a large-scale dataset of ground-truth image-surface correspondences and develop novel architectures for recovering highly-accurate dense correspondences between images and the body surface in multiple frames per second. We anticipate this will lead to novel augmented reality or graphics tasks, and we intend to further pursue the association of images with semantic 3D object representations.

Figure 2 :

 2 Figure 2: We annotate dense correspondence between images and a 3D surface model by asking the annotators to first segment the image into semantic regions and then localize each of the sampled points on any of the rendered part images. The surface coordinates of the rendered views are used to localize the collected 2D points on the 3D model.

Figure 3 :

 3 Figure 3: Visualization of annotations: Image (left), U (middle) and V (right) values for the collected points.

17

 17

Figure 4 :

 4 Figure 4: Average human annotation error on the surface.

Figure 5 :

 5 Figure 5: Human annotation error distribution within parts.

Figure 6 :

 6 Figure 6: DensePose-RCNN architecture: we use a cascade of region proposal generation and feature pooling, followed by a fully-convolutional network that densely predicts discrete part labels and continuous surface coordinates.

Figure 7 :

 7 Figure 7: Cross-cascading architecture: The RoIAlign output in Fig. 6 into the DensePose network and auxiliary networks for other tasks (masks, keypoints). Once first-stage predictions are obtained from all tasks, they are combined and fed into a second-stage refinement unit.

Figure 11 :

 11 Figure 11: Results of multi-person dense correspondence labelling.Here we compare the performance of our proposed DensePose-RCNN system against the fully-convolutional alternative on realistic images from the COCO dataset including multiple persons with high variability in scales, poses and backgrounds.

Figure 12 :

 12 Figure 12: Qualitative evaluation of DensePose-RCNN. Left: input, Right: DensePose-RCNN estimates. Our system successfully estimates body pose regardless of skirts or dresses, while handling a large variability of scales, poses, and occlusions.

Table 1 :

 1 Method AP AP 50 AP 75 AP M AP L AR AR 50 AP 75 AR M AR L Per-instance evaluation of DensePose-RCNN performance on COCO minival. All multi-task experiments are based on ResNet-50. DensePose-ST applies cascading to the base, single-task network.

	DensePose (ResNet-50)	51.0	83.5	54.2	39.4	53.1	60.1	88.5	64.5	42.0	61.3
	DensePose (ResNet-101) 51.8	83.7	56.3	42.2	53.8	61.1	88.9	66.4	45.3	62.1
				Multi-task learning
	DensePose + masks	51.9	85.5	54.7	39.4	53.9	61.1	89.7	65.5	42.0	62.4
	DensePose + keypoints	52.8	85.6	56.2	42.2	54.7	62.6	89.8	67.7	45.4	63.7
				Multi-task learning with cascading
	DensePose-ST	51.6	83.9	55.2	41.9	53.4	60.4	88.9	65.3	43.3	61.6
	DensePose + masks	52.8	85.5	56.1	40.3	54.6	62.0	89.7	67.0	42.4	63.3
	DensePose + keypoints	55.8	87.5	61.2	48.4	57.1	63.9	91.0	69.7	50.3	64.8

Acknowledgements

We thank the authors of [16] for their code, P. Dollar and T.-Y. Lin for help with COCO, the authors of [28] for making the SMPL model open for research and H. Y. Güler for his help with back-end development.


Adrien Barbaresi

Borderlands of text mapping: Experiments on Fontane's Brandenburg

This article deals with the detection and projection of spatial patterns in text collections. Especially for historical corpora, researchers face a lack of general-purpose tooling. In these experiments, different maps focusing on Brandenburg at the second half of the 19th century are created based on literary works by Theodor Fontane. Using a common ground for hypothesis testing and visualization, issues related to data curation and preparation, text processing and geocoding are presented and discussed: the abstract, static nature of the results should be made up for by particular scrutiny and contextualization, by critical machine reading and by adding depth through visual cues.

Introduction

In digital text corpora and especially literary works, space can be analyzed as patterns to be found across texts: "Large source bases are likely to contain 'meaningful patterns,' and the ability to examine them, that is, to gain a bird's-eye view with the assistance of a computer, is tantamount to having a team of readers, even many teams of readers, at one's disposal." [START_REF] Wrisley | Locating Medieval French, or Why We Collect and Visualize the Geographic Information of Texts[END_REF] Indeed, looking for patterns is widely considered to be a task at which distant reading and computer-based studies can be fruitful. In linguistics, a common criterion consists of frequency-based information, even in literary studies, patterns can be seen as "the strongest point of intersection between the computational strictures of text analysis and the open ended landscape of interpretative literary studies." [START_REF] Ramsay | In Praise of Pattern[END_REF] Looking for spatial patterns and displaying them can be assimilated to the recently coined concept of geocriticism, in the sense that it is not a character-centered or subjective framework. Under this assumption, the emergence of geographical patterns in texts is explicitly encouraged: "Geocriticism is a geocentered rather than an ego-centered approach; that is, the analysis focuses on global spatial representations rather than on individual ones (a given traveler's, for example)." [START_REF] Westphal | Geocritical Explorations : Space, Place, and Mapping in Literary and Cultural Studies[END_REF] In this perspective, this article deals with the detection and projection of spatial patterns in a historical text collection: based on experiments using a digitized version of works by Theodor Fontane, it focuses on extraction and visualization problems, which are exemplified by the situation of 19th century's Brandenburg. Finally, results in the form of maps are presented and commented.

Method

Trends in geocoding and visualization

Among the tendencies in geographic information retrieval and geocoding [START_REF] Melo | Automated Geocoding of Textual Documents: A Survey of Current Approaches[END_REF], the extraction and normalization of named places, itineraries, or qualitative spatial relations, as well as the extraction of locative expressions are particularly relevant to study text collections. In the field of information retrieval, named entity recognition defines a set of text mining techniques designed to discover named entities, connections and the types of relations between them. The particular task of finding place names in texts (geoparsing or place names extraction) involves first the detection of words and phrases that may potentially be proper nouns and second their classification as geographic references [START_REF] Nouvel | Les entités nommées pour le traitement automatique des langues[END_REF]. After the identification of toponyms, a further step (geocoding or toponym resolution) resides in disambiguating and adding geographical coordinates to a place name. Geocoding mostly relies on gazetteers, i.e. geospatial dictionaries containing mostly names, locations, and metadata such as typological information, variants or dates [START_REF] Hill | Core elements of digital gazetteers: place names, categories, and footprints[END_REF]. Toponym resolution as well as named-entity recognition can use machine learning methods, however these are generally not ideal when tackling data not present in the training set, so that knowledge-based methods using additional fine-grained registers have already been used with encouraging results.

Especially for historical corpora, researchers face a lack of general-purpose tooling. In order to produce cartographic visualizations, both the capacity to adapt to different contexts [START_REF] Alex | Adapting the Edinburgh geoparser for historical georeferencing[END_REF] and the necessity to complement existing resources with a precise historical gazetteer [START_REF] Borin | Geographic visualization of place names in Swedish literary texts[END_REF] have been highlighted. Such historical gazetteers exist, but their development is challenging [START_REF] Southall | On historical gazetteers[END_REF] even for texts as late as 20th century Europe [START_REF] Plini | One name one place? Dealing with toponyms in WWI[END_REF]. Existing toolboxes, such as AATOS [START_REF] Tamper | AATOS -a Configurable Tool for Automatic Annotation[END_REF], mostly feature candidate extraction and ranking as well as entity linking. The approach used here is more light-weight, modular and adaptable, with a similar scope as CORE [START_REF] Mäkelä | CORE -a Contextual Reader Based on Linked Data[END_REF] but with an overall greater focus on general-purpose usability, using texts as input, the integration of registers, and serialized map export as images.

Crossing philology and computer science

The task at hand does not simply reside in linking text to space, it is closely related to the interpretation of texts and maps and implies to try and cross philological and computational approaches. Even if both the methods of natural language processing and the results in the form of maps can convey an impression of scientific objectivity, the validity of mental and computerized operations described here should always be examined with respect to their potential relevance. Geospatial analysis and spatial representation may indeed be deficient or inadequate as the symbolic role and the expressive power of place names do not necessarily coincide with Western instrumental science and cartography, meaning in that particular case the registers and models used for extraction, the world geodetic system, and the chosen map projection. The static, abstract nature of this sometimes superficial reading of the texts should be made up for by putting information into context, for example using visual cues to add qualitative insights. Additionally, a particular scrutiny is required to approach the texts, most importantly concerning the proper adaptation of concepts between disciplinary boundaries and in practice concerning error analysis.

Challenges and corresponding tooling

Three common issues in geographic information extraction are specifically addressed here with a particular focus on philological soundness: detecting geographical references, disambiguating place names and developing effective user interfaces. This approach involves to gather and curate supplementary information applicable to historical texts. To this end, the aggregation of tools used here is being developed with historical texts in mind 1 and has already been used so far to map different collections ranging from the 17th to the 20th century [START_REF] Barbaresi | Visualisierung von Ortsnamen im Deutschen Textarchiv[END_REF][START_REF] Barbaresi | Towards a Toolbox to Map Historical Text Collections[END_REF][START_REF] Barbaresi | Toponyms as Entry Points into a Digital Edition: Mapping Die Fackel[END_REF].

The extraction of spatial entities (or geoparsing) can be considered as one of the most important part in spatial analysis [START_REF] Fize | Geodict: an integrated gazetteer[END_REF]. It has been shown that a method for automatically constructing a geographic gazetteer using heterogeneous sources can mitigate coverage issues by combining information but also improve resolution and correctness by validating the datasets against another. It is for example useful to mine Wikipedia for location information [START_REF] Popescu | Gazetiki: automatic creation of a geographical gazetteer[END_REF], which results in the constitution of minimum explicit tuples for geographic names: Entity-Name, Entity-Coordinates, and Entity-Type. In this experiment, both extraction and disambiguation are parametrized to suit the particular case at hand. Regarding toponym resolution, two different types of disambiguation methods [START_REF] Buscaldi | Approaches to disambiguating toponyms[END_REF] are used so far: map-based and knowledgebased. It has been shown that an acceptable precision can be reached by including metainformation [START_REF] Pouliquen | Geocoding multilingual texts: Recognition, disambiguation and visualisation[END_REF], which consists here in distance (based on a calculation relative to a contextual setting), type and importance of the entries (as known from information extracted from GeoNames or Wikipedia), as well as immediate context (e.g. the expected range and the last country seen). The process is controlled by parameters such as distance calculations, filter level or size of the search area.

The difficulties related to German include capitalization, genitive forms (typically formed with s), adjectival use (frequently as modifiers), and the fact that inhabited place names are also frequently found as personal names [START_REF] Volk | Learn-Filter-Apply-Forget. Mixed Approaches to Named Entity Recognition[END_REF]. These reasons make it difficult 1

The ongoing work on the toolbox is open-source: https://github.com/adbar/geokelone to build a gazetteer and prompt for the integration of morphological information. As a general principle, it is expected that "named entity recognition in German will profit from precompiled lists as well as from learning and filtering" (ibid.). Especially for historical texts it is necessary to refine or complement the resources at hand: during the 20th century there have been significant political changes in Central Europe that have severely affected toponyms, so that geographical databases lack coverage and detail. The operations performed prior to the extraction comprise the bootstrapping, filtering and merging of registers; since there is no commonly adopted standard for gazetteers they have to be combined. It also includes helpers to bootstrap geographical data, as knowledge-based methods using fine-grained data improve the results [START_REF] Vrandečić | Wikidata: a Free Collaborative Knowledge Base[END_REF]. So far, import filters for GeoNames and structured data from Wikipedia and Wikidata are implemented, with a particular emphasis on data cleaning. Finally, the results are projected on customized maps. It is profitable to allow for adaptability of projection and design and to leave it open to the user to refine the map. It is for example possible to convey information such as typology or frequency on the maps.

Exploratory study

The study grounds on digitized versions of the first four volumes of Theodor Fontane's Wanderungen durch die Mark Brandenburg 2 (DTA, 2018), a series of travel feuilletons about Brandenburg published between 1862 and 1882 with a focus on its history and its landscape.

The experimental character of Fontane's writing results in an apparent lack of unity in the Wanderungen, which include quotations, lists, and commentaries. The method used here can provide a visual summary of the text and its heterogeneous and possibly non-literary segments, all the more since the numerous place names structure the narration. 3 The experimental setting is derived from previous experiments and includes custom gazetteers for historical European locations. The streamlined process from text to map involves a series of decisions. Most importantly, the filtering level affects both the loading of gazetteers prior to geoparsing and the toponym recognition phase in itself. Its purpose is to allow for tighter or looser control, with either restricted options or opportunistic search. The first experiments, whose results are displayed on Figures 1 and2, are designed to try to maximize recall with a smaller amount of filtering.

2 "Ramblings through Brandenburg" or "Walks through the March of Brandenburg" 3 For a review of studies on space by Fontane, see [START_REF] White | Space in Theodor Fontane's Works: Theme and Poetic Function[END_REF]. As can be seen from the scattering of points on Figure 1, the volumes seem to focus primarily on Brandenburg but other place names are found, so that only a loose cluster emerges around Berlin. Even so, the current borders of Germany can be distinguished, which implies that the accuracy is low, since the convergence of regional entities which was to become the German Empire and the empire as a whole had boundaries different from the current state. This fact highlights to what extent the use of current gazetteers (here GeoNames restricted to Germany) can impact the results. Figure 2 is useful for error analysis, as the filtering is reduced and a sense of frequency is given by the feature size and by the labels for the most prominent tokens.

The names for former states and regions (e.g. Prussia) are linked to fixed locations in the custom gazetteer. Interestingly, the impression given by Figure 1 is corrected in the right sense by clearly focusing on Brandenburg, however, the current boundaries also become much more identifiable, notably because of the noise coming from common names matching actual locations on the map. The cluster around Berlin can also be explained by the influence of distance calculations.

In a second experiment, the texts are then tokenized with SoMaJo [START_REF] Proisl | SoMaJo: State-of-the-art tokenization for German web and social media texts[END_REF] and POS-tagged with SoMeWeTa [START_REF] Proisl | SoMeWeTa: A Part-of-Speech Tagger for German Social Media and Web Texts[END_REF] to isolate tokens which have been identified as named entities. A short stoplist is gathered manually to discard frequent proper names which could correspond to actual locations. Figure 3 depicts the results of the focus on precision with a high level of filtering and geoparsing applied on named entities only. There are less points to be seen then on Figure 1 and the sharper focus on the northwestern end of Germany appears more clearly, which is in line with the expectations concerning the contents of the works. Figure 4 features a zoomed-in view centered on Brandenburg along with a medium amount of filtering and frequency cues. The predominance of Berlin and a few locations around it (e.g. Potsdam, Freienwalde, Rheinsberg, Küstrin, the river Havel -marked here as a point) is clear and actually corresponds to depictions in the texts and to the contrast between metropole and province in the texts [START_REF] Scheiding | Raumordnungen bei Theodor Fontane[END_REF]. Overall, it can be deemed to be an adequate if somewhat biased overview of Fontane's ramblings.

Discussion

The status of place names that are to be found and projected on the map ought to be discussed since there are consubstantial ambiguities on linguistic level that complicate the search [START_REF] Smith | Bootstrapping toponym classifiers[END_REF]: the referent ambiguity (one name used for more than one location) and the referent class ambiguity (place names used as organization or person names) are commonly addressed by disambiguation processes, whereas reference ambiguity (more than one name for the same location) has to be dealt with during the compilation of geographical databases. In general, successful detection and disambiguation relies on a smart interplay of resources and tools at different levels. Last, the case of either imprecise, vague or vernacular names [START_REF] Jones | Modelling vague places with knowledge from the Web[END_REF]) is a prominently linguistic issue which can at least be addressed by manual curation and should in any case be attended to in order to meet the expectations of philological research. Additionally, from a literary standpoint, it seems necessary to take the changes of perspective into account as well as the literary devices which are used to guide the reader's gaze.

The artifical character of research results and especially maps in this context also ought to be mentioned. The "selection, omission, simplification, classification [...] and symbolization" steps of mapping as shown here are all "inherently rhetorical" [START_REF] Harley | Deconstructing the map[END_REF], they are both the foundation and the product of scientific reasoning and have to be taken with a grain of salt. One must bear in mind that "computers are lousy readers", and "our current digital tools walk a delicate line between analytical power and accessibility" [START_REF] Wilkens | Contemporary Fiction by the Numbers[END_REF]. As the products of machine learning and machine reading can diverge from the expectations, there is an estrangement in distant reading experiments which one has to face and to overcome in order to make proper use of the tools.

Conclusion

This article introduced theoretical and practical instruments combining philological knowledge, geographic information retrieval and visualization. A common ground for hypothesis testing and visualization has been presented, with the particular example of the detection and projection of spatial patterns in a historical text collection. Maps focusing on Brandenburg at the second half of the 19th century have been compared, they exemplify issues related to the unsupervised processing of historical resources and they show the advantages of an appropriate methodology and subsequent data cleaning and filtering. Rapidly spotting problems in methodology or datasets can hopefully help mitigating the side effects of largescale analysis and distant reading. Text visualizations are indeed the substrate of interpretable representations which do not follow data but rather confront them by putting them in perspective and trying to overcome the superficiality of computational reading, be it through a flattening constellation or by dealing with the rhizomatic character of literary texts (Barbaresi, 2018a). The difference between data wrangling and research in digital humanities resides precisely in the number and diversity of conceptual and technical filters which are repeatedly applied, consciously or sometimes unknowingly. The chosen approach and its inevitable imperfections have to be brought to light, documented and criticized.

As quantitative and qualitative analysis can go hand in hand, digital literary studies are not mere numeric accounts, they are first and foremost a exploratory process. The parametrization of tools, the linguistic annotation of both texts and gazetteers as well as an appropriate level of filtering and map customizing can lead to a representative overview of ramblings through Brandenburg's March, whereas the borderlands of text mapping require a careful examination and scrutiny to let patterns emerge, in a constant loop of questioning and feedback from datasets and methods.

Figure 1 :Figure 2 :

 12 Figure 1: Raw text, medium amount of filtering, no frequency Figure 2: Raw text, minimum filtering, frequency information through size and frequency-based labeling

Figure 3 :

 3 Figure 3: POS-tagged text, maximum filtering