Earth Syst. Sci. Data, 13, 1693–1709, 2021 https://doi.org/10.5194/essd-13-1693-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Mid-19th-century building structure locations in Galicia and Austrian Silesia under the Habsburg Monarchy Dominik Kaim, Marcin Szwagrzyk, Monika Dobosz, MateuszTroll, and Krzysztof Ostafn Instituteof Geographyand Spatial Management,Facultyof Geographyand Geology, Jagiellonian University, Gronostajowa 7, 30-387 Krak, Poland Correspondence: Dominik Kaim (dominik.kaim@uj.edu.pl) Received:8December 2020 – Discussion started:17 December 2020 Revised:8March 2021 – Accepted:10 March 2021 – Published:27 April 2021 Abstract. We produced a reconstruction of mid-19th-century building structure locations in former Galicia and Austrian Silesia (parts of the HabsburgMonarchy), which are located in present-day Czechia, Poland, and Ukraine and cover more than80000km2. Our reconstruction was based ona homogeneous seriesof detailed Second Military Surveymaps (1 : 28800) that were the result of a cadastral mapping (1 : 2880) generalization. The dataset consists of two types ofbuilding structures based on the original map legend – residential and out-buildings (mainlyfarm-relatedbuildings). The dataset’s accuracywas assessed quantitatively and qualitatively by using independent data sources and may serve as an important input in studying long-term socioeconomic processes and human–environmental interactions or as a valuable reference for continental settlement recon­structions. The dataset is available at https://doi.org/10.17632/md8jp9ny9z.2 (Kaim et al., 2020a). 1 Introduction Although the human impact on Earth has been ongoing for millennia (Stephens et al., 2019), it has accelerated since the mid-19th century with the development of industry,transport infrastructure, and land use changes (Fischer-Kowalski et al., 2014). In many regions of Europe, this has been a time of minimal forest cover due to high use from both agriculture and industry (Gingrich et al., 2019; Jepsen et al., 2015). Al-though manyland use reconstructions have covered this pe­riod, they have usually focused on the dominant land uses (Fuchs et al., 2013; Lieskovsket al., 2018) or, if they are global, have offered a generalized view of settlements (Hurtt et al., 2011; Klein Goldewijk et al., 2010). Detailed, large-scale historical settlement data are either missing or highly uncertain (Lieskovsket al., 2018). Only recently havelarge­scale, long-term, and highly accurate settlement reconstruc­tions become available to scholars (Leyk and Uhl, 2018). As human impacts on the landscape may result in long-lasting legacies (Fuchs et al., 2016; Munteanu et al., 2017), high-quality, spatially explicit historical settlement data need to be produced and shared. In the past, housing structures im-pacted the appearance of invasive species (Gavier-Pizarro et al., 2010) and increased the demand for forest litter, which resulted in reduced soil carbon pools (Gimmi et al., 2013) or triggered the development and persistence of the wildland– urban interface over time (Kaim et al., 2018). These exam­plesshowthattheexistenceofeasily accessible, high-quality data on historical settlements may contribute to a better un­derstanding of future human impacts on the environment. In this paper, we introduce a dataset that includes more than 1.3 million1 building structure locations up to the mid-19th century detected in parts of what is now Poland, Ukraine, and Czechia, which were formerly parts of the Hab-sburgMonarchy(Austrian Empire). The dataset contains the exact locationsof residential andfarmbuildingsina terri­torythatcovers morethan80000km2.Our database captures the situation just before rapid industrialization (Frank, 2005), massive inter-continental migration (Praszalowicz, 2003), and profound land use changes, which were a result of so­cietal and political changes in the region (Munteanu et al., 1The number of buildings within Galicia and Austrian Silesia was1305233;however,the databasealso includes additional struc­tures locatedonthe samemap sheets, which yields1327466 struc­tures in total. 2014). Our database can be used as a stand-alone dataset for avariety of human-related analyses in the environmental and social sciences or as reference data for broad-scale (i.e. con­tinental) reconstructions. 2 Dataset 2.1 Study area The data were collected for parts of what is now Poland, Ukraine, andCzechia that belonged to the HabsburgMonar­chy (Austrian Empire) in the mid-19th century. These ar-eas were called Austrian Silesia and Galicia at the time (Fig. 1). Austrian Silesia (with more than 80% of its area in present-day Czechia and less than 20% in Poland) was the small southernmost part of the Silesia region, and it re-mained in the HabsburgMonarchyafter Silesia’s division in 1742. It consisted of two historical parts –Tesin Silesia and Opava Silesia – where Opava (Troppau) was the largest city (16 608 inh., inhabitants, 1869; Bevkerung, 1871). Galicia was an Austrian name introduced for part of the Crown of the Kingdom of Poland territory when it was annexed by Austria in 1772(~ 40%of its area is in present-day Poland and the rest is in Ukraine)2. Galicia was one of the largest and most populated crown lands in the Austrian Empire and Austria-Hungary, and agriculture dominated its econ-omy.Two prominent citiesin Galicia wereLviv(87109 inh., 1869) and Krak (49 835 inh., 1869; Bevkerung, 1871). The regions, as neighbouring areas, were closely connected based on social and economic reasons, which makes it ratio-nal to present them together, especially taking into account their legacies in later decades. 2.2 Materials and methods 2.2.1 Historical maps The reconstruction was based on a homogeneous set of Sec­ond Military Survey maps, which were acquired from the Austrian State Archives in Vienna in the form of scanned .tif fles (at 300 DPI). The maps for Austrian Silesia (42 map sheets) were published in the period of 1837–1841, and the maps for Galicia (412 map sheets) were published in the period of 1861–1864. One map sheet located in the north-eastern part of Galicia was not available in the archive and could not be used in the study. The scale of the map is 1: 28800, anditwas produced asa resultofa generalization and updateof cadastral maps(1 : 2880) for military purposes (Konias, 2000). Cadastral maps were prepared in the peri-ods of 1824–1830 and 1833–1836 (Silesia) and 1824–1830 and 1844–1854 (Galicia). The Second Military Survey was 2The territory annexed in 1772 was enlarged in 1775 (Bukov­ina), reduced in 1815 (Zamo´s´c District), enlarged in 1846 (Free City of Cracow), and reduced again in 1849 (Bukovina as a sep-arate Austrian crown land). the frst empire-wide topographic mapping initiative based ona propermap projection(Affek,2015; Skalošetal.,2011; Timár et al., 2010). Due to the high quality of the maps, their relatively low positional errors, and their large catalogue of land use categories, they are often used in land use recon­structions for different parts of the HabsburgEmpire (Feur­dean et al., 2017; Kaim et al., 2016; Munteanu et al., 2015; Pavelková et al., 2016). 2.2.2 Building images in the Second Military Survey Although the map scales of cadastral mapping and military mapping differ substantially, theimages of thebuildings on the Second Military Survey are detailed (Fig. 2). However, to assess the differences between the maps, we frst con-ducted a systematic comparison of the maps by comparing the building information presented by the Second Military Survey to that in the cadastral maps. The procedure aimed at assessing the impact of map generalization on the po-tential number of structures that we could acquire. We se-lected 10 case study areas located in the different parts of Galicia (eight cases) and Austrian Silesia (two cases) that represent different landscape conditions. The selection was determined by the availability of the cadastral maps, which was a true obstacle, especially for the Galician part of the study area. Finally, we used the resources available at http: //www.szukajwarchiwach.gov.pl (last access: 28 July 2020), an offcial website for documents stored in the Polish na-tional archives, and at https://www.geshergalicia.org(last ac-cess: 28 July 2020), a non-proft organization that supports Jewish genealogical and historical research on Galicia. The maps presented on the website were originally stored in the national archives in Poland and Ukraine. The cadastral maps from Austrian Silesia were consulted on the website of the State Administration of Land Surveying and Cadas­tre of Czechia at https://archivnimapy.cuzk.cz (last access: 28 July 2020). We selected easily identifable parts of vil­lages and towns, counted the building structures on the cadastral maps, and compared them to the Second Military Surveymaps(Table1).We foundthat althoughthe structure imagesintheSecondMilitarySurveymapsarevery detailed, historical cartographers had to employgeneralization proce-dures.Onaverage, the numberofbuildings presented on the Second MilitarySurveymapswasnearly85%ofthe number ofbuildings presentedonthe cadastralmaps(Table1);how­ever, the results in towns were lower, and the results in rural areas were higher due to the generalization procedures. De-spite the differences, we decided that thebuilding structures are presented withveryhigh quality, whichmakesit possible to obtain reasonably accurate structures (Fig. 2). 2.2.3 Geometric correction and georeferencing Different referenced data were employed to georeference the maps. In the case of the Polish part of Austrian Silesia and Table 1. Comparison of thenumber ofbuilding structures presented on the cadastralmaps (1 : 2880) andSecond Military Surveymaps (1 : 28800) in 10 selected test areas. Village/ Cadastral Second Second Military Date of Second Military Cadastral Second town maps Military Survey structures publication of Survey date maplink Military Survey asapercentage cadastralmap ofpublication Survey of cadastral (last access: 20 April 2021)map(%) Austrian Silesia Krnov25 18 72.0 1836 1840/1841 https://archivnimapy.cuzk.cz/uazk/https://mapire.eu/en/map/europe-(Jägerndorf) com/com_data/1367-1/1367-1-006_19century-secondsurvey/ index.html Melc113 90 79.6 1836 1839/1840 https://archivnimapy.cuzk.cz/uazk/https://mapire.eu/en/map/europe-(Meltsch) com/com_data/1750-1/1750-1-004_19century-secondsurvey/ index.html Galicia – western part Baran47 41 87.2 1850 1861/1862 https://maps.geshergalicia.org/https://mapire.eu/en/map/europe­ Sandomier-cadastral/baranow-1850/ 19century­ ski secondsurvey/?bbox=2385401 Biecz 39 34 87.2 1850 1861/1862 https://maps.geshergalicia.org/https://mapire.eu/en/map/europe­ cadastral/biecz-1850/ 19century­ secondsurvey/?bbox=2356521 Gorlice 58 45 77.6 1850 1861/1862 https://maps.geshergalicia.org/https://mapire.eu/en/map/europe­ cadastral/gorlice-1850/ 19century-secondsurvey/?layers=158 Kolbuszowa 32 32 100.0 1850 1861/1862 https://maps.geshergalicia.org/https://mapire.eu/en/map/europe­ cadastral/kolbuszowa-1850/ 19century-secondsurvey/?bbox Galicia – eastern part Jagielnica143 116 81.1 1861 1862/1863 https://maps.geshergalicia.https://mapire.eu/en/map/europe-( ) org/cadastral/19century­ jagielnica-yahilnytsya-1861/ secondsurvey/?bbox=2860400 Jarycz24 20 83.3 1850 1862/1863 https://maps.geshergalicia.org/https://mapire.eu/en/map/europe­ Nowycadastral/jaryczow-nowy-1850/ 19century­ ( ) secondsurvey/?bbox=2695554 Kulik88 80 90.9 1854 1862/1863 https://maps.geshergalicia.org/https://mapire.eu/en/map/europe-( ) cadastral/kulikow-kulykiv-1854/ 19century­ secondsurvey/?bbox=2674116 D. Kaim et al.: Mid-19th-century building structure locations 1695 https://doi.org/10.5194/essd-13-1693-2021 Earth Syst. Sci. Data, 13, 1693–1709, 2021 ÿ Zur43 38 88.4 1848 1862/1863 https://maps.geshergalicia.org/https://mapire.eu/en/map/europe-( ) cadastral/zurow-zhuriv-1848-1/ 19century-secondsurvey/?layers=158 Average 84.7 Figure 1. Study area; lower left corner presents a small portion of the maps used in the study (source: Austrian State Archives). Galicia, Polish topographic maps from the 1970s at a scale of1 : 25000 were used. Maps elaborated in the Polish 1965 coordinate system based on the Pulkovo-42 reference frame were obtained as raster images transformed to the PL-1992 coordinate system based on the ETRF-89 reference frame (explanations for the terms used in this paragraph can be found in Appendix A). In the case of the Ukrainian part of Galicia, high-resolutionWorld Imagery and DigitalGlobe imagery and Soviet military topographic maps at scales of 1: 25000,1 : 50000 and1 : 100 000 were used. The Soviet maps elaborated in the 1942 coordinate system based on the Pulkovo-42 reference frame were transformed to the proper zone of the UTM coordinate system. In the case of the Czech part of Austrian Silesia, the local-level administrative bound­aries were used as georeferenced information. Original map sheets from the Second Military Surveywere cropped along the map frame. Each cropped image was pro-cessed separatelybyusingatleast20 controlpointspersheet. The points were chosen from triangulation points, historical buildings (e.g. churches), recognizable crossroads, bridges, viaducts, and local administrative boundaries. If such points were lacking, then river/stream connections were also used. Geometric correction and georeferencing to the PL-1992 or UTM coordinate system were obtained with 2nd-order poly­nomial transformation.For the map sheets withalow cover-age along the borderland,a 1st-order polynomial transforma­tion was applied. The total root mean square error (RMSE) for most sheets reachedvalues between10 and30m and oc­casionally exceeded 30 m, which indicates the level of geo­metric accuracyof the fnal dataset. 2.2.4 Building structure acquisition Themapsshowtwomain categoriesofbuildingsindifferent colours. Red (German Wohngebäude)indicates mainly resi­dentialbuildings(but also includes some churches, monaster-ies, town halls, and railway stations; in total, approximately 1%ofthe“red”buildings were non-residential). Black(Ger­man Wirtschaftsgebäude)includesfarm or agriculturalbuild-ings(but also includes similarexceptions as those mentioned above) (Zaffauk, 1889). Although we are aware that among residential buildings, there are some non-residential struc­tures and that amongfarm-relatedbuildings, there are some craft-related structures or warehouses, we wanted to be con-sistent with the map content, and we decided to acquire all the structures accordingto thesetwo main categories.Aval­idation of the more specifc information on building usage would require using different methods, consulting local in­dependent sources, which is beyond the scope of our work, and taking into account the area under study compared to, for example, counting the number of houses (for details, see D. Kaim et al.: Mid-19th-century building structure locations 1697 Figure 2. Comparisonof cadastral1 : 2880 maps (upper row) and SecondMilitary Survey1 : 28800 maps (lower row) for Szczawnica (a), Kani (b), and Niewiar (c) in Galicia (source: National Archive in Krak, National Archive in Katowice, Austrian State Archives). Sect. 3.2 “Completeness – reference to census data at the dis-trictlevel”).We present,however, some potentialexceptions to show the reader what might also be found in the database (Fig.3).Thedivisionofthetwomainbuilding categorieswas not relatedtothe materialsusedto constructthebuilding(e.g. wood, bricks, and stones), as was presented on the cadas­tral maps, apart from the black structures surrounded by the red border, which mean outbuildingsbuilt of stone or brick. We used a semi-automatic, colour-based method to acquire residentialbuildings (Fig.4) anda manualvectorization for farmbuildings.Inthe frst step, the training data were manu-ally digitized for 12 randomly selected map sheets. The train­ing polygons includedtwoclasses, namely,buildingsandall other objects. Based on the training data, signatures for red, green, and blueraster bands were produced and used for map sheet classifcation. Classifed raster images were then con-vertedintovectorsoutof whichthe initialbuilding structures were identifed. These initial structures were then classifed asbuildings based on the typical size and shape of the map symbols that representbuilding structures on the map. The initially classifed structures were fltered to remove all ob-jectswithan areaoflessthan65m2andwithalength-to-area ratio higher than 0.6. The threshold values used in the proce-dure were based on the values found on the map and partly depended on the map scale (i.e. the minimal structure size could not be lower than 0.5 mm, which is ~ 15min the map scale). Only the shapes that met the criteria of a defned size and shape were further processed. The procedure described above was then performed for the other map sheets by using a loop; however, the initial training data had to be defned separately several times due to differences in the map sheets’ quality in different regions. After this semi-automatic pro-cedure, each map sheet was also verifed manually to elim­inate commissions (e.g. the points along roads marked with red) and omissions (e.g. missing structures in high-density housing areas); on the one hand, this was a time-consuming process, but on the other hand, it assured the high quality of the fnal product. As black was a widespread colour on the map, we acquired all thefarmbuildings through manual vectorization, as visually inspecting the errors was a time­ way station. Please note that in(c),the textual information (Schloss) is related to the building marked in red (source: Austrian State Archives). consuming process. All thebuildings were fnally combined into one layer and attributed a function – residential (orig­inally red) or farm (originally black). Additionally, we as-signed each building a date based on when the map sheet was published. The fnal layer was transferred to the Lam­bert azimuthal equal-area (LAEA) coordinate system. The acquisition work was performedwith ArcMap classifcation and spatial analysis tools. 3 Technical validation The data presented in the paper were subject to several accu­racyassessments.We assessed the acquisition accuracyand referred to the data with census data at different administra­tivelevels. Additionally, weverifed the numberofbuildings with the textual information presented on the original map sheets in the form ofbuilding number summaries and used auxiliary data such as cadastral maps as needed. 3.1 Relation between the mapped structures and the vectorized structures The relation between the structures presented on the Sec­ond Military Surveymaps and the structures captured in our databasewas assessedbycomparingthe numbersofbothval­uesin randomly selected, non-overlapping circles(300m ra- Figure 4. Semi-automatic procedure for acquiring residentialbuild­ing structures (originally marked in red) from historical maps. tio; area – 28.27 ha) located across the study area. First, we selected 1000 circles,verifed them visually, and counted the structures found on the map. Then, we removed from the next steps of the comparison the circles in the places where there were nobuildings either on the map or in the database. The fnal number of test circles was thus reduced to 311, which contained 4791 structures from the database. This number resulted in a 1.86% margin of error for the entire study area witha99% confdencelevel (population size –1305233). After comparing the number of structures, we calculated the root mean square error (RMSE) and the correlations (Pear­son’s r)between the structures’ sums on the map and in the database for all 311 test areas. The RMSE was based on the following formula (Eq. 1): s X n (Pi - Oi )2 RMSE = , (1) i=1 n where Pi signifes predicted values (vectorized building structures), Oi observed values (building structures on the maps), and n sample size (number of test areas). The procedure was employed for the three conditions of residential buildings, farm-related buildings, and all build­ings. Additionally, the results of the accuracy assessment were represented by a confusion matrix that compared the user’s accuracy, producer’s accuracy, overall accuracy, the kappa coeffcient of agreement, and the F score, which D. Kaim et al.: Mid-19th-century building structure locations 1699 Table 2. Accuracyassessment measures. User’s Producer’s Overall F score accuracy accuracy accuracy Residential 98.44 % 97.65 % 97.46 % 0.98 buildings Farm-related 99.05 % 91.18 % 96.66 % 0.95 buildings is a harmonic mean of the producer’s and user’s accuracy (Fawcett, 2006; Leyk and Uhl, 2018). The results show that the number of buildings present on the maps were very similar to the numbers that we ac-quired. The RMSE values were equal to 1.30 for the condi­tion with allbuildings, 0.87 for the condition with only resi­dentialbuildings, and 1.19 for the condition with onlyfarm­relatedbuildings(the meanvaluesofthe structures foundin the test circles on the maps were 15.4, 10.3, and 5.1, respec­tively). The correlations between the structures presented on the maps and thebuildings that we acquired were alsovery high, specifcally, r = 0.999 for the total number of build­ings, r = 0.998 for residentialbuildings, and r = 0.994 for farm-related structures (Fig. 5). The overall accuracy for all buildings that we acquired was 95.03%;however,itwas higherfor residentialbuildings (97.46%) than forfarm-related structures (96.66%). Simi­larly, the slightly higher quality of the residential building class was supported by the F score (Table 2). The kappa co-effcient for the classifcation procedurewas 0.89. 3.2 Completeness – reference to census data at the district level To verify the total number of houses acquired in our proce-dure with the independent source, we compared the number ofvectorized structures with the information from the census data at the district level for the entire study area(n = 99). The censuses closest in time to the publication of the maps were organized in 1857 for Austrian Silesia(n = 23) and in 1869 for Galicia(n = 76). Although there is a time difference be-tween the maps and the census data(~ 18 years in Austrian Silesia and ~ 7years in Galicia), there was no better option to compare the number ofbuildings for these regions due to the timing of the censuses. Additionally, we could verify in the sources only the number of residentialbuildings (which account for 69%of our structures) as the census did not con-tain information on farm-related structures. The respective district map with additional attribute information including the year of the census, year of map creation (the dominat-ing value for the district unit), time difference between the map and census dates, and number of houses according to the census was attached to the dataset to help defne the poten­tial uncertainties responsible for the differences. Apart from the statistical information based on the censuses, the above­mentioned layer also consists of district-level information on main road accessibility based onthe Second Military Survey road network (Kaim et al., 2020b) and information on to-pographybased on the SRTM digital elevation model (DEM; Farretal.,2007).Afulllistofthe attributescanbefoundin the “Data availability” section, and some of the variables are also presented in the form of maps in Appendix B. The results show that the number of houses recorded in the census data and captured in our database differed, but the difference was not great. The censuses indicated 914 107 structures, whereaswe acquired897020buildingsofa sim-ilar type for the entire study area. However, regionally, the differences were diverse.A comparison at the district level indicated that on average, we acquired 99.4%of the houses recordedby the census data,but the differences among the districts were substantial (Fig. 6). We found that the num­ber of vectorized residential buildings for the districts lo­cated in Austrian Silesia was usually higher than the num­ber recorded in the census. At the same time, in Galicia, the differences were wider ranging as both overestimations and underestimations were found. In one district (Staremiasto; Staryj Sambir, Ukrainian )the number of houses thatwevectorizedwaslessthan70%ofthe houses recorded by the census. Interestingly, however, when we compare all the structures that we acquired from the maps (residential andfarm-relatedbuildings together), their sum accounts for 98.9%of the houses recorded in the census for this district. This may suggest that thebuildingdivision presented on the mapmighthavebeen understoodinadifferentwaybydiffer­ent cartographers.However, thishypothesis canonlybe con-frmed through additional research and deeper study,which is only partly conducted within this paper (see Sect. 3.4 below). Unfortunately, the original map instructions for the Second Military Survey are not available in the archives and cannot be consulted.Usingasetof uncertainty-relatedvariables,we produced a set of correlations between the percentage differ­ence in the number of houses in the database and the cen-sus for the population density, road accessibility, time differ­ence between the map and census publication, mean eleva­tion, and mean slope. The only statistically signifcant corre­lation(p< 0.05)was the correlation with the time difference betweenthemapand census publication,butitwas relatively low – r = 0.217. 3.3 Completeness – reference to map frame information Each map sheet (approx. 15 × 15km) of the Second Mili-tary Surveyhas additional textual information in the frame, where the basic statistics that are important from a military point of view are presented. The statistics, which are usually presented at the village level, include the number of houses, number of stables, and number of people and horses that could be stationed there. We used this information to ver­ify the number of houses that we captured in the database Figure 5. Pearson’s correlations r between the numberofbuildings shown on the maps and the numberof structures acquiredin the dataset for allbuildings (a), residentialbuildings (b), andfarm-relatedbuildings (c), n = 311. The lower panel is the location of the test areas. by choosing 10 evenly distributed map sheets (2 from Aus­trian Silesia,4from the western part of Galicia, and4from the eastern part of Galicia; Fig. 7a) that represent different landscape conditions (e.g. lowlands, foothills, and mountain­ousareas)andto comparethe numberofvectorizedbuilding structures within them at the village level (Fig. 7b). Since the number of stables was not fully comparable with the num-beroffarmbuildingsin our database, we compared only the number of houses. In some cases, the villages were split into neighbouring map sheets, and corrections, including adding or removing somebuildings located within the specifed vil­lages, had to be implemented (Fig. 7c). In two cases, how­ever, we found that the number of houses for the village was not listed as the two neighbouring map sheets each informed that the information was available on the other sheet. Alto-gether, information from 283 towns and villages on the 10 selected map sheets was summarized. The comparison showed that in most cases (7 out of 10), the number of houses that we captured in the database was higher than the number presented in the map frame. The dif­ferences ranged from 0.4% to 54.7%, with anaverage dif­ference of 14% (Table 3). A more detailed explanation of the potential reasons for thisis partly presented in Sect. 3.4, where the local level analyses are presented. 3.4 Completeness – reference to census data and map sheet information at the local level The comparisons with census data at the district level and map frame information at the map sheet level showed that although on average our database captured information on houses relatively well, local differences were substantial.To better understand the nature and potential explanations of these local differences, we present a few situations below D. Kaim et al.: Mid-19th-century building structure locations Figure 6. Number of vectorized residentialstructures compared to the census data at the district level for the entire study area(n = 99), Austrian Silesia(n = 23), and Galicia(n = 76). Table 3. Comparison of the number of houses with the statistics presented in the map frames (number of houses according to the map frame = 100 %). Map sheet Vectorized Houses Difference houses according (%) to the map frame Austrian Silesia Section 3Column 11 E 3021 2276 32.7 Section 3Column 6E 3624 3637 0.4 Galicia – western part Section 10 Column 11 W 2757 3818 27.8 Section 5Column 13 W 2113 2828 25.3 Section 9Column 23 W 5072 3279 54.7 Section 8Column 18 W 2981 2632 13.3 Galicia – eastern part Section 8Column 3E 3207 2542 26.2 Section 16 Column 2W 66 46 43.5 Section 16 Column 8E 2939 2848 3.2 Section 8Column 6W 3538 2953 19.8 Average 14 where we address the underestimation or overestimation be-tween our structures and the reference data (Table 4). 3.4.1 Underestimation The analyses at the district level showed that in extreme cases, the number of houses that we covered in the database was more than 30% lower than this number in the census data.We chosetwo villages –Jaworkiand Milcza –asex­amples to analyse in detail. In both cases, the differences https://doi.org/10.5194/essd-13-1693-2021 1701 were substantial;inJaworki, we captured slightly more than 70.5%of the number of houses in the census, and in Milcza, we captured 43%of the number of houses in the census data. The example of Jaworki shows that the map frame infor­mation gave very similar values to those presented in the census. At the same time, the map frame provided informa­tion about the relatively low number of people who could be housed there.The numberisverylow when comparedto this number in other villages that we analysed, although some of the villages were located in similar mountainous condi­tions. The ethnological research performed in the village in the frst half of the 20th century confrmed a very low stan­dard of living there, even compared to the standard of liv­ing in neighbouring areas (Reinfuss, 1947). Jaworki had an unusual system of seasonalfarmbuildings located higher in the mountains that were inhabited by shepherds in the sum-mer season. Our data show that in the village, the number offarm-related structures waseven higher than the number of houses, which is also unusual.Wehypothesize that some of the inhabitedbuildings were classifed asfarm-related on themapbut were residentialin reality.This couldexplainthe difference between our data and the census data. In Milcza, both the census data and the map frame infor­mation confrmed a much higher number of houses than we captured in the database. However, in this case, the percent­ageofbuildingsin the databasewasevenlower than theval­ues observedinextreme situationsatthe districtlevel(Fig.6) as we captured only 43%of the number ofbuildings in the census. Since the map frame information confrmed the val­ues from the census, which were substantially different from the values in our database, we consulted the original cadas­tral maps to compare them with the Second Military Sur-veymaps. The comparison showed that although the cadas­tral maps (1851) indicated99buildings, the Second Military Survey maps (1861/1862) showeda totalof only49build­ings, including37 residentialbuildings. This confrmsan un­precedented level of map generalization here when compared to that in other areas (Table 1; Fig. 8). It also explains the level of underestimation that we noticed in the database when compared to other, independent sources. 3.4.2 Overestimation The analysis conducted for the village of Milka, located in the western part of the Carpathians (western Galicia), showed that our database captured a slightly higher number of struc­tures than that indicated in the 1869 census, and it cap-tured a substantially higher number than that indicated in the map frame summary (Table 3). In the map frame, however, Milka also contained the hamlet of Sucha Ga, which formally belonged to the main village, although the statistics for the hamlets were kept separately in some cases, poten­tially for strategic reasons. The census data were published for the commune level, and only some of the hamlets were indicated separately. Adding the numbers from the main vil- Earth Syst. Sci. Data, 13, 1693–1709, 2021 Figure 7. Verifcationofthebuilding structure numbersacquiredfromthemapswithmap frame information. Locationof10evenlydis­tributed map sheets (a);building locations and map frame information(b);and information notingthat the statistics for the selected villages are available on the neighbouring map sheet (c) (source: Austrian State Archives). lage and the hamlet together makes the difference between our database and the census versus map frame statistics sub-stantially smaller (Table 3). Potentially, the hamlets could have been moved from one village to another over time, which in some cases, makes comparisons over longer time periods diffcult (Ostafn et al., 2020). In the same region, relatively close to Milka(< 15 km), we also found that compared to the data in the census and map frame information, our dataset substantially overesti­mated the number of houses in the village of Trzebinia (our data had more than 170% of the number listed in the 1869 census). Although we consulted cadastral maps (1844: 169 buildings, including houses) and the 1880 census data (89 houses) and verifed the potential administrative bound-ary changes, we could not fnd anyobjective reason for such a large difference.We must bear in mind that the mid-19th century was a time of dramatic political movements, natu­ral disasters, diseases, andfamine in Galicia, which resulted in the most dramatic population decrease in over 100 years (Zamorski, 1989). It is diffcult to determine whether these events were responsible for the reduction in the number of houses over such a short period of time. It is also beyond the scopeofthe data descriptortoexplainthe socioeconomic background in detail at the local level. Although the differ­ences that we observed were on average much lower than in this extreme case, we provide this example to show potential database users that such situations are possible. D. Kaim et al.: Mid-19th-century building structure locations 1703 Table 4. Number of houses captured in our database related to the census data and map frame information – examples of underestimation and overestimation. * Summaryoftwo cadastral villages,JaworkiITheilandJaworkiII Theil; ** houses for Milka, and Sucha Ga also covered. Vectorization (1861/1862) Census (1869) Map frame (1861/1862) Houses Farm-related buildings Houses Houses Stables Number of people that can be housed Number of horses that can be housed Examples of underestimation Jaworki 98 Milcza 37 135 12 139 86 135* 103 – 2 30 80 – 20 Examples of overestimation Milka 281 Trzebinia 118 184 12 279 68 230 + 30** = 260 56 6 – 150 + 10 = 160 – 14 – Figure 8. Comparison of a cadastral map (1851) and Second Military Survey map (1861/1862) that indicates a very high level of map generalization (source: National Archive in Przemy´sl, Austrian State Archives). 4 Data availability The dataset is available at https://doi.org/10.17632/md8jp9ny9z.2 (Kaim et al., 2020a). The data are stored in an open, widely used shapefle (.shp) format, which may be opened in GIS software (including open-source software, such as QGIS). The shapefle format consists of three mandatory fles (.shp, .shx, .dbf) and a set of non-mandatory fles. In the case of our fle, the complete set of fles include the following. buildings_GASID.shp– the feature geometry buildings_GASID.shx–apositionalindexofthe feature geometry buildings_GASID.dbf – attribute format in dBase fle format buildings_GASID.prj– projection description with the text representation of coordinate reference systems buildings_GASID.sbn,buildings_GASID.sbx – spatial indexes of the features buildings_GASID.xml – geospatial metadata in XML format buildings_GASID.cpg– used to specify the code page (only for .dbf) to identify the character encoding to be used The attributes available within the dataset are the follow­ing. type – the type ofbuilding(1 – residential,2 –farm­related) Year1,Year2– map sheet production period comment – if map sheet production dates were not spec-ifed, then we analysed the dates of neighbouring sheets and added it here as the most probable period An additional layer that addresses the uncertainty-related attributes is also included in the dataset as a separate layer – uncertainty_metadata.shp. It contains a map of all the dis-tricts of the study area and covers a set of attributes that are helpful to any uncertainty analysis. The statistical cen-sus data for Austrian Silesia are based on the 1857 census and for Galicia, on the 1869 census. The list of attributes in-cludes the following. District – name of the district according to the census Man – number of men Women– number of women M_and_W – number of men and women combined census_h – number of houses according to the census database_h – number of houses according to the database per_of_cen – houses in the database as a percentage of houses recorded in the census cens_date – census date map_date–mapdate(ifthemapwas producedfor more than 1 year, then the date closest to the census was taken) time_diff – number of years between the census and map publication area_ha – the area of the district based on shapefle poly-gon geometry (hectares) pop_dens – population density based on “M_and_W” and “area_ ha” attributes (people/km2) mean_dist – mean distance to the main roads – four cat­egories of roads based on Kaim et al. (2020b) mean_elev – mean elevation (metres above sea level based on SRTM DEM) mean_slope – mean slope (degrees based on SRTM) slope_rg – slope range in the district (degrees based on SRTM) farm_b – numberoffarm-related structures basedonthe database The fles are compressed in .7z format and can be un­packedbyusing, forexample, 7-Zip(https://www.7-zip.org/, Kaim et al., 2020a). 5 Conclusions The data descriptor presents the complete coverage of the mid-19th-centurybuilding structure locations in the histori-cal regions of Galicia and Austrian Silesia in Central Europe. The dataset covers more than 1.3 million objects, including housesandfarm-relatedbuildings. Thisisthe frst suchlarge and detailed database in the region. The dataset is based on the Second Military Survey maps (1 : 28800), which were the resultofa cadastral mapping(1 : 2880) generalization for military purposesandthusoffereda much higherlevelofde­tail than earlier (e.g. the First Military Survey,1 : 28800) or later (e.g. black and white, BW,editions of the Third Military Survey,1 : 25000) mapping sources in the area. This is also the only source of information on the number and location of farm-related structures at the time as theywere not included in other, independent datasets. The technical validation of the database showed a high level of object completeness when compared to different in­dependent sources. Nevertheless, there were some discrep­ancies in the number of houses that we acquired and the number according to the census data, map frame information, and cadastral mapping.However,we attemptedtoexplainthe types and reasons for the potential differences. Considering the size of the study area and the number of structures that we acquired, local differences cannot be explained here as they go beyond the scope of the data descriptor. We hope, however, that making this dataset available and adding a set of uncertainty-related variables will enable further analysis and improve the knowledge of the differences among the datasets. Our dataset may serve as a valuable source of in-formation not only for scientists who study the drivers and legacies of land use changesbut also for scholars who study changes in the standard of living, which potentially infu­ences decisions on migrations. Environmental scientists may also be able to use the data, especially when combining them with other land use and environmental variables. Since the time that we capture in the database shows the moment just before the important social and economic processes of in-dustrialization and urbanization, we believe that it may also contribute to a broad range of studies on the Anthropocene. D. Kaim et al.: Mid-19th-century building structure locations 1705 Appendix A: Explanation of the coordinate system terms, abbreviations, and respective EPSG geodetic parameter dataset codes 1942 coordinate system – the Soviet zonal projected coor­dinate system based on the Gauss–Krer projection and Pulkovo-42 reference frame used for military purposes in Warsaw Treaty countries until the beginning of the 1990s; Soviet military maps used in this project were elaborated in two 6-degree zones – zone4 (EPSG: 28404) and zone5 (EPSG: 28405). 1965 coordinate system – Polish offcial zonal projected co-ordinate system for large-scale topographic, civil maps used from 1968 until 2009; maps used in this project were elab­orated in two zones – zone I, based on double stereographic projection and Pulkovo-42 reference frame (EPSG: 3120), and zoneV, based on Gauss–Krer projection and Pulkovo­42 reference frame (EPSG: 2175). PL-1992 coordinate system – Polish offcial projected coor­dinate system used since the beginning of the 1990s for maps at scalesof1 : 10000 and lower (EPSG: 2180). ETRF-89 reference frame – EuropeanTerrestrial Reference System 1989, geodetic reference frame fxed to the stable part of the Eurasian continental plate at epoch 1989.0 (EPSG: 1178). LAEA coordinate system – projected coordinate sys­tem based on Lambert azimuthal equal-area projection (EPSG:3035). Pulkovo-42 reference frame – Soviet geodetic reference frame, based on Krasovsky1940 ellipsoid, used inWarsaw Treaty countries and in Poland until 2009 (EPSG: 4284). UTM coordinate system – zonal projected coordinate system based on the specifc case of the transverse Mercator pro-jection called UniversalTransverse Mercator; two 6-degree UTM zones were used for the Ukrainian part of Galicia – zone 34N (EPSG: 32634) and zone 35N (EPSG: 32635). Figure B1. Uncertainty-relatedvariables presentedatthe districtlevel: residentialbuildingsinthe databaseas percentageof homes recorded in the census (a), time difference between census and map publication (b), and population density (c). D. Kaim et al.: Mid-19th-century building structure locations 1707 Author contributions. DK, MS, MT, andKO conceived of and designedthe research.DK,MS,MD,MT,andKOacquiredthe data. DK performed the accuracy assessment. DK wrote the paper, and DK,MS,MD,MT,andKO revisedthe manuscript. Competing interests. The authors declare that theyhave no con-fict of interest. Acknowledgements. The authors would like to thank three anonymous referees and the editor, David Carlson, for their valu­able comments and suggestions. Financial support. 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