This statistic shows the biggest cities in Austria in 2025. In 2025, approximately **** million people lived in the administrative area of Vienna, making it the biggest city in Austria.

Actual value and historical data chart for Austria Population In The Largest City Percent Of Urban Population

Austria AT: Population in Largest City: as % of Urban Population data was reported at 36.487 % in 2024. This records an increase from the previous number of 36.336 % for 2023. Austria AT: Population in Largest City: as % of Urban Population data is updated yearly, averaging 33.104 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 36.889 % in 2014 and a record low of 30.956 % in 1981. Austria AT: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Austria – Table AT.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

Population in largest city in Austria was reported at 1990487 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Austria - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Austria AT: Population in Largest City data was reported at 1,990,487.000 Person in 2024. This records an increase from the previous number of 1,975,271.000 Person for 2023. Austria AT: Population in Largest City data is updated yearly, averaging 1,618,539.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 1,990,487.000 Person in 2024 and a record low of 1,531,462.000 Person in 1981. Austria AT: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Austria – Table AT.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;

In 2020, there were ***** thousand square meters of retail real estate in the City Center of Vienna, while Graz City Center had ***** thousand meters of retail space. Central city districts often concentrate a large share of the retail real estate in the city. It is important to note though, that retail real estate is developed not only in the central parts. For example, Meidlinger Hauptstraße, Favoritenstraße, Landstraßer Hauptstraße and Mariahiler Straße in Vienna are also areas strong retail real estate prensence.

This dataset provides demographic and geographic information for the nine federal states of Austria. The data was collected in April 2023 from public sources.

The dataset contains 9 rows, one for each state in Austria, and 15 columns with the following information:

State: The name of the state in Austria. Population: The population of the state. Population_percent: The percentage of the total Austrian population that lives in the state. Foreign_citizens: The number of foreign citizens living in the state. Foreign_citizens_percent: The percentage of the total population that are foreign citizens. State_area_km2: The area of the state in square kilometres. City_count: The number of cities in the state. Capital_city: The name of the capital city of the state. Capital_population: The population of the capital city. GDP_per_capita_euro: The gross domestic product (GDP) per capita in euros. City_area_km2: Capital city area in square kilometres. Largest_lake: The name of the largest lake in the state. Lake_area_km2: The area of the largest lake in the state in square kilometres. Highest_mountain: The name of the highest mountain peak in the state. Mount_peak_meters: The height of the highest mountain peak in meters.

Full edition for scientific use. This dataset comprises data from a survey of nonprofit organizations (NPOs) in the metropolitan region of Vienna, Austria. The data was collected between October 2019 and December 2020 and provides a comprehensive overview of the current state of the nonprofit sector in Vienna. It comprises a representative sample of 358 NPOs and an additional targeted sample of 235 large NPOs. The survey includes more than 60 questions covering a wide range of topics, including organizational goals and activities, beneficiary and staff demographics, different forms of organizing and related practices, performance metrics, budgeting, funding sources, and collaborative efforts. The dataset is a valuable resource for scholars interested in studying the inner workings, relationships, and societal contributions of nonprofit organizations, and it appeals to a variety of scholarly debates.

The Austrian real estate market, exhibiting a Compound Annual Growth Rate (CAGR) of 4.00% from 2019 to 2024, presents a robust investment opportunity. While precise market size figures for 2025 are unavailable, extrapolation from the historical data and considering typical market fluctuations suggests a market value of approximately €50 billion in 2025. This estimate accounts for potential variations in construction activity, economic growth, and regulatory influences. Key drivers include a growing population, increasing urbanization, and strong investor interest fueled by low interest rates and a relatively stable political environment. However, rising construction costs and material shortages, particularly following global supply chain disruptions, present significant restraints. The market is segmented by property type, primarily into single-family homes and multi-family dwellings. Single-family homes are expected to maintain a significant market share due to sustained demand from individual buyers, while the multi-family sector, driven by apartment rentals and increased housing density in urban areas, shows promising growth potential. Prominent companies like Swietelsky AG, ELK Fertighaus GmbH, and others contribute to the construction and development segments. Regional analysis reveals that major cities like Vienna and Salzburg will likely showcase higher transaction volumes and stronger value appreciation compared to rural areas. The forecast for 2025-2033 projects continued growth, though the pace might slightly moderate due to anticipated interest rate adjustments and potential macroeconomic changes. Despite the challenges, the Austrian real estate market remains attractive for both domestic and international investors. Long-term growth projections remain positive, with continuous development of sustainable and energy-efficient housing expected to shape the market in coming years. The segment focusing on environmentally friendly building materials and technologies is likely to attract significant investment and gain market share. Continued economic stability and government policies supporting the real estate sector are crucial for maintaining this growth trajectory. Recent developments include: January 2023: The residential project is being completed for the Neunkirchen non-profit housing and settlement cooperative by the SWIETELSKY branch office for building construction in Lower Austria and Burgenland as part of the general contractor. On a roughly 4,000-square-meter plot, 38 low-rise residential apartments with subsidies are being developed, along with 75 underground parking spaces., January 2023: The non-profit cooperatives GEDESAG and SCHNERE ZUKUNFT are constructing a total of 40 residential units in the Waldviertel neighborhood thanks to the SWIETELSKY subsidiary. For the non-profit Donau-Ennstalersiedlungs AG, 16 apartments and six semi-detached homes are being constructed in the heart of Gföhl. The 105 square meters of living space in the semi-detached homes at Seilergasse 5 will be split between the ground level, the upper floor, and a basement that is roughly 60 square meters in size. A two-story residential building is situated close by. Living spaces in the 16 units range from 55 to 84 square meters.. Notable trends are: The decrease in Labor Force in Austria is driving the demand of prefabricated houses.

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

Full edition for scientific use. The aim of the FIMAS project series is to track and analyse the integration processes of refugees and beneficiaries of subsidiary protection in Austria. The project focuses on labour market integration and the factors that facilitate or hinder it. The third wave of the survey comprises interviews with over 2400 refugees and was carried out in spring 2019 as part of the FIMAS+INTEGRATION 2 project. The sample consists of persons from the main countries of origin Syria, Afghanistan, Iraq and Iran.

Tourist arrivals in accommodation in Austria have been steadily rising. In 2019 they peaked at **** million arrivals, including both domestic and foreign visitors. The number of nights spent by tourists also gradually increased

Tourism in Austria

Over half of all visitors arriving at accommodation in Austria are international tourists, with numbers rising each year. Austria is popular with both summer and winter tourists. It benefits from an Alpine landscape and culture-packed cities, making it a key destination for both skiing and city breaks. Austria borders several countries and subsequently most inbound visitors arrive from neighboring countries, including Germany and Italy.

Accommodation market in Austria

The accommodation market in Austria mixes both urban and rural lodgings. As with most European destinations, hotels still dominate as main type of accommodation present across Austria, although other holiday accommodation such as chalets and pensions are also popular options in the mountain towns. The capital city Vienna is also an important market for the European hotel industry.

Austrian Cafe Market Outlook

According to our latest research, the global Austrian Cafe market size reached USD 7.8 billion in 2024, with a robust year-on-year growth driven by the increasing popularity of European café culture worldwide. The market is expected to expand at a CAGR of 6.2% from 2025 to 2033, ultimately reaching a projected value of USD 13.4 billion by the end of the forecast period. This growth is primarily fueled by rising consumer demand for authentic culinary experiences, evolving lifestyles, and the proliferation of specialty coffee and pastry offerings. As per our latest research, the market continues to benefit from both domestic innovation and international expansion, positioning Austrian cafes as a significant force within the global foodservice industry.

One of the most significant growth factors for the Austrian Cafe market is the global fascination with Austrian culinary heritage and café culture. The traditional Viennese coffeehouse, with its storied history and unique ambiance, has become a symbol of leisure, artistry, and social interaction. This allure is not limited to Austria alone; international cities have witnessed a surge in demand for authentic Austrian cafes, driven by tourists and expatriates seeking nostalgic experiences. The fusion of classic pastries such as Sachertorte and Apfelstrudel with modern culinary trends has further broadened the appeal, attracting younger demographics and foodies alike. Moreover, the rise of social media has amplified the visual and experiential aspects of these cafes, making them attractive destinations for global travelers and local patrons, which in turn continues to fuel market expansion.

The evolution of consumer preferences is another critical driver shaping the Austrian Cafe market. Modern consumers are increasingly seeking premium, artisanal, and ethically sourced products, leading to a transformation in cafe offerings. Austrian cafes have adeptly responded by enhancing their menus with specialty coffees, organic ingredients, and innovative beverage options, while still preserving the essence of their traditional fare. The emphasis on ambiance, customer service, and unique in-cafe experiences has also contributed to higher customer retention and repeat visits. Furthermore, the integration of technology, such as digital ordering and contactless payment systems, has streamlined operations and improved convenience, making Austrian cafes more accessible to a broader audience. This blend of tradition and innovation positions the market for sustained growth in the coming years.

The global expansion of Austrian cafes, supported by strategic partnerships and franchising models, is a pivotal factor in the market’s growth trajectory. Leading Austrian cafe brands are increasingly establishing their presence in high-growth regions such as North America, Asia Pacific, and the Middle East, catering to a diverse clientele. The adaptability of the Austrian cafe concept—ranging from luxurious flagship locations to convenient takeaway kiosks—enables operators to tap into various market segments and urban environments. Additionally, collaborations with local suppliers and culinary experts have allowed Austrian cafes to tailor their offerings to regional tastes without compromising authenticity. This internationalization strategy not only diversifies revenue streams but also enhances brand recognition, reinforcing the global appeal of Austrian cafe culture.

From a regional perspective, Europe remains the epicenter of the Austrian Cafe market, accounting for the largest share due to its deep-rooted café traditions and high consumer spending on out-of-home dining. However, North America and Asia Pacific are emerging as dynamic markets, driven by urbanization, rising disposable incomes, and a growing appetite for international cuisines. In particular, cities such as New York, Toronto, Tokyo, and Singapore have witnessed a proliferation of Austrian cafes, both as standalone businesses and as part of larger hospitality groups. The Middle East & Africa and Latin America, while currently representing smaller shares, are poised for gradual growth as global travel and cultural exchange continue to increase. This regional diversification is expected to play a crucial role in sustaining the market’s upward momentum through 2033.

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

最大城市人口在12-01-2024达1,990,487.000人，相较于12-01-2023的1,975,271.000人有所增长。最大城市人口数据按年更新，12-01-1960至12-01-2024期间平均值为1,618,539.000人，共65份观测结果。该数据的历史最高值出现于12-01-2024，达1,990,487.000人，而历史最低值则出现于12-01-1981，为1,531,462.000人。CEIC提供的最大城市人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的奥地利 – Table AT.World Bank.WDI: Population and Urbanization Statistics。

Full edition for scientific use. The dataset consists of data from a public participation GIS survey and provides insights into heat perception in the dense and socially heterogenous 15th district in Vienna (Rudolfsheim-Fünfhaus). The data was collected within the research project “BLUEMAP: Map-based citizen knowledge on the use and development of Viennese blue spaces” at the Austrian Academy of Sciences (ÖAW) Institute for Urban and Regional Research. The project was funded by the Anniversary Fund of the City of Vienna for the ÖAW. The project duration was 01.03.2024 - 28.02.2025. Further information about the project can be found here: https://www.oeaw.ac.at/en/isr/sustainable-urban-region/bluemap-map-based-citizen-knowledge-on-the-use-and-development-of-viennese-blue-spaces. The method of data collection was a PPGIS (public participatory geographical information system) survey using the Maptionnaire software (Mapita). The online survey was developed in collaboration with local stakeholders (Gebietsbetreuung Stadterneuerung südwest, Bezirksvorstehung des 15. Wiener Gemeindebezirkes, Wiener Klimateam) with the aim of collecting place-based data about heat and cooling in the 15th district of the city of Vienna (Austria). The survey was open during summer 2024. It was available in five languages: German, English, Serbian/Bosnian/Croatian, Turkish and Hungarian. The sampling procedure was convenience sampling. The survey was distributed through the channels of the cooperation partners (mainly of Gebietsbetreuung Stadterneuerung südwest) and other local organizations, that are active in the 15th district. Additionally, some posters and flyers were distributed in public institutions, local suppliers and recreational facilities. The data consist of socio-demographic data about the respondents, data about perceived heat stress during summers, and mapped locations of 1st public places that are perceived as especially hot during summers, 2nd public places with perceived cooling benefits in summer, 3rd public places where respondents would wish for interventions concerning cooling or heat mitigation. No personal data was collected. The raw data was manually cleaned. Five mapped points for the third mapping question were removed as duplicates, some cases were assigned missing values because of small case counts. 219 respondents engaged with the survey questions, 162 finished the survey. The data includes 2060 mapped locations. The CRS of the spatial data is EPSG:4326 - WGS 84.

Airborne laser scanning of the city of Vienna, which was organized by the survey department of the City Administration of Vienna (MA41). The data acquisition was performed in eight flight missions in November 2015.

After strip adjustment relative accuracy of the point cloud was in the order of 2cm. The absolute accuracy measured as RMSE is better than 5cm in planimetry and better than 4cm in elevation. The dataset was cleaned and does not involve obvious gross errors, e.g. points high up in the air or points far below ground level. The point density is measured as by last echoes per unit area and is more than 15 points/m² for 97% of the area. The average point density is 33 points/m².

The LiDAR dataset of the city is organized in a number of 1270m×1020m tiles (including a 10m overlap of all neighboring tiles).

Reference labels were generated semi-automatically: a rough filtering of main objects was firstly conducted by the software “Terrasolid”, and the final classification was refined by manual labelling. For the purpose of Vienna city administration, five classes are considered, namely ground, buildings, vegetation, others, water and bridges. All common street objects are categorized as others, such as (e.g.) streetlights, benches, shrubs, cars, construction sites and garbage bins. The different classes are defined by the following numeric integer codes: 2: Ground, 5: Vegetation, 6: Buildings, 8: Others, 9: Water and 17: Bridges.

The quality of the labelling was manually checked. In 20 sites of 100m×100m the classification was manually verified, and the average accuracy of reference labels is 95%.

Total 9 tiles are published, in which 4 tiles were used for the training and 5 tiles for the evaluation in the paper “A Comparison of Deep Learning Methods for Airborne LiDAR Point Clouds Classification”. Their locations in the city of Vienna can be found in the file of metadata, which also provides WGS84/GRS80 latitude and longitude coordinates of the extent corners of each tile (EPSG: 31256). This can be used to access images of the tiles from Google Maps or other public map service.

This ranking displays the results of the worldwide Made-In-Country Index 2017, a survey conducted to show how positively products "made in..." are perceived in various countries all over the world. During this survey, 74 percent of respondents from Austria perceived products made in Germany as "slightly positive" or "very positive". The survey indicates that German products have the strongest reputation in Austria, followed by Switzerland.

The number of domestic and international tourist overnight stays in Austria increased in 2024 over the previous year, exceeding pre-pandemic levels. In 2024, travel accommodation establishments in Austria reported around 114 million inbound overnight stays and over 40 million domestic overnight stays.

This statistic displays the results of the worldwide Made-In-Country Index 2017, a survey conducted to show how positively products "made in..." are perceived in various countries all over the world. For this statistic, respondents were asked about attributes they associate with products made in Austria. 27 percent of respondents stated that they associate "high quality" with products from Austria.