Switzerland Population in Largest City data was reported at 1,356,037.000 Person in 2017. This records an increase from the previous number of 1,341,453.000 Person for 2016. Switzerland Population in Largest City data is updated yearly, averaging 951,846.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 1,356,037.000 Person in 2017 and a record low of 535,471.000 Person in 1960. Switzerland Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.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.; ;

Switzerland Population in Largest City: as % of Urban Population data was reported at 20.309 % in 2017. This records a decrease from the previous number of 20.328 % for 2016. Switzerland Population in Largest City: as % of Urban Population data is updated yearly, averaging 20.220 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 20.747 % in 2007 and a record low of 19.215 % in 1963. Switzerland 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 Switzerland – Table CH.World Bank: 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;

Comprehensive socio-economic dataset for Switzerland including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

Abstract copyright UK Data Service and data collection copyright owner.

The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material.
The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files.

Main Topics:

The files in this dataset relate to the datafiles in the Leicester database held in the directory /korner/*.*.
File Information
g124sid1.* Ordinary revenues of the Swiss towns, 1501-1611
g124sid2.* Growth of public debts and loans of nine Swiss towns, 1501-1611
g124sid3.* Growth of interests on loans and life rents at Bale, 1501-1611
g124sid4.* Revenue to the state of Luzern from wine, 1421-1798
g124sid5.* Revenue and expenditure of the city and state of Lucerne, 1430-1795

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

This dataset provides information about the number of properties, residents, and average property values for Swiss Drive cross streets in Reed City, MI.

Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network

This dataset contains ozone (O3) and carbon monoxide (CO) concentration measurements collected by the OpenSense (http://www.opensense.ethz.ch) mobile senor network over the course of 4.5 years (2012/02-2016/09). The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.

In particular, the dataset contains:

Ozone (O3) data: 2012/02 - 2016/09 (19.9 Mio samples)

Carbonmonoxide (CO) data: 2014/03 - 2016/09 (49.7Mio samples)

Hardware:

Ozone sensor: SGX (former e2V) MiCS-OZ-47 Ozone Sensing Head with Smart Transmitter PCB

Carbon monoxide sensor: Alphasense CO-B4

GPS receiver: u-blox EVK-6p

Data files format:

co_data_*:

Time of day: yyyy.mm.dd HH:MM

Latitude WGS84

Longitude WGS84

HDOP: horizontal dilution of precision, uncertainty of the GPS position

Tram ID

WE_CHANNEL_SENSOR_1_MV: The voltage [in mV] at the working electrode of the electrochemical sensor (see Alphasense CO-B4 datasheet for more details)

o3_data_*:

Time of day: yyyy.mm.dd HH:MM

Latitude WGS84

Longitude WGS84

HDOP: horizontal dilution of precision, uncertainty of the GPS position

Tram ID

Ozone [ppb]: On-device calibrated (according to manufacturer) ozone measurement [in parts-per-billion]

Temperature [in °C]

Relative Humidity [in %]

Data quality:

The data has NOT been post-processed! In order to achieve high data quality, the data needs to be cleaned (e.g. outlier filtering) and, most importantly, the sensors need to be individually calibrated. Reference data can be obtained from www.ostluft.ch, the official air quality monitoring network in eastern Switzerland, which operates multiple monitoring stations in the city of Zurich.

Plot Coverage Map (MATLAB):

The provided MATLAB script plot_data_coverage.m plots the locations of the collected samples onto the map of Zurich (map_zurich.png).

References:

The dataset (and related aspects) has partly been used and is described in more detail in the following publications:

Balz Maag et al. SCAN: Multi-Hop Calibration for Mobile Sensor Arrays. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol.1, No.2 (IMWUT), 2017.

Olga Saukh et al. Reducing Multi-Hop Calibration Errors in Mobile Sensor Networks. In IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN), 2015. Best Paper Award!

Olga Saukh et al. Route Selection for Mobile Sensor Nodes on Public Transport Networks. In Journal of Ambient Intelligence and Humanized Computing, 5(3), Springer, 2014.

Olga Saukh et al. On Rendezvous in Mobile Sensing Networks. In Proceedings of the 5th Workshop on Real-World Wireless Sensor Networks (RealWSN), 2013.

Jason Jingshi Li et al. Sensing the Air we Breathe – The OpenSense Zurich Dataset. In Proceedings of the 26th International Conference on Artificial Intelligence (AAAI), 2012.

Olga Saukh et al. Route Selection for Mobile Sensors with Checkpointing Constraints. In Proceedings of the 8th International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS, in conjunction with IEEE PerCom), March 2012.

David Hasenfratz et al. On-the-fly Calibration of Low-Cost Gas Sensors. In Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN), 2012.

For further information, visit: http://www.opensense.ethz.ch

When using this data set, it should be bibliographically referred to as 'Urban Audit, 2004'. The Urban Audit (UA) provides European urban statistics for a representative sample of large and medium-sized cities across 30 European countries. It enables an assessment of the state of individual EU cities and provides access to comparative information from other EU cities. This spatial dataset will support the study and dissemination of the UA data. It allows the visualisation of participating cities at three conceptual levels: - UA City - the core city, using an administrative definition - UA City Kernel - a concept introduced to improve comparability between large cities - Larger Urban Zone (LUZ) - approximating the functional urban region In addition, this spatial dataset allows visualisation of a 285 participating cities at two hierarchical sublevels to analyse the disparities within cities: - Sub City Districts level 1 (SCD L1) - Sub City Districts level 2 (SCD L2) The extent of this dataset is the EU 27 (2007) plus Croatia (HR), Norway (NO) and Switzerland (CH). The URAU_2004 dataset contains a polygonal feature class for UA Cities, UA City Kernels and Large Urban Zones, derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004). Polygonal feature classes for Sub City Districts are derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004) or spatial data supplied by URAU delegates which has been made coincident with UA City geometry. A generalised version of each feature class allows for visualisation at the scale of 1:3 Million. UA Cities are also represented by a point topology that are derived from and synchronised with the GISCO STTL_V3 dataset of European Settlements. The UA city points are, when possible, synchronised to an Urban Fabric class in Corine Land Cover 2000.

Context. This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 million cells.

Content. The data published in this repository correspond to a first release, i.e. a diachronic slice of one register every 8 to 9 years. Unfortunately, the remaining data are currently under embargo. Their publication will take place as soon as possible, and at the latest by the end of 2023. In the meantime, the data presented here correspond to a large subset of 2,844 pages, which already allows to investigate most research hypotheses.

Description. The population censuses, digitized by the Archives of the city of Lausanne, continuously cover the evolution of the population in Lausanne throughout the 19th century, starting in 1805, with only one long interruption from 1814 to 1831. Highly detailed, they are an invaluable source for studying migration, economic and social history, and traces of cultural exchanges not only with Bern, but also with France and Italy. Indeed, the system of tracing family origin, specific to Switzerland, allows to follow the migratory movements of families long before the censuses appeared. The bourgeoisie is also an essential economic tracer. In addition, censuses extensively describe the organization of the social fabric into family nuclei, around which gravitate various boarders, workers, servants or apprentices, often living in the same apartment with the family.

Production. The structure and richness of censuses have also provided an opportunity to develop automatic methods for processing structured documents. The processing of censuses includes several steps, from the identification of text segments to the restructuring of information as digital tabular data, through Handwritten Text Recognition and the automatic segmentation of the structure using neural networks. Please note that the detailed extraction methodology, as well as the complete evaluation of performance and reliability is published in:

Petitpierre R., Rappo L., Kramer M. (2023). An end-to-end pipeline for historical censuses processing. International Journal on Document Analysis and Recognition (IJDAR). doi: 10.1007/s10032-023-00428-9

Data structure. The data are structured in rows and columns, with each row corresponding to a household. Multiple entries in the same column for a single household are separated by vertical bars ⟨|⟩. The center point ⟨·⟩ indicates an empty entry. For some columns (e.g., street name, house number, owner name), an empty entry indicates that the last non-empty value should be carried over. The page number is in the last column.

Liability. The data presented here are not curated nor verified. They are the raw results of the extraction, the reliability of which was thoroughly assessed in the above-mentioned publication. We insist on the fact that for any reuse of this data for research purposes, the implementation of an appropriate methodology is necessary. This may typically include string distance heuristics, or statistical methodologies to deal with noise and uncertainty.

Based on a wide variety of categories, the top major global smart cities were ranked using an index score, where a top index score of ** was possible. Scores were based on various different categories including transport and mobility, sustainability, governance, innovation economy, digitalization, living standard, and expert perception. In more detail, the index also includes provision of smart parking and mobility, recycling rates, and blockchain ecosystem among other factors that can improve the standard of living. In 2019, Zurich, Switzerland was ranked first, achieving an overall index score of ****. Spending on smart city technology is projected to increase in the future.

Smart city applications Smart cities use data and digital technology to improve the quality of life, while changing the nature and economics of infrastructure. However, the definition of smart cities can vary widely and is based on the dynamic needs of a cities’ citizens. Mobility seems to be the most important smart city application for many cities, especially in European cities. For example, e-hailing services are available in most leading smart cities. The deployment of smart technologies that will incorporate mobility, utilities, health, security, and housing and community engagement will be important priorities in the future of smart cities.

This dataset contains power measurements and meteorological forecasts relative to a set of 24 power meters located in Rolle (Switzerland). These datasets are published to provide a standard benchmark for evaluating forecasting algorithms for demand side management applications.

In L.Nespoli, V. Medici, K. Lopatichki, F. Sossan, Hierarchical Demand Forecasting Benchmark forthe Distribution Grid, arXiv, 2019, this dataset is used to test several regressors in predicting the 24 hours ahead electrical load.

This dataset consists of measurements coming from 62 IEC 61000-4-30 Class A power quality meters manufactured by DEPsys (Switzerland) installed in secondary substations and LV cabinets of the distribution grid of the city of Rolle (Switzerland). The dataset has been enriched with numerical weather predictions from commercial provider Meteoblue (Switzerland), updated every 12 hours.

The power measurements are provided as a pickle dataset, which includes:

For each phase:

mean active and reactive power

voltage magnitude

maximum total harmonic distortion (THD)

voltage frequency (\omega)

the average power over the three phases.

The latter one has been used as target variable in the aforementioned paper.

The meteorological forecasts are provided as a Hierarchical Data Format 5 file, which includes:

temperature

global horizontal and normal irradiance (GHI and GNI, respectively)

relative humidity (RH)

pressure

wind speed and direction.

How to read the files with Python

The following code allows to open the files in python

import pandas as pd import pickle as pk

nwp_data = pd.read_hdf('nwp_data.h5','df') power_data = pk.load(open("power_data.p", "rb"))

The nwp_data is a pandas DataFrame of arrays. Each column, whose name is self explanatory, represent a set of 24 hours forecasted meteorological variable. These represents the most recent forecasts available from the NWP service at the respective time index of the dataset.

The power_data is a dict of pandas DataFrame. The each value of the dict, whose key is self explanatory, contains a DataFrame whose columns are the name of the meter they refers to. The DataFrame 'P_mean' additionally contains 6 fictitious aggregations of the phase-mean power of the meters, 'S1', 'S2', 'S11', 'S12', 'S21', 'S22', and 'all', which represents the sum of all the meters. The hierarchical structure of the aggregations is the following one:

 all
 _|_
|   |

S1 S2 | | | | | | S11 S12 S21 S22

S11 contains the first quarter of the time series presented in the dataset, while S12,S21,S22 contain the second, third and fourth quarter of the time series, respectively.

Additionally to this, the reference paper also considered the following vacation days:

01.01.2018 02.01.2018 30.03.2018 02.04.2018 10.05.2018 21.05.2018 01.08.2018 17.09.2018 25.12.2018 01.01.2019 02.01.2019

This project is carried out within the frame of the Swiss Centre for Competence in Energy Research on the Future Swiss Electrical Infrastructure (SCCER-FURIES) with the financial support of the Swiss Innovation Agency (Innosuisse - SCCER program) and of the Swiss Federal Office of Energy with the project SI/501523.

When using this data set, it should be bibliographically referred to as 'Urban Audit, 2004'.

The Urban Audit (UA) provides European urban statistics for a representative sample of large and medium-sized cities across 30 European countries. It enables an assessment of the state of individual EU cities and provides access to comparative information from other EU cities.

This spatial dataset will support the study and dissemination of the UA data. It allows the visualisation of participating cities at three conceptual levels: - UA City - the core city, using an administrative definition - UA City Kernel - a concept introduced to improve comparability between large cities - Larger Urban Zone (LUZ) - approximating the functional urban region

In addition, this spatial dataset allows visualisation of a 285 participating cities at two hierarchical sublevels to analyse the disparities within cities: - Sub City Districts level 1 (SCD L1) - Sub City Districts level 2 (SCD L2)

The extent of this dataset is the EU-27 plus Croatia (HR), Norway (NO) and Switzerland (CH).

The URAU_2004 dataset contains a polygonal feature class for UA Cities, UA City Kernels and Large Urban Zones, derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004). Polygonal feature classes for Sub City Districts are derived from the geometry of the GISCO COMM_2004 dataset (based on EuroBoundary Map 2004) or spatial data supplied by URAU delegates which has been made coincident with UA City geometry.

A generalised version of each feature class allows for visualisation at the scale of 1:3 Million. UA Cities are also represented by a point topology that are derived from and synchronised with the GISCO STTL_V3 dataset of European Settlements. The UA city points are, when possible, synchronised to an Urban Fabric class in Corine Land Cover 2000.

The Swiss Städtekonferenz Mobilität (transl: City Conference on Mobility) publishes a "Städtevergleich Mobilität" (transl: City comparison on mobility) for the six biggest German-speaking Swiss cities (Basel, Bern, Luzern, St.Gallen, Winterthur and Zürich), every couple of years. It is based on combining federal data and data the cities collect themselves. The reports are published here: https://skm-cvm.ch/de/Info/Fakten/Stadtevergleich_Mobilitat

Every year, modal shares between cities are reported. Currently reported years are 2010, 2015, 2021. Modal shares are calculated as the percentages of the main mode of transport per trip ("Hauptverkehrsmittel pro Weg", p. 18 of report for 2021).

This repository provides a dataset, which is a manual transcription of modal shares reported on page 18 of the report for 2021 to make modal share data available in CSV format. The original report is attached as well. The repository contains the following files:

• modal_shares_staedtevergleich.csv: The modal shares dataset (CSV file)
• variables.csv: Variable descriptions for modal_shares_staedtevergleich.csv (CSV file)
• staedtevergleich_2021_231030.pdf: PDF report (original data source) retrieved on 15.5.2025 from https://skm-cvm.ch/de/Info/Fakten/Stadtevergleich_Mobilitat

The permanent resident population is the reference population for population statistics. The permanent resident population includes:All Swiss nationals having their main place of residence in SwitzerlandForeign nationals who have held a residence or permanent residence permit for a minimum of 12 months.

The population evolves due to certain demographic movements (births, immigrations, deaths and emigrations). Since 2007, the average increase has been over 1%, making Switzerland one of the most dynamic countries in Europe in terms of population growth.The Population and Households Statistics are part of the surveys conducted within the framework of the Federal population census. The statistics provides information regarding population size and composition of the permanent resident population at the end of a year as well as population change during the same year.Features registered:Individuals: date of birth, gender, marital status, citizenship, place of residence, place of birth, place of previous residence, household composition.Foreign nationals: residence permit, duration of stay.For data protection reasons, absolute values ​​from 1 to 3 cannot be given in standard evaluations and are therefore indicated in this data set as a class with the value «3».

The service is in the Swiss coordinate system CH1903+ LV95. The LV95 Swiss Topographic map is best suited as a basemap for this service.

The data collected on members of the local elites of the three largest city-regions (Basel, Geneva and Zurich) are integrated in the more general OBELIS database on Swiss Elites. Currently, the OBELIS database includes elites from four sectors at the national level: Economic, Political, Administrative and Academic (+ national sociability associations) and covers nine dates: 1890, 1910, 1937, 1957, 1980, 2000, 2010, 2015 and 2020. The elite status of individuals is defined by the position/function held in these four spheres at the date mentioned. A description of all the different samples of the Swiss elites can be consulted on the website. The data allows researchers to understand the elites through a relational analysis (network analysis) to highlight the interrelations between these elites. The data is also suitable to conduct prosopographical analysis. As for national elites, the identification of local elites of the three largest Swiss city-regions also follows a positional approach by selecting all individuals occupying leading positions in the major local economic, political, cultural and academic institutions for the 7 benchmark years: 1890, 1910, 1937, 1957, 1980, 2000 and 2020. For the economic sphere we collected information on all the committee members of the regional chambers of commerce as well as directors of the most important companies of the three cities’ leading economic sectors. This includes the major banks and insurance companies for the financial sector; for Basel, all the major textile (until 1937) and chemical-pharmaceutical companies; for Geneva, the major watch-making companies, as well as a few other industrial companies; and for Zurich, all the major companies from the machine industry. The total number of companies varies from 49 in 1890 to 35 in 2020. The smaller sample for the recent period is due to the strong concentration process in all economic sectors, involving mergers and acquisitions as well as bankruptcies. For these companies, all CEOs/general directors and directors’ board members were taken into account. For the political sphere, we included all members of the cantonal (regional) and local (city) parliaments and governments for Geneva and Zurich, whereas in Basel, where the city’s territory fully coincides with the canton, only the members of the cantonal parliament and government were considered. For the academic sphere we include all full and extraordinary (associate) professors of the three cities’ universities until 1957, and, for the more recent dates, a selection of professors according to the occupation of institutional positions or according to their scientific reputation. Finally, the committee members of the three cities’ fine art societies are included as urban elites from the cultural sphere.

The permanent resident population is the reference population for population statistics. The permanent resident population includes:All Swiss nationals having their main place of residence in SwitzerlandForeign nationals who have held a residence or permanent residence permit for a minimum of 12 months.

The population evolves due to certain demographic movements (births, immigrations, deaths and emigrations). Since 2007, the average increase has been over 1%, making Switzerland one of the most dynamic countries in Europe in terms of population growth.The Population and Households Statistics are part of the surveys conducted within the framework of the Federal population census. The statistics provides information regarding population size and composition of the permanent resident population at the end of a year as well as population change during the same year.Features registered:Individuals: date of birth, gender, marital status, citizenship, place of residence, place of birth, place of previous residence, household composition.Foreign nationals: residence permit, duration of stay.For data protection reasons, absolute values ​​from 1 to 3 cannot be given in standard evaluations and are therefore indicated in this data set as a class with the value «3».

The service is in the Swiss coordinate system CH1903+ LV95.

The dataset contains the population of the resident population at the end of the corresponding month. The resident population includes:- the permanent resident population at the main residence: all persons who are registered with their main residence in the city of St.Gallen and have Swiss citizenship or a foreign citizenship with a residence or settlement permit - the non-permanent foreign resident population: foreign nationals with a short-stay permit, temporarily admitted persons, persons in need of protection and applicants for asylum as far as they are registered with the municipal population control - persons with a secondary residence (so-called "weekly residents"): registered residents in the city of St.Gallen with a main residence elsewhere in Switzerland or abroad. A secondary residence is usually established in connection with a job or a visit to a training institution in the city of St.Gallen. It is based on data from the Population Services of the City of St.Gallen (processed under the name "STADTSGPOP" by the Statistical Office).

This dataset includes thirteen group discussions, which were conducted within the PARTISPACE Project. French, Turkisch , Swedish, German, Bulgarian, English and Swiss Young People from artistic Projects, youth groups, environmental networks, sports associations and projects against racism were participants of the Group discussions. The transcripts are partly transcribed, the whole document is about 165 pages. The PARTISPACE project receives funding from the European Union's Horizon 2020 research and innovation Programme and provides empirical knowledge on youth participation across formal, non-formal and informal Settings.

The dataset contains the population of the resident population at the end of the corresponding month.

The resident population includes:- the permanent resident population at the main residence: all persons who are registered with their main residence in the city of St.Gallen and have Swiss citizenship or a foreign citizenship with a residence or settlement permit - the non-permanent foreign resident population: foreign nationals with a short-stay permit, temporarily admitted persons, persons in need of protection and applicants for asylum as far as they are registered with the municipal population control - persons with a secondary residence (so-called "weekly residents"): registered residents in the city of St.Gallen with a main residence elsewhere in Switzerland or abroad. A secondary residence is usually established in connection with a job or a visit to a training institution in the city of St.Gallen.

It is based on data from the Population Services of the City of St.Gallen (processed under the name "STADTSGPOP" by the Statistical Office).

ICDAR 2021 Competition on Historical Map Segmentation — Dataset

This is the dataset of the ICDAR 2021 Competition on Historical Map Segmentation (“MapSeg”).
This competition ran from November 2020 to April 2021.
Evaluation tools are freely available but distributed separately.

Official competition website: https://icdar21-mapseg.github.io/

The competition report can be cited as:

Joseph Chazalon, Edwin Carlinet, Yizi Chen, Julien Perret, Bertrand Duménieu, Clément Mallet, Thierry Géraud, Vincent Nguyen, Nam Nguyen, Josef Baloun, Ladislav Lenc, and Pavel Král, "ICDAR 2021 Competition on Historical Map Segmentation", in Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR'21), September 5-10, 2021, Lausanne, Switzerland.

BibTeX entry:

@InProceedings{chazalon.21.icdar.mapseg,
 author  = {Joseph Chazalon and Edwin Carlinet and Yizi Chen and Julien Perret and Bertrand Duménieu and Clément Mallet and Thierry Géraud and Vincent Nguyen and Nam Nguyen and Josef Baloun and Ladislav Lenc and and Pavel Král},
 title   = {ICDAR 2021 Competition on Historical Map Segmentation},
 booktitle = {Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR'21)},
 year   = {2021},
 address  = {Lausanne, Switzerland},
}

We thank the City of Paris for granting us with the permission to use and reproduce the atlases used in this work.

The images of this dataset are extracted from a series of 9 atlases of the City of Paris produced between 1894 and 1937 by the Map Service (“Service du plan”) of the City of Paris, France, for the purpose of urban management and planning. For each year, a set of approximately 20 sheets forms a tiled view of the city, drawn at 1/5000 scale using trigonometric triangulation.

Sample citation of original documents:

Atlas municipal des vingt arrondissements de Paris. 1894, 1895, 1898, 1905, 1909, 1912, 1925, 1929, and 1937. Bibliothèque de l’Hôtel de Ville. City of Paris. France.

Motivation

This competition aims as encouraging research in the digitization of historical maps. In order to be usable in historical studies, information contained in such images need to be extracted. The general pipeline involves multiples stages; we list some essential ones here:

• segment map content: locate the area of the image which contains map content;
• extract map object from different layers: detect objects like roads, buildings, building blocks, rivers, etc. to create geometric data;
• georeference the map: by detecting objects at known geographic coordinate, compute the transformation to turn geometric objects into geographic ones (which can be overlaid on current maps).

Task overview

• Task 1: Detection of building blocks
• Task 2: Segmentation of map content within map sheets
• Task 3: Localization of graticule lines intersections

Please refer to the enclosed README.md file or to the official website for the description of tasks and file formats.

Evaluation metrics and tools

Evaluation metrics are described in the competition report and tools are available at https://github.com/icdar21-mapseg/icdar21-mapseg-eval and should also be archived using Zenodo.