In 2025, Moscow was the largest city in Europe with an estimated urban agglomeration of 12.74 million people. The French capital, Paris, was the second largest city in 2025 at 11.35 million, followed by the capitals of the United Kingdom and Spain, with London at 9.84 million and Madrid at 6.81 million people. Istanbul, which would otherwise be the largest city in Europe in 2025, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population is almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in these seventy years. Before the turn of the millennium, Europe was the second-most populated continent, before it was overtaken by Africa, which saw its population increase from 228 million in 1950 to 817 million by 2000. Asia has consistently had the largest population of the world’s continents and was estimated to have a population of 4.6 billion. Europe’s largest countries Including its territory in Asia, Russia is by far the largest country in the world, with a territory of around 17 million square kilometers, almost double that of the next largest country, Canada. Within Europe, Russia also has the continent's largest population at 145 million, followed by Germany at 83 million and the United Kingdom at almost 68 million. By contrast, Europe is also home to various micro-states such as San Marino, which has a population of just 30 thousand.

As of 2024, London ranked first as the smartest city in Europe. Additionally, Amsterdam ranked fourth with a motion index score of 77.44. It is important to note that Amsterdam was the first in Europe to launch a smart city plan in 1994 called "The Digital City".

As of 2024, Paris was rated the best prepared city in Europe for a smart city future, with an index score of 76.4. London and Amsterdam followed, with 73.1 and 72.8, respectively. While Paris and London ranked best in their connectivity and infrastructure preparedness, Amsterdam scored best in the city's technology job market.

By 1800, London had grown to be the largest city in Western Europe with just under one million inhabitants. Paris was now the second largest city, with over half a million people, and Naples was the third largest city with 450 thousand people. The only other cities with over two hundred thousand inhabitants at this time were Vienna, Amsterdam and Dublin. Another noticeable development is the inclusion of many more northern cities from a wider variety of countries. The dominance of cities from France and Mediterranean countries was no longer the case, and the dispersal of European populations in 1800 was much closer to how it is today, more than two centuries later.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

As of 2024, Paris was rated the European city with the best smart city infrastructure, having an index score of 91.8. London and Madrid followed, with 79.9 and 71.7, respectively. While Paris and London are both in the top three of the best prepared European cities for a smart city future LINK to stat 1491047 after published, Madrid ranked fifth, with a general index score of 68.5.

The largest Western European city in 1200 was Palermo, with 150 thousand inhabitants. This is a great decrease in the number 150 years previously, where the population was 350 thousand. The city of Cordova also decreased by almost 400 thousand in this time, possibly because of the declining Arabian control and influence in the area. Seville is the third largest city on this list, although it's overall population decreased by ten thousand since 1050. The largest cities are generally in Spain or Italy, although the second largest city on this list is Paris, with 110 thousand inhabitants. In the lists that follow, Paris remains at the top as either the largest (1500 and 1650) or second largest (1330 and 1800) city in Western Europe.

In 1500, the largest city was Paris, with an estimated 225 thousand inhabitants, almost double the population of the second-largest city, Naples. As in 1330, Venice and Milan remain the third and fourth largest cities in Western Europe, however Genoa's population almost halved from 1330 until 1500, as it was struck heavily by the bubonic plague in the mid-1300s. In lists prior to this, the largest cities were generally in Spain and Italy, however, as time progressed, the largest populations could be found more often in Italy and France. The year 1500 is around the beginning of what we now consider modern history, a time that saw the birth of many European empires and inter-continental globalization.

ESA, in collaboration with European Space Imaging, has collected this WorldView-2 dataset covering the most populated areas in Europe at 40 cm resolution. The products have been acquired between July 2010 and July 2015.

The datasets are split by census block, cities, counties, districts, provinces, and states. The typical dataset includes the below fields.

Column numbers, Data attribute, Description 1, device_id, hashed anonymized unique id per moving device 2, origin_geoid, geohash id of the origin grid cell 3, destination_geoid, geohash id of the destination grid cell 4, origin_lat, origin latitude with 4-to-5 decimal precision 5, origin_long, origin longitude with 4-to-5 decimal precision 6, destination_lat, destination latitude with 5-to-6 decimal precision 7, destination_lon, destination longitude with 5-to-6 decimal precision 8, start_timestamp, start timestamp / local time 9, end_timestamp, end timestamp / local time 10, origin_shape_zone, customer provided origin shape id, zone or census block id 11, destination_shape_zone, customer provided destination shape id, zone or census block id 12, trip_distance, inferred distance traveled in meters, as the crow flies 13, trip_duration, inferred duration of the trip in seconds 14, trip_speed, inferred speed of the trip in meters per second 15, hour_of_day, hour of day of trip start (0-23) 16, time_period, time period of trip start (morning, afternoon, evening, night) 17, day_of_week, day of week of trip start(mon, tue, wed, thu, fri, sat, sun) 18, year, year of trip start 19, iso_week, iso week of the trip 20, iso_week_start_date, start date of the iso week 21, iso_week_end_date, end date of the iso week 22, travel_mode, mode of travel (walking, driving, bicycling, etc) 23, trip_event, trip or segment events (start, route, end, start-end) 24, trip_id, trip identifier (unique for each batch of results) 25, origin_city_block_id, census block id for the trip origin point 26, destination_city_block_id, census block id for the trip destination point 27, origin_city_block_name, census block name for the trip origin point 28, destination_city_block_name, census block name for the trip destination point 29, trip_scaled_ratio, ratio used to scale up each trip, for example, a trip_scaled_ratio value of 10 means that 1 original trip was scaled up to 10 trips 30, route_geojson, geojson line representing trip route trajectory or geometry

The datasets can be processed and enhanced to also include places, POI visitation patterns, hour-of-day patterns, weekday patterns, weekend patterns, dwell time inferences, and macro movement trends.

The dataset is delivered as gzipped CSV archive files that are uploaded to your AWS s3 bucket upon request.

A large number of European cities are covered by this dataset; for each city you can find one or more Cartosat-1 ortho image products and one or more Euro-Maps 3D DSM tiles clipped to the extent of the ortho coverage. The Euro-Maps 3D DSM is a homogeneous, 5 m spaced Digital Surface Model semi-automatically derived from 2.5 m Cartosat-1 in-flight stereo data with a vertical accuracy of 10 m. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. The final product includes several pixel-based quality and traceability layers: The dsm layer (_dsm.tif) contains the elevation heights as a geocoded raster file The source layer (_src.tif) contains information about the data source for each height value/pixel The number layer (_num.tif) contains for each height value/pixel the number of IRS-P5 Cartosat-1 stereo pairs used for the generation of the DEM The quality layer (_qc.tif) is set to 1 for each height/pixel value derived from IRS-P5 Cartosat-1 data and which meets or exceeds the product specifications The accuracy vertical layer (*_acv.tif) contains the absolute vertical accuracy for each quality controlled height value/pixel. The ortho image is a Panchromatic image at 2.5 m resolution. The following table defines the offered product types. EO-SIP product type Description PAN_PAM_3O IRS-P5 Cartosat-1 ortho image DSM_DEM_3D IRS-P5 Cartosat-1 DSM

🌤️ Weather Prediction Dataset

European Weather Data for ML & Education

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Unleash your ML skills with this weather dataset covering 18 European cities from 2000-2010. Perfect for teaching and training, it blends real-world complexity (overfitting, unbalanced data) with intuitive accessibility—ideal for classification, regression, and forecasting! 🌡️✨

📜 What’s This About?

Built for machine learning and deep learning, this dataset tracks daily weather features across Europe. Paired with optional "picnic weather" labels, it’s a hands-on tool for exploring weather patterns and their predictive power. Sourced from the European Climate Assessment & Dataset (ECA&D). 🌍

📊 What’s Inside?

• Main File: weather_prediction_dataset.csv (2.8 MB)

• Features:

  • _temp_mean, _temp_max, _temp_min (°C) 🌡️
  • _cloud_cover (oktas) ☁️
  • _global_radiation (100 W/m²) ☀️
  • _humidity (%) 💧
  • _pressure (1000 hPa) 🌬️
  • _precipitation (10 mm) 🌧️
  • _sunshine (0.1 hours) 🌞
  • _wind_speed, _wind_gust (m/s) 🍃

• Extras:

  • weather_prediction_picnic_labels.csv (394.3 kB) – Picnic suitability (True/False)
  • weather_prediction_dataset_light.csv (1.5 MB) – Slimmed-down version
  • weather_prediction_dataset_map.jpg (337.8 kB) – Location map
  • metadata.txt (4.7 kB) – Full details

💡 What Can You Do With It?

• Train models for weather forecasting. 🤖
• Tackle classification (e.g., picnic weather) or regression. 📈
• Teach ML concepts like overfitting with real data. 🎓

📜 Citation

Using this? Cite it:

Huber, F., van Kuppevelt, D., Steinbach, P., Sauze, C., Liu, Y., & Weel, B. (2022). Weather prediction dataset (Version v5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7525955

🧑‍🤝‍🧑 Creators

• Florian Huber
• Dafne van Kuppevelt
• Peter Steinbach
• Colin Sauze
• Yang Liu
• Berend Weel

📜 License

Creative Commons Attribution 4.0 International (CC BY 4.0).

✅ Use and share—just credit the creators!

🌟 Let’s Make an Impact!

This dataset is your playground for weather prediction and ML education. Dive in, experiment, and upvote if it sparks your next project—let’s learn and innovate together! 🙌

Available as a single coverage collection of data over 50 European Cities acquired by KOMPSAT-1’s Electro-Optical Camera (EOC) geolocated and orthorectified. The dataset is composed by PAN imagery at 6.6 m GSD, in GeoTIFF orthorectified format.

This dataset is about book series. It has 1 row and is filtered where the books is The rise of cities in north-west Europe. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

In 2023, London was the leading European city tourism destination based on the number of bed nights. That year, bed nights in the United Kingdom's capital exceeded 78 million, denoting a sharp annual increase but not fully recovering yet from the impact of COVID-19. Meanwhile, Paris and Istanbul followed in the ranking in 2023, with roughly 52 million and nearly 30 million bed nights. What are the most visited countries in Europe? While the French capital came in second among leading European cities based on bed nights, France topped the ranking of the European countries with the highest number of inbound tourist arrivals in 2023, ahead of Spain, Italy, and Turkey. Meanwhile, when looking at European countries with the highest tourism receipts that year, Spain recorded the highest figure, with over 90 billion U.S. dollars, followed by the United Kingdom. How many international tourists visit Europe every year? In 2023, the number of international tourist arrivals in Europe grew significantly over the previous year, totaling over 700 million. This figure, however, remained below pre-pandemic levels. Overall, either before and after the impact of COVID-19, Europe was the region with the highest number of international tourist arrivals worldwide.

It is estimated that the cities of Cordova (modern-day Córdoba) and Palermo were the largest cities in Europe in 1050, and had between fifteen and twenty times the population of most other entries in this graph, Despite this the cities of Cordova (the capital city of the Umayyad caliphate, who controlled much of the Iberian peninsula from the seventh to eleventh centuries), and Palermo (another Arab-controlled capital in Southern Europe) were still the only cities in Western Europe with a population over one hundred thousand people, closely followed by Seville. It is also noteworthy to point out that the five largest cities on this list were importing trading cities, in modern day Spain or Italy, although the largest cities become more northern and western European in later lists (1200, 1330, 1500, 1650 and 1800). In 1050, todays largest Western European cities, London and Paris, had just twenty-five and twenty thousand inhabitants respectively.

The period of European history (and much of world history) between 500 and 1500 is today known as the 'Dark Ages'. Although the term 'Dark Ages' was originally applied to the lack of literature and arts, it has since been applied to the lack or scarcity of recorded information from this time. Because of these limitations, much information about this time is still being debated today.

The data refers to the paper "Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities"

The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them.

The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.

The datasets are a csv file (Metrics.csv) and a shapefile (UFES.shp) of polygons with attributes.

UFES.shp attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA.

Metrics.csv contains the FUA codes, the land cover and landscape metrics used in the HCA.

This dataset is about book subjects. It has 8 rows and is filtered where the books is Sustainable cities in Europe. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807.

Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019

Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.

Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.

Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.

Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.

Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.

Language of the data: English.

References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602

The study examines the accessibility of 13 Alpine regions in Austria by public transport. The focus is on travel by train. In addition to the duration of the journey, the frequency of transfers and the railway service, public transport in the tourist destinations was also analysed. Travel costs were collected and compared on a sample basis according to different modes of transport. Currently, less than 10% of tourists arrive by train. The study identifies the causes of this and makes recommendations on how to increase this proportion. The prerequisite for this is that the tourism industry and traffic management cooperate intensively at regional, national and international level. The study is part of a cross-border study of travel behaviour in the Alpine region within the framework of the Alpine Convention.