Russia is the largest country in the world by far, with a total area of just over 17 million square kilometers. After Antarctica, the next three countries are Canada, the U.S., and China; all between 9.5 and 10 million square kilometers. The figures given include internal water surface area (such as lakes or rivers) - if the figures were for land surface only then China would be the second largest country in the world, the U.S. third, and Canada (the country with more lakes than the rest of the world combined) fourth. Russia Russia has a population of around 145 million people, putting it in the top ten most populous countries in the world, and making it the most populous in Europe. However, it's vast size gives it a very low population density, ranked among the bottom 20 countries. Most of Russia's population is concentrated in the west, with around 75 percent of the population living in the European part, while around 75 percent of Russia's territory is in Asia; the Ural Mountains are considered the continental border. Elsewhere in the world Beyond Russia, the world's largest countries all have distinctive topographies and climates setting them apart. The United States, for example, has climates ranging from tundra in Alaska to tropical forests in Florida, with various mountain ranges, deserts, plains, and forests in between. Populations in these countries are often concentrated in urban areas, and are not evenly distributed across the country. For example, around 85 percent of Canada's population lives within 100 miles of the U.S. border; around 95 percent of China lives east of the Heihe–Tengchong Line that splits the country; and the majority of populations in large countries such as Australia or Brazil live near the coast.

The average for 2021 based on 196 countries was 656013 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

Based on land area, Brazil is the largest country in Latin America by far, with a total area of over 8.5 million square kilometers. Argentina follows with almost 2.8 million square kilometers. Cuba, whose surface area extends over almost 111,000 square kilometers, is the Caribbean country with the largest territory.

Brazil: a country with a lot to offer

Brazil's borders reach nearly half of the South American subcontinent, making it the fifth-largest country in the world and the third-largest country in the Western Hemisphere. Along with its landmass, Brazil also boasts the largest population and economy in the region. Although Brasília is the capital, the most significant portion of the country's population is concentrated along its coastline in the cities of São Paulo and Rio de Janeiro.

South America: a region of extreme geographic variation

With the Andes mountain range in the West, the Amazon Rainforest in the East, the Equator in the North, and Cape Horn as the Southern-most continental tip, South America has some of the most diverse climatic and ecological terrains in the world. At its core, its biodiversity can largely be attributed to the Amazon, the world's largest tropical rainforest, and the Amazon river, the world's largest river. However, with this incredible wealth of ecology also comes great responsibility. In the past decade, roughly 80,000 square kilometers of the Brazilian Amazon were destroyed. And, as of late 2019, there were at least 1,000 threatened species in Brazil alone.

Land area (sq. km) in World was reported at 129718826 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

The statistic shows the largest countries in South America, based on land area. Brazil is the largest country by far, with a total area of over 8.5 million square kilometers, followed by Argentina, with almost 2.8 million square kilometers.

The international land border between the United States and Canada is the longest in the world at almost 8,900 kilometers. It includes the border between Canada and the continental U.S. as well as the border between Alaska and northern Canada.

This map, compiled to support the analysis of SOLAW report concerning land tenure and water rights (FAO -NRL, SOLAW 2010 Report 5A - Hotspots of land tenure and water rights) shows the mean surface availability of agricultural land use per capita at global scale. Land use agricultural availability varies considerably according to different regions. For example, the agricultural population in China has an average of 0.66 ha agricultural land per person, while that of Argentina has 41 ha. But the country averages most often give a false picture of reality, because the agricultural land use, which here include pastures, do not have the same potential, and because the situations of different regions in one country can be extremely mixed.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

This dataset provides high-resolution (10 m) industrial land maps for 1,093 global cities from 2017 to 2023.

The dataset includes:

• 10 m resolution industrial land maps for each year (GeoTIFF, Zip)
• Detailed information for each city, including industrial land area per capita indicators (1093_city_information.xlsx)
• Validation Package (Validation_Package.zip)

File Naming Convention

• GeoTIFF files: Industrial_land_XXX_YYY_YEAR.tif
  • XXX: Country code
  • YYY: City ID
  • YEAR: Year of data
  • Example: Industrial_land_USA_634_2017.tif represents the industrial land map for Chicago, USA, in 2017.

Each TIF file has a 10 m spatial resolution with the GCS_WGS_1984 spatial projection. The maps include three classes:

• Class 1: Industrial land in built-up areas
• Class 2: Non-industrial land in built-up areas
• Class 0: Non-built-up areas

City Information

A detailed summary of city-specific information, including the annual total industrial land area, is provided in 1093_city_information.xlsx. This file includes:

• ID_HDC_G0: Unique city ID (Urban Centre)
• CTR_MN_NM: Main country name
• CTR_MN_ISO: ISO-3 country codes
• UC_NM_MN: Main city (Urban Centre) name
• UC_NM_LST: Full list of assigned city (Urban Centre) names
• CLUSTER: Assigned cluster number in industrial land modeling
• URB_ECOREGION: Assigned urban ecoregion
• CLUSTER: Assigned cluster number in industrial land modeling
• IND_YEAR: Total industrial land area for each year (in m²).

Validation Package

• This package includes validation samples used for industrial land mapping validation.
• Validation_shapefile/: Contains the validation shapefiles used for assessing the accuracy of industrial land maps.
• Validation of Industrial Land Map Using CO₂ Emissions Data.xlsx: Includes validation results comparing industrial land maps with CO₂ emissions data.
• Association between Proposed Industrial Land and the Official Data.xlsx: Contains data assessing the relationship between the proposed industrial land and official datasets.

In 2023, New York led the ranking of the largest built-up urban areas worldwide, with a land area of ****** square kilometers. Boston-Providence and Tokyo-Yokohama were the second and third largest megacities globally that year.

Japan Urban Land Price Index: Biggest 6 Cities: Average data was reported at 75.200 31Mar2000=100 in Sep 2017. This records an increase from the previous number of 74.100 31Mar2000=100 for Mar 2017. Japan Urban Land Price Index: Biggest 6 Cities: Average data is updated semiannually, averaging 69.950 31Mar2000=100 from Mar 1955 (Median) to Sep 2017, with 126 observations. The data reached an all-time high of 291.140 31Mar2000=100 in Sep 1990 and a record low of 1.660 31Mar2000=100 in Mar 1955. Japan Urban Land Price Index: Biggest 6 Cities: Average data remains active status in CEIC and is reported by Japan Real Estate Institute. The data is categorized under Global Database’s Japan – Table JP.EB016: Urban Land Price Index: 31Mar2000=100. Rebased from 31Mar2000=100 to 31Mar2010=100 Replacement series ID: 403770597

Dataset Overview

This dataset is associated with a pre-print article "Deep Learning for Monitoring Large-Scale Croplands in sub-Saharan Africa" and consists of land-use/land cover classifications of known Large-Scale Land Acquisitions (LSLA) in Tanzania and Ethiopia. In total, 12 LSLA sites were classified based on available high-resolution and Sentinel-2 imagery spanning 2006 to 2018. Where possible, classifications before and after the occurrence of LSLA are provided (see kilimo_kubwa_metadata.xlsx file). Additionally, land-use land cover classifications are provided for a set of "treatment" areas exposed to LSLAs and control areas with no LSLAs to provide a basis of comparison and counterfactual analysis. The land-use/ land cover include 12 classifications covering various cropland types, forest, natural land cover, built area and water bodies (see kilimo_kubwa_metadata.xlsx file).

In addition to the land-use/land cover dataset, we provide the trained Random Forest (RF) and UNet model presented in "Deep Learning for Monitoring Large-Scale Croplands in sub-Saharan Africa". Find the trained models in the ml_models.zip folder in .pkl and .h5 format.

Article Abstract

Increasing commodity prices, rising food demand, and technological advances are changing the scale of global agriculture in the 21^st century, with large-scale croplands emerging as primary driver of global environmental change. Understanding how small-scale versus large-scale cropland contributes and responds to global environmental change is increasingly important for designing effective agricultural, development, and land-use policies. However, current remote sensing methods are inadequate for differentiating small versus large cropland types across broad spatial scales. Existing methods to monitor large-scale cropland are designed at local or site-specific scales with unknown skill in generalizing to new regions. We address this gap by leveraging machine learning to differentiate small-scale versus large-scale cropland in Tanzania and Ethiopia from 2006-2018. We compare the ability of deep learning versus random forest to disaggregate cropland by size using both in-sample and out-of-sample datasets. We find that a random forest model performs better in-sample (88% overall accuracy) compared to a deep learning UNet model (81%). The deep learning UNet model, however, generalizes better out-of-sample with 72-74% accuracy compared to random forest (62-69%). Our findings suggest that deep learning models provide greater generalization because they are more robust to changing landscape patterns, although they are more sensitive to sensor noise. The acceleration of large-scale croplands is occurring across the Global South, demanding methods capable of accurately monitoring a diverse set of agricultural conditions. We anticipate our dataset and deep learning method to be a starting point for scaling identification of large-scale croplands that can be amended with additional hand-labeled data, leveraged for computer generated labels, or applied to more sophisticated model frameworks such as transfer learning. Time-series of large-scale croplands at country or continental scales will be important to improved understanding of shifting food systems, food-security, and impacts on global environmental change.

Land Supply: Year to Date: Residential: Commodity Bldg: Deluxe & Large Size: Hainan data was reported at 102.906 ha in Jun 2012. This records a decrease from the previous number of 296.390 ha for Dec 2011. Land Supply: Year to Date: Residential: Commodity Bldg: Deluxe & Large Size: Hainan data is updated quarterly, averaging 200.381 ha from Jun 2010 (Median) to Jun 2012, with 4 observations. The data reached an all-time high of 381.430 ha in Dec 2010 and a record low of 102.906 ha in Jun 2012. Land Supply: Year to Date: Residential: Commodity Bldg: Deluxe & Large Size: Hainan data remains active status in CEIC and is reported by Ministry of Natural Resources. The data is categorized under China Premium Database’s Real Estate Sector – Table CN.RKL: Land Supply: Residential: Commodity Building: Deluxe & Large Size: By Region.

Suriname and French Guyana are the countries with the largest forest area as a percentage of their respective total land area. As of 2020, some ** percent of the countries' territories was covered by forests. This was followed by neighboring Guyana, where roughly ** percent of total land is overlaid by forests. Comparatively, as the largest country in the world, Russia was also the country with the greatest forest area, at over *** million hectares.

Land Supply: Year to Date: Residential: Commodity Bldg: Deluxe & Large Size: Tianjin data was reported at 113.290 ha in Jun 2012. This records a decrease from the previous number of 419.000 ha for Dec 2011. Land Supply: Year to Date: Residential: Commodity Bldg: Deluxe & Large Size: Tianjin data is updated quarterly, averaging 416.000 ha from Jun 2010 (Median) to Jun 2012, with 4 observations. The data reached an all-time high of 635.000 ha in Dec 2010 and a record low of 113.290 ha in Jun 2012. Land Supply: Year to Date: Residential: Commodity Bldg: Deluxe & Large Size: Tianjin data remains active status in CEIC and is reported by Ministry of Natural Resources. The data is categorized under China Premium Database’s Real Estate Sector – Table CN.RKL: Land Supply: Residential: Commodity Building: Deluxe & Large Size: By Region.

We (Intelligent Mining and Analysis of Remote Sensing big data, IMARS) create a large-scale annotated dataset (Globe230k) for land use/land cover (LULC) mapping, which is annotated on Google Earth image of 1 m spatial resolution. Globe230k is annotated by numerous experts and students major in survey and mapping after necessary training, through visual interpretation on very high-resolution images, as well as in-situ field survey, under the guidance of the organized annotation pipeline. Globe230k has three superiorities:

1) Large scale: the Globe230k includes 232,819 annotated images with the size of 512x512 and spatial resolution of 1 m, with more than 3x10¹⁰ annotated pixels, and it includes 10 first-level categories.

2) Rich diversity: the annotated images are sampled from worldwide regions, with coverage area of over 60,000 km², indicating a high variability and diversity. Besides, in order to ensure the category balance, we intentionally give more chance to the rare categories to be sampled, such as wetland, ice/snow, etc.

3) Multi-modal: Globe230k not only contains RGB bands, but also include other important features for Earth system research, such as Normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical-vertical polarization (VV) bands, vertical-horizontal polarization (VH) bands, which can facilitate the multi-modal data fusion research.(This part will updating soon).

The image patches and their corresponding annotated patches are respectively stored in "patch_image.rar" and "patch_label.rar" file. The RGB image is in forms of ".jpg", with size of 512x512, the pixel value is ranged from 0-255. The annotated patches is in forms of ".png", also with size of 512x512, the pixel value is ranged from 1-10, which respectively represent 1#cropland, 2#forest, 3#grass, 4#shrubland, 5#wetland, 6#water, 7#tundra, 8#impervious, 9#bareland, 10#ice/snow. The total 232,819 pairs are officially divided into training set, validation set, and test set, based on ratio of 7:1:2, which can be find in "train.txt","val.txt","test.txt" file. Based on this division, the official baseline accuracy of several state-of-the-art semantic segmentation can be found in the related arcticle (https://spj.science.org/doi/10.34133/remotesensing.0078).

We hope it can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.

Source data of publication “Land grabbing” and REDD+: global synthesis of drivers and risks by Ameur M. Manceur, Sarah Carter, Louis Verchot, Martin Herold and Ralf Seppelt

Graph and download economic data for NASDAQ Global Real Estate Large Mid Cap TR Index (NASDAQNQG35LMT) from 2001-03-30 to 2025-10-10 about mid cap, market cap, NASDAQ, large, real estate, and indexes.

Graph and download economic data for NASDAQ Global Real Estate Large Mid Cap NTR Index (NASDAQNQG35LMN) from 2001-03-30 to 2025-09-15 about mid cap, market cap, NASDAQ, large, real estate, and indexes.

Land area (sq. km) in United States was reported at 9147420 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.