About this dataset

Context

The dataset tabulates the Earth population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Earth. The dataset can be utilized to understand the population distribution of Earth by age. For example, using this dataset, we can identify the largest age group in Earth.

Key observations

The largest age group in Earth, TX was for the group of age 10 to 14 years years with a population of 102 (10.89%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Earth, TX was the 85 years and over years with a population of 4 (0.43%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Earth is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Earth total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Earth Population by Age. You can refer the same here

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.

About this dataset

Context

The dataset tabulates the Earth population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Earth across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Earth was 897, a 0.44% decrease year-by-year from 2022. Previously, in 2022, Earth population was 901, a decline of 0.55% compared to a population of 906 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Earth decreased by 204. In this period, the peak population was 1,101 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Earth is shown in this column.
• Year on Year Change: This column displays the change in Earth population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Earth Population by Year. You can refer the same here

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_population_in_1800. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

Humans need food, shelter, and water to survive. Our planet provides the resources to help fulfill these needs and many more. But exactly how much of an impact are we making on our planet? And will we reach a point at which the Earth can no longer support our growing population?Just like a bank account tracks money spent and earned, the relationship between human consumption of resources and the number of resources the Earth can supply—our human footprint—can be measured. Our human footprint can be calculated for an individual, town, or country, and quantifies the intensity of human pressures on the environment. The Human Footprint map layer is designed to do this by deriving a value representing the magnitude of the human footprint per one square kilometer (0.39 square miles) for every biome.This map layer was created by scientists with data from NASA's Socioeconomic Data and Applications Center to highlight where human pressures are most extreme in hopes to reduce environmental damage. The Human Footprint map asks the question, where are the least influenced, most “wild” parts of the world?The Human Footprint map was produced by combining thirteen global data layers that spatially visualize what is presumed to be the most prominent ways humans influence the environment. These layers include human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). Based on the amount of overlap between layers, each square kilometer value is scaled between zero and one for each biome. Meaning that if an area in a Moist Tropical Forest biome scored a value of one, that square kilometer of land is part of the one percent least influenced/most wild area in its biome. Knowing this, we can help preserve the more wild areas in every biome, while also highlighting where to start mitigating human pressures in areas with high human footprints.So how can you reduce your individual human footprint? Here are just a few ways:Recycle: Recycling helps conserve resources, reduces water and air pollution, and helps save space in overcrowded landfills.Use less water: The average American uses 310 liters (82 gallons) of water a day. Reduce water consumption by taking shorter showers, turning off the water when brushing your teeth, avoiding pouring excess drinking water down the sink, and washing fruits and vegetables in a bowl of water rather than under the tap.Reduce driving: When you can, walk, bike, or take a bus instead of driving. Even 3 kilometers (2 miles) in a car puts about two pounds of carbon dioxide (CO2) into the atmosphere. If you must drive, try to carpool to reduce pollution. Lastly, skip the drive-through. You pollute more when you sit in a line while your car is emitting pollutant gases.Know how much you’re consuming: Most people are unaware of how much they are consuming every day. Calculate your individual ecological footprint to see how you can reduce your consumption here.Systemic implications: Individually, we are a rounding error. Take some time to understand how our individual actions can inform more systemic changes that may ultimately have a bigger impact on reducing humanity's overarching footprint.

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

The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.

For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L. Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: A new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Maintainer: Sarah E., Castle

Data lineage:

The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 5 kilometers of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 5-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Forest proximate people - 5km cutoff distance"

About this dataset

Context

The dataset tabulates the population of White Earth by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Earth. The dataset can be utilized to understand the population distribution of White Earth by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Earth. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Earth.

Key observations

Largest age group (population): Male # 10-14 years (17) | Female # 40-44 years (13). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the White Earth population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White Earth is shown in the following column.
• Population (Female): The female population in the White Earth is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in White Earth for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth Population by Gender. You can refer the same here

Population, female (% of total population) in World was reported at 49.71 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Population, female (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Monthly average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). As these data are composited monthly, there are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to …

The Asian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

 This project (which has been carried out as a cooperative activity
 between NCGIA, CGIAR and UNEP/GRID between Oct. 1995 and present) has
 pooled available data sets, many of which had been assembled for the
 global demography project. All data were checked, international
 boundaries and coastlines were replaced with a standard template, the
 attribute database was redesigned, and new, more reliable population
 estimates for subnational units were produced for all countries. From
 the resulting data sets, raster surfaces representing population
 distribution and population density were created in collaboration
 between NCGIA and GRID-Geneva.

Metadata record for data from ASAC Project 2668 See the link below for public details on this project.

The dataset contains data in the following formats:

The *.met files contain the height, time, direction and range of a meteor detection.

The *.vel file contains meteor determined wind velocities: the horizontal and vertical velocities.

There are other ancillary parameters in each file but these are the main ones.

The parameters are described in the pdf document included in the dataset. We have been able to get IDL based reading routines from the radar company (ATRAD) but in general, one is expected to write ones own software for reading the datasets.

Public The gap in our knowledge of the mesosphere and lower thermosphere (MLT) has stemmed from a difficulty in probing this remote region of our atmosphere. Spanning the height range between 50 and 110 km, the MLT is sometimes jokingly termed the 'ignorosphere'. However, observations from sites in Antarctica can now be combined with satellite data to overcome the limitations of our observing techniques. This project seeks to learn more about the many processes that contribute to the character of this region, with the goal of enhancing our understanding of the earth's atmosphere and identifying the effects of global climate change.

Project objectives: This project aims to provide a point of focus within the Australian Antarctic Program for investigations of the polar mesosphere and lower thermosphere (MLT) using satellite observations. Ground-based measurements typically have excellent vertical and temporal resolution, but are limited in their horizontal coverage. Satellite observations, on the other hand, provide a global perspective that cannot be achieved with ground-based instruments. Our knowledge of the polar MLT and its role in the global climate system can be significantly enhanced through studies that combine ground-based and satellite based measurements.

The importance of ground-based measurements of the structure and dynamics of the polar MLT is underlined by the Australian Antarctic Program's support of the unique combination of experiments operated at Davis station. An MF (medium frequency) radar measures horizontal wind speeds in this region every few minutes. A VHF (very high frequency) radar, LIDAR (laser radar) and a spectrometer provide other wind and temperature measurements when conditions allow. And all of these instruments yield data with a temporal and altitude resolution that cannot be achieved using a satellite.

Satellite observations of the MLT have, until recently, neglected the polar regions. The Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) mission, whose primary goal is to investigate and understand the basic structure, variation, and energy balance of the MLT region and the Ionosphere [Yee, 2003], sought to redress this neglect. Since its launch in December 2001, the TIMED satellite has made observations that extend well into the polar regions and include the latitude of Davis

Significantly, the instigators of TIMED recognised the contribution that ground-based experiments will make to its scientific yield by explicitly including them in the mission. A group of Ground Based Investigators (GBIs) have been funded to facilitate the incorporation of ground-based data sets into TIMED activities. The Davis MF radar is one of the instruments to be included in the TIMED mission through this mechanism.

It is therefore timely to focus some of our research activity on the opportunities provided by satellites such as TIMED. The availability of polar satellite data extends the reach of our existing ground-based experiments and adds value to our scientific endeavours. As a result, the common goals of the TIMED mission and the Australian Antarctic Science Program are achieved, our understanding of the role of Antarctica in the global climate system is enhanced and our international scientific profile is increased.

A document providing further details about the history of the project is available for download at the provided URL.

Taken from the 2009-2010 Progress Report: Progress against objectives: -Adding value to satellite data and ground-based data: As a result of the Fulbright sponsored visit of co-investigator Palo in late 2008, it is now clear that, due to differences in the characteristics of space- and ground-based data, the design of techniques for combining data sets should be specific to the wave class being considered (principally planetary waves and tides).

Significant contributions to the Aeronomy of Ice in the Mesosphere (AIM) satellite mission have been made using the tidal observations and analysis that form part of project 674. In the context of the current project, progress has been made in the following areas.

The 2007/2008 season of southern hemisphere observations has become a focus because both the AIM satellite instruments and the Antarctic MF radars operated well for much of that time. The Cloud Imaging and Particle Size (CIPS) instrument on AIM has now been used extensively to image and map the occurrence of Polar Mesospheric Clouds (PMC) and to identify gravity wave signatures within these clouds The position and time of the centre pixel of each usable CIPS image in the 2007/2008 season forms the basis of a number of our studies. These locations and times are combined with a representation of the tidal wind field that can be calculated for the mesosphere and lower thermosphere south of about 60 degrees. Values of the tides at the time of the CIPS samples provide a measure of the wind variations due to the tides (but not the mean winds of planetary waves) throughout the season.

This extensive tidal data base is being used to consider the temporal and seasonal variability of PMC occurrence. Satellite up-leg and down-leg observations show systematic differences that are yet to be explained. A proxy for the temperature history of air parcels sampled by the satellite that considers the tidal perturbations due to the zonally symmetric tides (diurnal and semidiurnal) has been proposed. Knowledge of the spatial and temporal variation of the wind field obtained from the tides is then used to trace the air parcel position back in time by 3 or 6 hours (estimates of the time taken to form a PMC) and to assess the extent of the upwelling and thus temperature influence on the observed air parcel. Similarities to the PMC occurrence are apparent and are being further investigated.

Tides are a possible modulator of gravity wave activity in the polar mesosphere so the role they might play in distorting the observed distribution of gravity waves is being explored. The distribution of the winds in the tidal wind field sampled by the CIPS instrument (whose sampling scheme is determined by the orbit period and satellite precession rate) has been compared to the actual distribution (derivable from the tidal winds by applying a regular sampling regime). Although the potential for bias is present, the range of heights below the cloud layer in which the tides have had significant amplitude is only a few kilometres so it is currently thought the bias will not be great. Comparisons of the distributions of the zonally and meridionally propagating gravity waves are to be made by our colleagues to consider this question further.

The potential for the AIM sampling scheme to 'alias' tidal variations into the planet-scale maps of ice occurrence has been considered. Regularly sampled tidal winds and those sampled by a CIPS sampling scheme have been analysed for their spatial and temporal variations and comparisons made to see if aliasing is occurring. However, this study is yet to be extended to the entire season. At this stage, only wind effects have been included. Improvements to a model that calculates the tidal temperature response is required and a strategy for making those improvements has been identified but has not been programmed into software.

In addition to the AIM satellite studies, some more general areas of investigation have been pursued (albeit at a low level of activity).

A technique whereby the theoretical structure of atmospheric tides (described using Hough modes) is extended to include the characteristics of a real atmosphere (Hough mode extensions or HMEs) has been proposed for combining data sets and is being explored. Discussions with our colleagues from NCAR (USA) and Clemson University (USA) (who generate the HMEs) have identified some concerns about the quality of the representation of tidal dissipation and the effect this has on the HMEs. We await further advice on this.

A technique whereby planetary-wave heat fluxes can be calculated using space-based temperatures and ground-based winds has been designed and is to be tested using the results of a previous ground-based only study. The long period (multiple days) and large scale of these waves, along with the ability to remove the mean temperature by decomposition, (and therefore any instrumental biases) make this study possible given the practical difficulties noted elsewhere. The software required for the extraction of the necessary data from the TIMED/SABER instrument data base at the University of Colorado is being developed in conjunction with colleagues there.

An explanation for a climatological dip in ground-based measurements of temperature at 87 km above Davis was proposed after TIMED/SABER satellite observations of large scale structures showed the presence of slowly moving wavenumber one features at the time of the dip. The outline of a manuscript on this subject has been drafted but the software required for some of the diagrams of the paper using University of Colorado computers is still being developed (see above). On completion, the proposed explanation will be tested against a more extensive data base.

These data represent 33MHz data. For 55MHz data from the meteor radar, see the related metadata record at the

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few. With increasing complexity and different sensing techniques at our disposal, it has become our primary interest to design efficient algorithms to retrieve data from multiple data modalities, given the complementary information that is captured by different sensors. This type of problem is referred to as inter-modal data retrieval. In remote sensing (RS), there are primarily two important types of problems, i.e., land-cover classification and object detection. In this work, we focus on the target-based object retrieval part, which falls under the realm of object detection in RS. Object retrieval essentially requires high-resolution imagery for objects to be distinctly visible in the image. The main challenge with the conventional retrieval approach using large-scale databases is that, quite often, we do not have any query image sample of the target class at our disposal. The target of interest solely exists as a perception to the user in the form of an imprecise sketch. In such situations where a photo query is absent, it can be immensely useful if we can promptly make a quick hand-made sketch of the target. Sketches are a highly symbolic and hieroglyphic representation of data. One can exploit the notion of this minimalistic representative of sketch queries for sketch-based image retrieval (SBIR) framework. While dealing with satellite images, it is imperative to collect as many samples of images as possible for each object class for object recognition with a high success rate. However, in general, there exists a considerable number of classes for which we seldom have any training data samples. Therefore, for such classes, we can use the zero-shot learning (ZSL) strategy. The ZSL approach aims to solve a task without receiving any example of that task during the training phase. This makes the network capable of handling an unseen class (new class) sample obtained during the inference phase upon deployment of the network. Hence, we propose the aerial sketch-image dataset, namely Earth on Canvas dataset.

Classes in this dataset: Airplane, Baseball Diamond, Buildings, Freeway, Golf Course, Harbor, Intersection, Mobile home park, Overpass, Parking lot, River, Runway, Storage tank, Tennis court.

Soils and soil moisture greatly influence the water cycle and have impacts on runoff, flooding and agriculture. Soil type and soil particle composition (sand, clay, silt) affect soil moisture and the ability of the soil to retain water. Soil moisture is also affected by levels of evaporation and plant transpiration, potentially leading to near dryness and eventual drought.Measuring and monitoring soil moisture can ensure the fitness of your crops and help predict or prepare for flash floods and drought. The GLDAS soil moisture data is useful for modeling these scenarios and others, but only at global scales. Dataset SummaryThe GLDAS Soil Moisture layer is a time-enabled image service that shows average monthly soil moisture from 2000 to the present at four different depth levels. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. The GLDAS soil moisture data is useful for modeling, but only at global scales. Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Depth: This layer has four depth levels. By default they are summed, but you can view each using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter. It is also possible to toggle between depth layers using raster functions, accessed through the Image Display tab.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available. This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

Subnational Population Database presents estimated population at the first administrative level below the national level. Many of the data come from the country’s national statistical offices. Other data come from the NASA Socioeconomic Data and Applications Center (SEDAC) managed by the Center for International Earth Science Information Network (CIESIN), Earth Institute, Columbia University. It is the World Bank Group’s first subnational population database at a global level and there are data limitations. Series metadata includes methodology and the assumptions made.

The countries party to SEATRACK host large and internationally important populations of several seabird species, many of which have experienced negative population trends over recent decades. Many seabird species are spread over vast oceanic areas for most of the year and only aggregate on land during the breeding season. Consequently, little is known about many aspects of their life away from the breeding grounds leaving large gaps in our knowledge and understanding of seabird life-histories.

Development of small and lightweight instruments, so-called light-logger or GLS (global location sensor) technology has now provided scientists with the means to monitor bird movements throughout the year on a much greater scale than before. The loggers primarily record light levels which, in relation to time of year and day, can be used to calculate twice daily positions of an individual within a radius of approximately 180 km. SEATRACK is utilizing the full potential of light-logger technology with a large-scale coordinated and targeted effort encompassing a representative choice of species, colonies and sample sizes. Such data will help researchers to identify:

• The most important moulting areas, migration routes and wintering areas for different seabird populations.
• The size and the composition of seabird populations during the non-breeding season.
• What environmental threats the different populations face.
• The origin of birds (i.e. the breeding population) that will be affected in acute incidents such as oil spills, mass mortality due to starvation or drowning in fishing gear.
• The different environmental conditions characterizing the different habitats occupied by Norwegian seabirds, how these change over time, and how they are reflected in the population dynamics and demography in the colonies
• Responses to climate change and how this affects the different populations.

Seabird migration patterns and non-breeding distribution have repeatedly been highlighted, by several social sectors as being some of the most important knowledge gaps, needed to be filled for effective management of seabird populations. SEATRACK intends to provide that information by producing:

• Distribution maps and population origin maps. Documenting the area use during the non-breeding season, including moulting areas, migration routes and wintering areas for different seabird populations over a three-year period. Estimating the size and the composition/colony origin of populations during the non-breeding season.
• Research articles about I) variation in migration strategies and the environmental factors underlying this variation, II) migration strategies and seabird demography/population dynamics, III) seabird migration strategies, human activities and marine spatial planning

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. The data-sets contain the population surfaces, metadata, and data quality layers. The population data surfaces are stored as GeoTIFF files for use in remote sensing or geographic information system (GIS) software. The data can also be explored via an interactive map - http://columbia.maps.arcgis.com/apps/View/index.html?appid=ce441db6aa54494cbc6c6cee11b95917 Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

As of February 2025, 5.56 billion individuals worldwide were internet users, which amounted to 67.9 percent of the global population. Of this total, 5.24 billion, or 63.9 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 20254. In The Netherlands, Norway and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide – over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a ten percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most significant usage penetration, 98 percent. In comparison, the worldwide average for the age group 15–24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.

Area Covered: World

What can I do with this layer?
The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.


It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.

Attribute Information

• Latitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?
• Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.
• Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?
• Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.
  
• Acquisition Date: Derived Date/Time field combining Date and Time attributes.
• Satellite: Whether the detection was picked up by the Terra or Aqua satellite.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.
• Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.
• Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

This map is provided for informational purposes and is not monitored 24/7 for accuracy and