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 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

The Berkeley website provides data and analysis for a number of weather stations within the North Slope region. Data download and summary graphs with trend are provided. The datasets presented are divided into three categories: Output data, Source data, and Intermediate data. The Berkeley Earth averaging process generates a variety of Output data including a set of gridded temperature fields, regional averages, and bias-corrected station data. Source data consists of the raw temperature reports that form the foundation of our averaging system. Source observations are provided as originally reported and will contain many quality control and redundancy issues. Intermediate data is constructed from the source data by merging redundant records, identifying a variety of quality control problems, and creating monthly averages from daily reports when necessary. The definitive repository for Source and Intermediate data is located in the SVN, which is built nightly. Sites include: Alpine, Ambler, Anaktuvuk, Atqasuk, Barrow, Cape Lisburne, Deadhorse, Dietrich Camp, Franklin Bluff, Galbraith Lake, Happy Valley, Lonely, Noatak, Nuiqsut, Oliktok, Point Lay, Prudhoe Bay, Red Dog, Sag River and UGNU Kuparuk

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?

The Earth Observing System Data and Information System (EOSDIS) is a major core capability within NASA''s Earth Science Data Systems Program. EOSDIS ingests, processes, archives and distributes data from a large number of Earth observing satellites. EOSDIS consists of a set of processing facilities and Earth Science Data Centers distributed across the United States and serves hundreds of thousands of users around the world, providing hundreds of millions of data files each year covering many Earth science disciplines. In order to serve the needs of a broad and diverse community of users, NASA''s Earth Science Data Systems Program is comprised of both Core and Community data system elements. Core data system elements reflect NASA''s responsibility for managing Earth science satellite mission data characterized by the continuity of research, access, and usability. The core comprises all the hardware, software, physical infrastructure, and intellectual capital NASA recognizes as necessary for performing its tasks in Earth science data system management. Community data system elements are those pieces or capabilities developed and deployed largely outside of NASA core elements and are characterized by their evolvability and innovation. Successful applicable elements can be infused into the core, thereby creating a vibrant and flexible, continuously evolving infrastructure. NASA''s Earth Science program was established to use the advanced technology of NASA to understand and protect our home planet by using our view from space to study the Earth system and improve prediction of Earth system change. To meet this challenge, NASA promotes the full and open sharing of all data with the research and applications communities, private industry, academia, and the general public. NASA was the first agency in the US, and the first space agency in the world, to couple policy and adequate system functionality to provide full and open access in a timely manner - that is, with no period of exclusive access to mission scientists - and at no cost. NASA made this decision after listening to the user community, and with the background of the then newly-formed US Global Change Research Program, and the International Earth Observing System partnerships. Other US agencies and international space agencies have since adopted similar open-access policies and practices. Since the adoption of the Earth Science Data Policy adoption in 1991, NASA''s Earth Science Division has developed policy implementation, practices, and nomenclature that mission science teams use to comply with policy tenets. Data System Standards NASA''s Earth Science Data Systems Groups anticipate that effective adoption of standards will play an increasingly vital role in the success of future science data systems. The Earth Science Data Systems Standards Process Group (SPG), a board composed of Earth Science Data Systems stakeholders, directs the process for both identification of appropriate standards and subsequent adoption for use by the Earth Science Data Systems stakeholders.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

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

What is SEPAL?

SEPAL (https://sepal.io/) is a free and open source cloud computing platform for geo-spatial data access and processing. It empowers users to quickly process large amounts of data on their computer or mobile device. Users can create custom analysis ready data using freely available satellite imagery, generate and improve land use maps, analyze time series, run change detection and perform accuracy assessment and area estimation, among many other functionalities in the platform. Data can be created and analyzed for any place on Earth using SEPAL.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/63a3efa0-08ab-4ad6-9d4a-96af7b6a99ec/download/cambodia_mosaic_2020.png" alt="alt text" title="Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia">

Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia

SEPAL reaches over 5000 users in 180 countries for the creation of custom data products from freely available satellite data. SEPAL was developed as a part of the Open Foris suite, a set of free and open source software platforms and tools that facilitate flexible and efficient data collection, analysis and reporting. SEPAL combines and integrates modern geospatial data infrastructures and supercomputing power available through Google Earth Engine and Amazon Web Services with powerful open-source data processing software, such as R, ORFEO, GDAL, Python and Jupiter Notebooks. Users can easily access the archive of satellite imagery from NASA, the European Space Agency (ESA) as well as high spatial and temporal resolution data from Planet Labs and turn such images into data that can be used for reporting and better decision making.

National Forest Monitoring Systems in many countries have been strengthened by SEPAL, which provides technical government staff with computing resources and cutting edge technology to accurately map and monitor their forests. The platform was originally developed for monitoring forest carbon stock and stock changes for reducing emissions from deforestation and forest degradation (REDD+). The application of the tools on the platform now reach far beyond forest monitoring by providing different stakeholders access to cloud based image processing tools, remote sensing and machine learning for any application. Presently, users work on SEPAL for various applications related to land monitoring, land cover/use, land productivity, ecological zoning, ecosystem restoration monitoring, forest monitoring, near real time alerts for forest disturbances and fire, flood mapping, mapping impact of disasters, peatland rewetting status, and many others.

The Hand-in-Hand initiative enables countries that generate data through SEPAL to disseminate their data widely through the platform and to combine their data with the numerous other datasets available through Hand-in-Hand.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/868e59da-47b9-4736-93a9-f8d83f5731aa/download/probability_classification_over_zambia.png" alt="alt text" title="Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia">

Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia

The MOD11A1 V6.1 product provides daily land surface temperature (LST) and emissivity values in a 1200 x 1200 kilometer grid. The temperature value is derived from the MOD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is the average of all qualifying observations. Provided along with both the day-time and night-time surface temperature bands and their quality indicator layers are MODIS bands 31 and 32 and six observation layers. Documentation: User's Guide Algorithm Theoretical Basis Document (ATBD) General Documentation

The centuries-old quest for other worlds like our Earth has been rejuvenated by the intense excitement and popular interest surrounding the discovery of hundreds of planets orbiting other stars. There is now clear evidence for substantial numbers of three types of exoplanets; gas giants, hot-super-Earths in short period orbits, and ice giants. The following websites are tracking the day-by-day increase in new discoveries and are providing information on the characteristics of the planets as well as those of the stars they orbit: The Extrasolar Planets Encyclopedia, NASA Exoplanet Archive, New Worlds Atlas, and Current Planet Count Widget. The challenge now is to find terrestrial planets (i.e., those one half to twice the size of the Earth), especially those in the habitable zone of their stars where liquid water and possibly life might exist. The Kepler Mission, NASA Discovery mission #10, is specifically designed to survey a portion of our region of the Milky Way galaxy to discover dozens of Earth-size planets in or near the habitable zone and determine how many of the billions of stars in our galaxy have such planets. Results from this mission will allow us to place our solar system within the continuum of planetary systems in the Galaxy.

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 …

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

This data set set contains version 1.0 of raw, 1.05- to 4.8-micron spectra of Earth acquired by the High Resolution Infrared Spectrometer (HRII) during the EPOCh phase of the EPOXI mission. Three sets of observations were acquired on 18-19 March, 28-29 May, and 4-5 June 2008 to characterize Earth as an analog for extrasolar planets. Each observing period lasted approximately 24 hours, and spectra were acquired twice per hour. During the observing period in May, the Moon transited across Earth as seen from the spacecraft. Additional Earth observations are planned for the mission, and these data will be added to a future version of this data set.

Oxygen Exposure for Benthic Megafauna near San Diego

Spatially Varying Environmental Risk

By [source]

About this dataset

This dataset captures an in-depth look into the environmental conditions of the underwater world off Southern California's coast. It provides invaluable information related to spatial risk variation, such as oxygen exposure levels, depths and habitat criteria of 53 species of benthic and epibenthic megafauna recorded during the three-year study. This data will provide insight into aquatic life dynamics and potentially generate improved management strategies for protecting these vital species. Moreover, due to the importance that waters play within our planet's fragile ecosystem, a proper understanding of their affairs could lead to greater marine sustainability in the long-term. Ultimately, this dataset may help answer our questions about how exactly ocean life is responding to intense human activity and its effects on today's seaside communities

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Download and install the dataset: The dataset contains two .csv files, each containing data from the three-year study on oxygen exposure for benthic and epibenthic megafauna off the coast of San Diego in Southern California. Download these two files to your computer and save them for further analysis.

• Familiarize yourself with the datasets: Each file includes very detailed information about a particular variable related to the study (for example, SpeciesMetadata contains species-level information on 53 species of benthic and epibenthic megafauna). Read through each data sheet carefully in order to gain a better understanding of what's included in each column.

• Clean up any outliers or missing values: Once you understand which columns are important for your analysis, you can begin cleaning up any outliers or missing values that may be present in your dataset. This is an important step as it will help ensure that further analysis is performed accurately.

• Choose an appropriate visualization method: Depending on what type of results you want to show from your analysis, choose an appropriate visualization method (e.g., bar plot, scatterplot). Also consider if adding labeling such as color with respect to categories would improve legibility of figures you produce from this dataset during exploratory data analyses stages.

  5) Choose a statistical test suitable for this type of project: Once allyour visuals have been produced its time to interpret results using statistics tests depending on how many categorical variables are presentin the data set (i.e t-test or ANOVA). As well understand key outputs like p_values so experiment could effectively conclude if thereare significant differences between treatmentswhen comparing distributions among samples/populations being studied here.. Be sureto adjust mean size/sample size when performing statistic testsuitably accordingto determining adequate power when selecting applicable tests etc.

Research Ideas

• Comparing the effects of different environmental factors (depth, temperature, salinity etc.) on depth-specific distributions of oxygen and benthic megafauna.
• Identifying and mapping vulnerable areas for benthic species based on environmental factors and oxygen exposure patterns.
• Developing models to predict underlying spatial risk variables for endangered species to inform conservation efforts in the study area

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: ROVObservationData.csv

File: SpeciesMetadata.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are onboard the Landsat 8 satellite, have acquired images of the Earth since February 2013. The sensors collect images of the Earth with a 16-day repeat cycle, referenced to the Worldwide Reference System-2. The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). Landsat 8 image data files consist of 11 spectral bands with a spatial resolution of 30 meters for bands 1-7 and bands 9-11; 15-meters for the panchromatic band 8. Delivered Landsat 8 Level-1 data typically include both OLI and TIRS data files; however, there may be OLI-only and/or TIRS-only scenes in the USGS archive. A Quality Assurance (QA.tif) band is also included. This file provides bit information regarding conditions that may affect the accuracy and usability of a given pixel – clouds, water or snow, for example.

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.

The SeaWinds on QuikSCAT Level 1B dataset contains the geo-located Sigma-0 measurements and antenna pulse "egg" and "slice" geometries as derived from ephemeris and the Level 1A dataset. The pulse "egg" represents the complete footprint of the pulse, which has a spatial geometry of approximately 25 km by 35 km. There are 8 slices that constitute the range-binned components of a pulse each of which has a spatial geometry of approximately 25 km by 7 km. The orientation of the long dimension of the slices varies with the rotation of the antenna and thus does not align with the along/across track orientation of the wind vector grid in the L2B/L2A products. This dataset represents the second reprocessed version of the Level 1B release. Special note: QuikSCAT went into a "non-spinning" mode on 22 November 2009. The final rev number in the nominal Operational "spinning" mode is 54296; the "non-spinning" mode of the instrument continued predominantly until the end of the time series. There were some brief periods of "spinning" in between, which include the following days and rev numbers (identified in parenthesis): 1) 29 January 2013 to 5 February 2013 (7909-71011), 2) 14 March 2013 (71536-71549), 3) 18 March 2013 to 21 March 2013 (71590-71634), and 4) 28 March 2013 to 31 March 2013 (71735-71769). Data during the "non-spinning" mode is not consistently calibrated with data from the "spinning" mode. Furthermore, incidence angles change periodically during the "non-spinning" mode. It is therefore advised that only "expert" users attempt using the data during the "non-spinning" mode. For standard L1B data users who wish to access consistently calibrated L1B data during the "non-spinning" mode, please consider using the L1B Averaged Sigma-0 dataset as alternative, which may be accessed by contacting podaac@podaac.jpl.nasa.gov

The Sentinel-1 mission provides data from a dual-polarization C-band Synthetic Aperture Radar (SAR) instrument at 5.405GHz (C band). This collection includes the S1 Ground Range Detected (GRD) scenes, processed using the Sentinel-1 Toolbox to generate a calibrated, ortho-corrected product. The collection is updated daily. New assets are ingested within two days after they become available. This collection contains all of the GRD scenes. Each scene has one of 3 resolutions (10, 25 or 40 meters), 4 band combinations (corresponding to scene polarization) and 3 instrument modes. Use of the collection in a mosaic context will likely require filtering down to a homogeneous set of bands and parameters. See this article for details of collection use and preprocessing. Each scene contains either 1 or 2 out of 4 possible polarization bands, depending on the instrument's polarization settings. The possible combinations are single band VV, single band HH, dual band VV+VH, and dual band HH+HV: VV: single co-polarization, vertical transmit/vertical receive HH: single co-polarization, horizontal transmit/horizontal receive VV + VH: dual-band cross-polarization, vertical transmit/horizontal receive HH + HV: dual-band cross-polarization, horizontal transmit/vertical receive Each scene also includes an additional 'angle' band that contains the approximate incidence angle from ellipsoid in degrees at every point. This band is generated by interpolating the 'incidenceAngle' property of the 'geolocationGridPoint' gridded field provided with each asset. Each scene was pre-processed with Sentinel-1 Toolbox using the following steps: Thermal noise removal Radiometric calibration Terrain correction using SRTM 30 or ASTER DEM for areas greater than 60 degrees latitude, where SRTM is not available. The final terrain-corrected values are converted to decibels via log scaling (10*log10(x)). For more information about these pre-processing steps, please refer to the Sentinel-1 Pre-processing article. For further advice on working with Sentinel-1 imagery, see Guido Lemoine's tutorial on SAR basics and Mort Canty's tutorial on SAR change detection. This collection is computed on-the-fly. If you want to use the underlying collection with raw power values (which is updated faster), see COPERNICUS/S1_GRD_FLOAT.

Initially, the format of this dataset was .json, so I converted it to .csv for ease of data processing.

"Online articles from the 25 most popular news sites in Vietnam in July 2022, suitable for practicing Natural Language Processing in Vietnamese.

Online news outlets are an unavoidable part of our society today due to their easy access, mostly free. Their effects on the way communities think and act is becoming a concern for a multitude of groups of people, including legislators, content creators, and marketers, just to name a few. Aside from the effects, what is being written on the news should be a good reflection of people’s will, attention, and even cultural standard.

In Vietnam, even though journalists have received much criticism, especially in recent years, news outlets still receive a lot of traffic (27%) compared to other methods to receive information."

Original Data Source: Vietnamese Online News .csv dataset