This dataset consists of 17,300 images and includes two annotation files, each in a distinct format. The first file, labeled "Label," contains annotations at their original scale, while the second file, named "yolo_format_labels," provides annotations in YOLO format. The dataset was created by utilizing the OIDv4 toolkit, a specialized tool for gathering data from Google Open Images. It's important to emphasize that this dataset is exclusively dedicated to human detection.

This dataset is a valuable resource for training deep learning models tailored for human detection tasks. The images in the dataset are of high quality and have been meticulously annotated with bounding boxes encompassing the regions where humans are present. Annotations are available in two formats: the original scale, which represents pixel coordinates of the bounding boxes, and the YOLO format, which represents bounding box coordinates in a normalized form.

The dataset was carefully curated by scraping relevant images from Google Open Images using the OIDv4 toolkit. Only images relevant to human detection tasks were included. Consequently, it is an ideal choice for training deep learning models specifically designed for human detection tasks.

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

GEOSatDB is a semantic representation of Earth observation satellites and sensors that can be used to easily discover available Earth observation resources for specific research objectives.BackgroundThe widespread availability of coordinated and publicly accessible Earth observation (EO) data empowers decision-makers worldwide to comprehend global challenges and develop more effective policies. Space-based satellite remote sensing, which serves as the primary tool for EO, provides essential information about the Earth and its environment by measuring various geophysical variables. This contributes significantly to our understanding of the fundamental Earth system and the impact of human activities.Over the past few decades, many countries and organizations have markedly improved their regional and global EO capabilities by deploying a variety of advanced remote sensing satellites. The rapid growth of EO satellites and advances in on-board sensors have significantly enhanced remote sensing data quality by expanding spectral bands and increasing spatio-temporal resolutions. However, users face challenges in accessing available EO resources, which are often maintained independently by various nations, organizations, or companies. As a result, a substantial portion of archived EO satellite resources remains underutilized. Enhancing the discoverability of EO satellites and sensors can effectively utilize the vast amount of EO resources that continue to accumulate at a rapid pace, thereby better supporting data for global change research.MethodologyThis study introduces GEOSatDB, a comprehensive semantic database specifically tailored for civil Earth observation satellites. The foundation of the database is an ontology model conforming to standards set by the International Organization for Standardization (ISO) and the World Wide Web Consortium (W3C). This conformity enables data integration and promotes the reuse of accumulated knowledge. Our approach advocates a novel method for integrating Earth observation satellite information from diverse sources. It notably incorporates a structured prompt strategy utilizing a large language model to derive detailed sensor information from vast volumes of unstructured text.Dataset InformationThe GEOSatDB portal(https://www.geosatdb.cn) has been developed to provide an interactive interface that facilitates the efficient retrieval of information on Earth observation satellites and sensors.The downloadable files in RDF Turtle format are located in the data directory and contain a total of 132,681 statements:- GEOSatDB_ontology.ttl: Ontology modeling of concepts, relations, and properties.- satellite.ttl: 2,453 Earth observation satellites and their associated entities.- sensor.ttl: 1,035 Earth observation sensors and their associated entities.- sensor2satellite.ttl: relations between Earth observation satellites and sensors.GEOSatDB undergoes quarterly updates, involving the addition of new satellites and sensors, revisions based on expert feedback, and the implementation of additional enhancements.

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"

-This dataset is replaced by a new version, see below.-Land use plays an important role in the climate system (Feddema et al., 2005). Many ecosystem processes are directly or indirectly climate driven, and together with human driven land use changes, they determine how the land surface will evolve through time. To assess the effects of land cover changes on the climate system, models are required which are capable of simulating interactions between the involved components of the Earth system (land, atmosphere, ocean, and carbon cycle). Since driving forces for global environmental change differ among regions, a geographically (spatially) explicit modeling approach is called for, so that it can be incorporated in global and regional (climate and/or biophysical) change models in order to enhance our understanding of the underlying processes and thus improving future projections.Integrated records of the co-evolving human-environment system over millennia are needed to provide a basis for a deeper understanding of the present and for forecasting the future. This requires the major task of assembling and integrating regional and global historical, archaeological, and paleo-environmental records. Humans cannot predict the future. But, if we can adequately understand the past, we can use that understanding to influence our decisions and to create a better, more sustainable and desirable future.Some researchers suggest that mankind has shifted from living in the Holocene (~emergence of agriculture) into the Anthropocene (~humans capable of changing the Earth’ atmosphere) since the start of the Industrial Revolution. But in the light of the sheer size and magnitude of some historical land use changes (e.g. collapse of the Roman Empire in the 4th century, the depopulation of Europe due to the Black Plague in the 14th century and the aftermath of the colonization of the Americas in the 16th century), some believe that this point might have occurred earlier in time (Ruddiman, 2003; Kaplan et al., 2010). Many uncertainties still remain today and gaps in our knowledge of the Antiquity and its aftermath can only be improved by interdisciplinary research.HYDE presents (gridded) time series of population and land use for the last 12,000 years. It is an update (v 3.2) of the History Database of the Global Environment (HYDE) from Klein Goldewijk et al. (2011, 2013) with new quantitative estimates of the underlying demographic and agricultural developments for the Holocene. Date: 2016-11-28 The datasets consist of different time steps for each period: 10k BCE - 1 CE: 1000 yr, 1 - 1700 CE: 100 yr, 1700 - 2000 CE: 10 yr, 2000 - 2015 CE: 1 yr.A previous version has been made available from May until November 2016. This dataset was deposited and published in November 2016 in order to fix an incompleteness in the data.A newer version was deposited and published in September 2017.See the relations for the new version. The data is offered in ZIPfiles. Be aware that when extracted the total size of the dataset is over 200 GB.

The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987.

WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields:

AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection

ACPD comes as an Arc/Info EXPORT file originally called "ACPD.E00" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command.

References to the WCPD data set can be found in:

• SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK.
• D. Rhind. "Cartographically-related research at Birkbeck College 1987-91" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66.

The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK.

The Australian Antarctic Data Centre's Casey Station GIS data were originally mapped from Aerial photography (January 4 1994). Refer to the metadata record 'Casey Station GIS Dataset'. Since then various features have been added to these data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, the locations of other features have been obtained from a variety of sources. The data are included in the data available for download from the provided URLs. The data conforms to the SCAR Feature Catalogue which includes data quality information. See the provided URL. Data described by this metadata record has Dataset_id = 17. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

The Macquarie Island Station Area GIS Dataset is a topographic and facilities data base covering Australia's Macquarie Island Station and its immediate environs. The database includes all man made and natural features within the operational area of the station proper. Attributes are held for many facilities including, buildings, site services, communications, fuel storage, aeronautical and management zones. The spatial data have been compiled from low level aerial photography, ground surveys and engineering plans. Detail attribution of hydraulic site services includes make, size and engineering plan number.

The dataset conforms to the SCAR Feature Catalogue which includes data quality information.

The data is included in the data available for download from a Related URL below. The data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 25. Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.

Changes have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added. As a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).

Where habitats for people are disappearing due to climate change: Where habitability is lost due to climate change. Humans have thrived under the relatively stable conditions of the Earth system during the Holocene. However, the living conditions on currently inhabited land will dramatically deteriorate in many places due to anthropogenic climate change. Here we provide an outlook on two impacts of a changing climate - sea level rise and an increase in extreme temperatures – that could render large regions effectively inhabitable.

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

Description of Earthquakes Dataset (1990-2023)

The earthquakes dataset is an extensive collection of data containing information about all the earthquakes recorded worldwide from 1990 to 2023. The dataset comprises approximately three million rows, with each row representing a specific earthquake event. Each entry in the dataset contains a set of relevant attributes related to the earthquake, such as the date and time of the event, the geographical location (latitude and longitude), the magnitude of the earthquake, the depth of the epicenter, the type of magnitude used for measurement, the affected region, and other pertinent information.

Features - time in millisecconds - place - status
- tsunami (boolean value) - significance - data_type - magnitudo - state - longitude - latitude
- depth - date

Importance and Utility of the Dataset:

Earthquake Analysis and Prediction: The dataset provides a valuable data source for scientists and researchers interested in analyzing spatial and temporal distribution patterns of earthquakes. By studying historical data, trends, and patterns, it becomes possible to identify high-risk seismic zones and develop predictive models to forecast future seismic events more accurately.

Safety and Prevention: Understanding factors contributing to earthquake frequency and severity can assist authorities and safety experts in implementing preventive measures at both local and global levels. These data can enhance the design and construction of earthquake-resistant infrastructures, reducing material damage and safeguarding human lives.

Seismological Science: The dataset offers a critical resource for seismologists and geologists studying the dynamics of the Earth's crust and various geological faults. Analyzing details of recorded earthquakes allows for a deeper comprehension of geological processes leading to seismic activity.

Study of Tectonic Movements: The dataset can be utilized to analyze patterns of tectonic movements in specific areas over the years. This may help identify seasonal or long-term seismic activity, providing additional insights into plate tectonic behavior.

Public Information and Awareness: Earthquake data can be made accessible to the public through portals and applications, enabling individuals to monitor seismic activity in their regions of interest and promoting awareness and preparedness for earthquakes.

In summary, the earthquakes dataset represents a fundamental information source for scientific research, public safety, and community awareness. By analyzing historical data and building predictive models, this dataset can significantly contribute to mitigating seismic risks and protecting people and infrastructure from the consequences of earthquakes.

The Australian Antarctic Data Centre's Mawson Station GIS data were originally mapped from March 1996 aerial photography. Refer to the metadata record 'Mawson Station GIS Dataset'. Since then various features have been added to this data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, other features have been 'eyed in' as more accurate data were not available. The eyeing in has been done based on advice from Australian Antarctic Division staff and using as a guide sources such as an aerial photograph, an Engineering plan, a map or a sketch. GPS data or measurements using a measuring tape may also have been used.

The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 119. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

This element involves the development of software that enables easier commanding of a wide range of NASA relevant robots through the Robot Application Programming Interface Delegate (RAPID) robot messaging system and infusing the developed software into flight projects.  In June and July of 2013, RAPID was tested on ISS as the robot messaging software for the Technology Demonstration Mission (TDM) Human Exploration Telerobotics (HET) Surface Telerobotics experiment.  RAPID has also been made available to — and integrated with — the Robot Operating System (ROS), a popular software framework for developing state-of-the-art robots for ground and space. While ROS powers a number of new robots and components such as Robonaut 2’s climbing legs and R5, the addition of RAPID allows these robots to interoperate in collaborative human-robot teams, safely and effectively over time-delayed communications links. The objective this year is to take this space-tested software and extend it to providing video streaming from remote robots and delivering this new capability to the Exploration Ground Data Systems (xGDS) area within HRS.  xGDS will then deliver its software to Science Mission Directorate (SMD) funded field tests to improve the technology readiness moving leading (potentially) to being used for the Lunar Prospector Mission ground data systems.  Success will involve delivering RAPID to xGDS and then xGDS supporting SMD field test.

The team is also developing algorithms for sensors capable of reconstructing remote worlds and efficiently shipping that remote environment back to earth using the RAPID robot messaging system.  This type of system could eventually lead to scientists on earth gain new insights as they are able to step into the remote world.  This sensor also has the ability to engage the public, bringing remote worlds back to earth.  During FY13, this task used science operations personnel from current SMD projects to objectively measure improvement in remote science target selection and decision-making based. The team continues to work with SMD projects to ensure that the technologies being developed are directly responsive to SMD project personnel needs. The objective of this work in FY14 is to expand the range of science operations tasks addressed by the technology, and to perform laboratory demonstrations for JPL/SMD stakeholders of the immersive visualization of data from a sensor using an SMD representative environment.

During 2014, the “Controlling Robots Over Time Delay” project element will develop two technologies:

• Develop RAPID robot messaging for unified cross-center operations platform for TDM, xGDS, and CCSDS
• Sensor Systems for the Construction of Immersive Virtual Environments

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.

This dataset represents topographic features and facilities at Mawson and its immediate environs. Feature types include buildings, masts, tanks, roads, coastline and contours (1 metre interval). The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which include data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 111. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

Changes have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added. As a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).

The Casey Station dataset represents man-made facilities around Australia's Casey Station and its immediate environs. Detailed attributes are held for the data including buildings, site services, communications, fuel storage. The spatial data have been compiled from low level aerial photography, ground surveys and engineering plans.

Detail attribution of site services includes make, size and engineering plan number.

Topographic data for Casey is part of the Windmill Islands 1:50000 Topographic Dataset (see Related URL). This data is described by the metadata record 'Windmill Islands 1:50000 Topographic GIS Dataset', Entry ID: Wind50k.

Changes have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added. As a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).

Land use plays an important role in the climate system (Feddema et al., 2005). Many ecosystem processes are directly or indirectly climate driven, and together with human driven land use changes, they determine how the land surface will evolve through time. To assess the effects of land cover changes on the climate system, models are required which are capable of simulating interactions between the involved components of the Earth system (land, atmosphere, ocean, and carbon cycle). Since driving forces for global environmental change differ among regions, a geographically (spatially) explicit modeling approach is called for, so that it can be incorporated in global and regional (climate and/or biophysical) change models in order to enhance our understanding of the underlying processes and thus improving future projections.Integrated records of the co-evolving human-environment system over millennia are needed to provide a basis for a deeper understanding of the present and for forecasting the future. This requires the major task of assembling and integrating regional and global historical, archaeological, and paleo-environmental records. Humans cannot predict the future. But, if we can adequately understand the past, we can use that understanding to influence our decisions and to create a better, more sustainable and desirable future.Some researchers suggest that mankind has shifted from living in the Holocene (~emergence of agriculture) into the Anthropocene (~humans capable of changing the Earth’ atmosphere) since the start of the Industrial Revolution. But in the light of the sheer size and magnitude of some historical land use changes (e.g. collapse of the Roman Empire in the 4th century, the depopulation of Europe due to the Black Plague in the 14th century and the aftermath of the colonization of the Americas in the 16th century), some believe that this point might have occurred earlier in time (Ruddiman, 2003; Kaplan et al., 2010). Many uncertainties still remain today and gaps in our knowledge of the Antiquity and its aftermath can only be improved by interdisciplinary research.HYDE presents (gridded) time series of population and land use for the last 12,000 years. It is an update (v 3.2) of the History Database of the Global Environment (HYDE) from Klein Goldewijk et al. (2011, 2013) with new quantitative estimates of the underlying demographic and agricultural developments for the Holocene.

The purpose of this project is to extend current ground-based Human Reliability Analysis (HRA) techniques to a long-duration, space-based tool to more effectively predict the risk associated with human actions on long-duration missions.  By doing so, the agency will be able to focus resources on the risk drivers, such as specific training, conditioning, procedures, exercising, etc. for these future missions.  NASA uses Probabilistic Risk Assessments (PRAs) to assess the probability of Loss of Crew (LOC) and Loss of Mission (LOM).  PRAs take into account multiple contributing factors and their interactions, such as how the crew, software, and hardware work together to achieve mission objectives.  HRA is used to assess the human contribution to risk in PRAs.  Current HRA techniques were developed for ground applications using Earth based human reliability data to estimate human error probability.  These ground-based HRA techniques have been shown to be a reasonable tool for short-duration space missions, such as Space Shuttle and lunar fly-bys.  However, longer-duration beyond Earth orbit missions, such as asteroid and Mars missions, will require crews to be in space for 400 to 900 days with periods of extended autonomy and self-sufficiency.  Current indications show higher risk due to fatigue, physiological effects due to extended low gravity environments, and others, which may impact HRA predictions by affecting the crew’s cognitive abilities, as well as their physiology, and yield a higher probability for LOC and LOM (e.g. early return).  PRAs will need to account for these effects in order to provide management, designers, and the crew our best estimate of risk.

With the funding of this IR&D project over the next three years, Safety & Mission Assurance (S&MA) will collaborate with Human Health & Performance (HH&P) to establish what is currently used to assess human reliability for human space programs, identify human performance factors that may be sensitive to long duration space flight, collect available historical data, and update current tools to account for performance shaping factors believed to be important to such missions.  JSC’s Human System Integration (HSI) initiative is a work in progress to better understand how the crew, software, and hardware work together and ensure that HSI is accounted for in future space mission designs.

Microbe organisms make up approximately 60% of the earth’s living matter and the human body is home to millions of microbe organisms. Microbes are microbial threats to health and may lead to several diseases in humans like toxoplasmosis and malaria. The microbiological toxoplasmosis disease in humans is widespread, with a seroprevalence of 3.6-84% in sub-Saharan Africa. This necessitates an automated approach for microbe organisms detection. The primary objective of this study is to predict microbe organisms in the human body. A novel hybrid microbes classifier (HMC) is proposed in this study which is based on a decision tree classifier and extra tree classifier using voting criteria. Experiments involve different machine learning and deep learning models for detecting ten different living microforms of life. Results suggest that the proposed HMC approach achieves a 98% accuracy score, 98% geometric mean score, 97% precision score, and 97% Cohen Kappa score. The proposed model outperforms employed models, as well as, existing state-of-the-art models. Moreover, the k-fold cross-validation corroborates the results as well. The research helps microbiologists identify the type of microbe organisms with high accuracy and prevents many diseases through early detection.