The shapefile depicts the distribution of planted forests in East Asia (China, Japan, ROK, and DPRK) and associated dominant tree species to the genus level. The dataset is in a shapefile where each polygon is 0.009° by 0.009° (approximately 1km2) in size within the forested area of 2020 (5m or greater in tree height) based on the FAO’s definition of “forest.”

For each polygon, attributes include information on planted forest, dominant tree species, and geospatial entity as follow: ID: Polygon ID Biome: Biome classes used in the study Country: Country Prc_Pln: Percent planted forest predicted by the three models (upper bound, midpoint, and lower bound). The values are means of the three models, which is the main result of our study. NA for ROK and majority of areas in Japan, where national planted forest maps were used as a final planted/natural label (see References). Prc_P_U: Percent planted forest predicted by the upper bound model. NA for ROK and majority of areas in Japan, where national planted forest maps were used as a final planted/natural label. Note that values are not always higher than Prc_Pln. Prc_P_L: Percent planted forest predicted by the lower bound model. NA for ROK and majority of areas in Japan, where national planted forest maps were used as a final planted/natural label. Note that values are not always lower than Prc_Pln. Type: “Planted” or “Natural” forests based on the main result. For our predicted percent planted forest, “Planted” if Prc_Pln is 0.5 or greater and “Natural” if Prc_Pln < 0.5. For Prc_Pln = NA, national planted forest maps were used to determine if the given polygon is a planted forest, and if not, “Natural.” Typ_Upp: “Planted” or “Natural” forests based on the upper bound model. For our predicted percent planted forest, “Planted” if Prc_P_U is 0.5 or greater and “Natural” if Prc_P_Upp < 0.5. For Prc_P_Upp = NA, national planted forest maps were used to determine if the given polygon is a planted forest, and if not, “Natural.” Typ_Lwr: “Planted” or “Natural” forests based on the lower bound model. For our predicted percent planted forest, “Planted” if Prc_P_L is 0.5 or greater and “Natural” if Prc_P_L < 0.5. For Prc_P_L = NA, national planted forest maps were used to determine if the given polygon is a planted forest, and if not, “Natural.” Genus: For Type = “Planted”, this attribute indicates the predicted dominant genus. NA for Type = “Natural”. Gns_Upp: For Typ_Upp = “Planted”, this attribute indicates the predicted dominant genus. NA for Typ_Upp = “Natural”. Gns_Lwr: For Typ_Lwr = “Planted”, this attribute indicates the predicted dominant genus. NA for Typ_Lwr = “Natural”. Besnard_Yr: Estimated planted year based on Besnard et al. (2021) by overlay. Du_Yr: Estimated planted year based on Du et al. (2022) by overlay. Area_m2: Polygon area in square meters.

Planted forest in this map includes forests planted for restoration purposes, commercial plantation, and other artificial planting for other purposes, such as for landscape and disaster prevention, of all ages.

Raster files are available for percent planted forest, type, and dominant genus, where 1 = "Planted" and 0 = "Natural" for the type. Values are 99 for natural forests for percent planted forest and dominant genus. Numbers for dominant genera are as follows: 1 = Abies 2 = Acer 3 = Alnus 4 = Betula 5 = Carpinus 6 = Castanea 7 = Castanopsis 8 = Chamaecyparis 9 = Cryptomeria 10 = Cunninghamia 11 = Eucalyptus 12 = Fagus 13 = Ilex 14 = Larix 15 = Picea 16 = Pinus 17 = Quercus 18 = Robinia 19 = Tilia

References -Biodiversity Center of Japan. Vegetation Survey (7) https://www.biodic.go.jp/moni1000/findings/data/index_file.html (2021). -Kim, K.-M., Kim, C.-M. & Jun, E. J. Study on the standard for 1:25,000 scale digital forest type map production in Korea. J. Korean Assoc. Geograp. Infor. Stud 12, 143-151 (2009). -Besnard, S. et al. Mapping global forest age from forest inventories, biomass and climate data. Earth Syst. Sci. Data, 13, 4881-4896 (2021). -Du, Z. et al. A global map of planting years of plantations v2. figshare https://doi.org/10.6084/m9.figshare.19070084.v2 (2022).

Maps with wind speed, wind rose and wind power density potential in Japan. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

Map georeferenced using ArcGIS Pro using 1st order polynomial (affine) transformation) with 6 control points and a total RMSE of 533 meters. JPEG of Northwest Kyushu was downloaded from Berkley Library's exhibit on Office of Strategic Services map.Created by Brittany Waltemate, former Maps and Geospatial Assistant

This dataset contains a list of visa-free countries for travelers with citizenship of Japan, as well as the maximum number of days they can stay without a visa.

As of November 2020, Japan had mapped nearly 98 percent of it's exclusive economic zone (EEZ). An EEZ is the sea zone stretching 200 nautical miles (nmi) from the coast of a state. The Seabed 2030 project aims to map the world's ocean floor by the year 2030 using crowdsource datasets.

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Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app

Globally available, ON-DEMAND noise pollution maps generated from real-world measurements (our sample dataset) and AI interpolation. Unlike any other available noise-level data sets! GIS-ready, high-resolution visuals for real estate platforms, government dashboards, and smart city applications.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

GCOM-C/SGLI L3 Map Land surface temperature (LST) (1-Day,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. This dataset is daily map-projected statistics product. This dataset includes LST: Land surface temperature and QA_flag. The physical quantity unit is Kelvin. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.

The Global Mangrove Watch (GMW) is a collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA).

The GMW aims to provide geospatial information about mangrove extent and changes to the Ramsar Convention, national wetland practitioners, decision makers and NGOs. It is part of the Ramsar Science and Technical Review Panel (STRP) work plan for 2016-2018 and a Pilot Project to the Ramsar Global Wetlands Observation System (GWOS), which is implemented under the GEO-Wetlands Initiative. The primary objective of the GMW has been to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation.

The GMW has generated a global baseline map of mangroves for 2010 using ALOS PALSAR and Landsat (optical) data, and changes from this baseline for seven epochs between 1996 and 2017 derived from JERS-1, ALOS and ALOS-2. Annual maps are planned from 2018 and onwards.

Citations: Bunting P., Rosenqvist A., Lucas R., Rebelo L-M., Hilarides L., Thomas N., Hardy A., Itoh T., Shimada M. and Finlayson C.M. (2018). The Global Mangrove Watch – a New 2010 Global Baseline of Mangrove Extent. Remote Sensing 10(10): 1669. doi: 10.3390/rs1010669. Other cited references: Thomas N, Lucas R, Bunting P, Hardy A, Rosenqvist A, Simard M. (2017). Distribution and drivers of global mangrove forest change,

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HD Map For Autonomous Vehicles Market Size 2024-2028

The HD map for autonomous vehicles market size is forecast to increase by USD 14 billion at a CAGR of 40.5% between 2023 and 2029

The market is experiencing significant growth, driven by the increasing adoption of autonomous vehicles and the development of advanced connected infrastructure. The integration of high-definition maps into autonomous systems enables vehicles to navigate complex environments more accurately and efficiently, reducing the risk of accidents and improving overall performance. HD map creation for autonomous vehicles is a complex process involving data acquisition, aggregation, and integration of advanced technologies such as AI and machine learning. However, the high cost associated with the technology remains a significant challenge for market expansion. Manufacturers must continue to innovate and find cost-effective solutions to make HD maps an essential component of autonomous vehicles, rather than a luxury. Companies seeking to capitalize on this market opportunity should focus on collaborating with infrastructure providers, developing scalable and cost-effective HD mapping technologies, and ensuring seamless integration with autonomous systems. By addressing these challenges and leveraging the growing demand for autonomous vehicles and advanced infrastructure, market participants can effectively navigate the strategic landscape and drive long-term success.

What will be the Size of the Market during the forecast period?

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The market is experiencing significant growth as the global push towards advanced driver-assistance systems (ADAS) and fully autonomous vehicles (AVs) continues. HD Maps, which utilize technologies such as Lidar, SLAM (Simultaneous Localization and Mapping), and digital cameras, play a crucial role in enabling AVs to navigate roads safely and efficiently. These maps provide real-time, high-precision data to AV systems, allowing them to identify and respond to road conditions, obstacles, and other vehicles in real time. The market is expected to reach a substantial size in the coming years, driven by the increasing demand for shared mobility services, including ride-sharing and robo-taxi services.
The integration of 5G networks is also expected to accelerate the adoption of HD Maps, as they enable faster and more reliable data transmission between vehicles and maps. The market is witnessing continuous innovation, with companies investing heavily in research and development to improve the accuracy and coverage of HD Maps. Additionally, the integration of HD Maps with other technologies, such as sensor fusion and deep learning algorithms, is expected to further enhance the capabilities of AVs. Overall, the HD Map market for autonomous vehicles is a dynamic and rapidly evolving market, poised for significant growth in the coming years.

How is this Industry segmented?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Solution

  Cloud-based
  Embedded


Vehicle Type

  Passenger
  Commercial


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Solution Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. HD maps are a critical component in the advancement of autonomous vehicles. These high-definition maps offer enhanced accuracy and precision for navigation, while their cloud-based infrastructure ensures accessibility and ease of updates. This enables autonomous vehicles to navigate complex and unfamiliar environments more effectively. Notable industry players, such as NavInfo Co. Ltd. (Navinfo), HERE Global BV (HERE), TomTom NV (TomTom), and NVIDIA Corp. (NVIDIA), prioritize cloud-based solutions and real-time services for their HD mapping offerings. The integration of 5G networks further enhances the capabilities of HD maps, contributing to the growth of autonomous driving technology in passenger and commercial vehicles.

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The cloud-based segment was valued at USD 1047.3 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 40% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The market in North America is primarily driven by the United States, where the increasing deployment of

GCOM-C/SGLI L3 Map Cloud top height (1-Month,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. This dataset is 1 month map-projected statistics product. This dataset includes cloud top height. The physical quantity unit is km. The physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.

Y731 (1: 50 000 scale) Topographic Maps represents the main 1: 50 000 scale mapping covering large parts of Kenya. The maps illustrate the key topographic features both natural and man made. There have been multiple versions of the maps published. Not all versions of the maps are held by the Geodata Centre. Those which are currently held (November 2018) are listed. Publishers OSD Government of the United Kingdom (Crown Copyright); OSD(K) Government of the United Kingdom for the Government of Kenya; OSD(T) Government of the United Kingdom for the Government of Tanzania; OSD(U) Government of the United Kingdom; USD Department of Land and Surveys Uganda; ING French National Geographic Institute for the Government of Kenya; JICA Japan International Co-operation Agency for the Government of Kenya.

This map shows the extents of the various datasets comprising the World Elevation dynamic (Terrain, TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.The map has pop-ups defined. Click anywhere on the map to reveal details about the data sources.Topography sources listed in the table below are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only. Data Source Native Pixel Size Approximate Pixel Size (meters) Coverage Primary Source Country/Region

Topography

Australia 1m 1 meter 1 Partial areas of Australia Geoscience Australia Australia

Moreton Bay, Australia 1m 1 meter 1 Moreton Bay region, Australia Moreton Bay Regional Council Australia

New South Wales, Australia 5m 5 meters 5 New South Wales State, Australia DFSI Australia

SRTM 1 arc second DEM-S 0.0002777777777779 degrees 31 Australia Geoscience Australia Australia

Burgenland 50cm 0.5 meter 0.5 Burgenland State, Austria Land Burgenland Austria

Upper Austria 50cm 0.5 meter 0.5 Upper Austria State, Austria Land Oberosterreich Austria

Austria 1m 1 meter 1 Austria BEV Austria

Austria 10m 10 meters 10 Austria Geoland Austria

Canada HRDEM 1m 1 meter 1 Partial areas of the southern part of Canada Natural Resources Canada Canada

Canada HRDEM 2m 2 meters 2 Partial areas of the southern part of Canada Natural Resources Canada Canada

Denmark 40cm 0.4 meter 0.4 Denmark SDFE Denmark

Denmark 10m 10 meters 10 Denmark SDFE Denmark

England 2m 2 meters 2 70 % of England Environment Agency England

Estonia 1m 1 meter 1 Estonia Estonian Land Board Estonia

Estonia 5m 5 meters 5 Estonia Estonian Land Board Estonia

Estonia 10m 10 meters 10 Estonia Estonian Land Board Estonia

Finland 2m 2 meters 2 Finland NLS Finland

Finland 10m 10 meters 10 Finland NLS Finland

Berlin 1m 1 meter 1 Berlin State, Germany Geoportal Berlin Germany

Hamburg 1m 1 meter 1 Hamburg State, Germany LGV Hamburg Germany

Nordrhein-Westfalen 1m 1 meter 1 Nordrhein-Westfalen State, Germany Land NRW Germany

Sachsen-Anhalt 2m 2 meters 2 Sachsen-Anhalt State, Germany LVermGeo LSA Germany

Hong Kong 50cm 0.5 meter 0.5 Hong Kong CEDD Hong Kong SAR

Italy TINITALY 10m 10 meters 10 Italy INGV Italy

Japan DEM5A *, DEM5B * 0.000055555555 degrees 5 Partial areas of Japan GSI Japan

Japan DEM10B * 0.00011111111 degrees 10 Japan GSI Japan

Latvia 1m 1 meter 1 Latvia Latvian Geospatial Information Agency Latvia

Latvia 10m 10 meters 10 Latvia Latvian Geospatial Information Agency Latvia

Latvia 20m 20 meters 20 Latvia Latvian Geospatial Information Agency Latvia

Lithuania 1m 1 meter 1 Lithuania NZT Lithuania

Lithuania 10m 10 meters 10 Lithuania NZT Lithuania

Netherlands (AHN3/AHN4) 50cm 0.5 meter 0.5 Netherlands AHN Netherlands

Netherlands (AHN3/AHN4) 10m 10 meters 10 Netherlands AHN Netherlands

New Zealand 1m 1 meter 1 Partial areas of New Zealand Land Infromation New Zealand (Sourced from LINZ. CC BY 4.0) New Zealand

Northern Ireland 10m 10 meters 10 Northern Ireland OSNI Northern Ireland

Norway 10m 10 meters 10 Norway NMA Norway

Poland 1m 1 meter 1 Partial areas of Poland GUGIK Poland

Poland 5m 5 meters 5 Partial areas of Poland GUGIK Poland

Scotland 1m 1 meter 1 Partial areas of Scotland Scottish Government et.al Scotland

Slovakia 10m 10 meters 10 Slovakia GKÚ Slovakia

Slovenia 1m 1 meter 1 Slovenia ARSO Slovenia

Madrid City 1m 1 meter 1 Madrid city, Spain Ayuntamiento de Madrid Spain

Spain 2m (MDT02 2019 CC-BY 4.0 scne.es) 2 meters 2 Partial areas of Spain IGN Spain

Spain 5m 5 meters 5 Spain IGN Spain

Spain 10m 10 meters 10 Spain IGN Spain

Varnamo 50cm 0.5 meter 0.5 Varnamo municipality, Sweden Värnamo Kommun Sweden

Canton of Basel-Landschaft 25cm 0.25 meter 0.25 Canton of Basel-Landschaft, Switzerland Geoinformation Kanton Basel-Landschaft Switzerland

Grand Geneva 50cm 0.5 meter 0.5 Grand Geneva metropolitan, France/Switzerland SITG Switzerland and France

Switzerland swissALTI3D 50cm 0.5 meter 0.5 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein

Switzerland swissALTI3D 10m 10 meters 10 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein

OS Terrain 50 50 meters 50 United Kingdom Ordnance Survey United Kingdom

3DEP 1m 1 meter 1 Partial areas of the conterminous United States, Puerto Rico USGS United States

NRCS 1m 1 meter 1 Partial areas of the conterminous United States NRCS USDA United States

FEMA LiDAR DTM 3 meters 3 Partial areas of the conterminous United States FEMA United States

NED 1/9 arc second 0.000030864197530866 degrees 3 Partial areas of the conterminous United States USGS United States

3DEP 5m 5 meters 5 Alaska, United States USGS United States

NED 1/3 arc second 0.000092592592593 degrees 10 conterminous United States, Hawaii, Alaska, Puerto Rico, and Territorial Islands of the United States USGS United States

NED 1 arc second 0.0002777777777779 degrees 31 conterminous United States, Hawaii, Alaska, Puerto Rico, Territorial Islands of the United States; Canada and Mexico USGS United States

NED 2 arc second 0.000555555555556 degrees 62 Alaska, United States USGS United States

Wales 2m 2 meters 2 70 % of Wales Natural Resources Wales Wales

WorldDEM4Ortho 0.00022222222 degrees 24 Global (excluding the countries of Azerbaijan, DR Congo and Ukraine) Airbus Defense and Space GmbH World

SRTM 1 arc second 0.0002777777777779 degrees 31 all land areas between 60 degrees north and 56 degrees south except Australia NASA World

EarthEnv-DEM90 0.00083333333333333 degrees 93 Global N Robinson,NCEAS World

SRTM v4.1 0.00083333333333333 degrees 93 all land areas between 60 degrees north and 56 degrees south except Australia CGIAR-CSI World

GMTED2010 7.5 arc second 0.00208333333333333 degrees 232 Global USGS World

GMTED2010 15 arc second 0.00416666666666666 degrees 464 Global USGS World

GMTED2010 30 arc second 0.0083333333333333 degrees 928 Global USGS World

Bathymetry

Canada west coast 10 meters 10 Canada west coast Natural Resources Canada Canada

Gulf of Mexico 40 feet 12 Northern Gulf of Mexico BOEM Gulf of Mexico

MH370 150 meters 150 MH370 flight search area (Phase 1) of Indian Ocean Geoscience Australia Indian Ocean

Switzerland swissBATHY3D 1 - 3 meters 1, 2, 3 Lakes of Switzerland swisstopo Switzerland

NCEI 1/9 arc second 0.000030864197530866 degrees 3 Puerto Rico, U.S Virgin Islands and partial areas of eastern and western United States coast NOAA NCEI United States

NCEI 1/3 arc second 0.000092592592593 degrees 10 Partial areas of eastern and western United States coast NOAA NCEI United States

CRM 1 arc second (Version 2) 0.0002777777777779 degrees 31 Southern California coast of United States NOAA United States

NCEI 1 arc second 0.0002777777777779 degrees 31 Partial areas of northeastern United States coast NOAA NCEI United States

CRM 3 arc second 0.00083333333333333 degrees 93 United States Coast NOAA United States

NCEI 3 arc second 0.00083333333333333 degrees 93 Partial areas of northeastern United States coast NOAA NCEI United States

USGS CoNED 1 - 3 meters 1, 2, 3 Partial coastal areas of eastern and western United States USGS United States

GEBCO 2021 ** 0.00416666666666666 degrees 464 Global GEBCO World

GEBCO 2014 0.0083333333333333 degrees 928 Global GEBCO World * Fundamental Geospatial Data provided by GSI with Approval Number JYOU-SHI No.1239 2016. ** GEBCO Compilation Group (2021) GEBCO 2021 Grid (doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f) *** Bathymetry datasets are part of TopoBathy and TopoBathy3D services only.Disclaimer: Data sources are not to be used for navigation/safety at sea and in air.

ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed "Midori II" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is "Version 2".

Y731 (1: 50 000 scale) Topographic Maps represents the main 1: 50 000 scale mapping covering large parts of Kenya. The maps illustrate the key topographic features both natural and man made. There have been multiple versions of the maps published. Not all versions of the maps are held by the Geodata Centre. Those which are currently held (November 2018) are listed. Publishers OSD Government of the United Kingdom (Crown Copyright); OSD(K) Government of the United Kingdom for the Government of Kenya; OSD(T) Government of the United Kingdom for the Government of Tanzania; OSD(U) Government of the United Kingdom; USD Department of Land and Surveys Uganda; ING French National Geographic Institute for the Government of Kenya; JICA Japan International Co-operation Agency for the Government of Kenya.

GCOM-C/SGLI L3 Map Water cloud effective radius (8-Days,1/12 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. This dataset is 8 days map-projected statistics product. This dataset includes water cloud effective radius. The physical quantity unit is micro meter. The stored statistics values are average (AVE) and quality flag (QA_flag). The provided format is HDF5. The Spatial resolution is 1/12 degree. The statistical period is 8 days, also 1 day and 1 month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.

Y731 (1: 50 000 scale) Topographic Maps represents the main 1: 50 000 scale mapping covering large parts of Kenya. The maps illustrate the key topographic features both natural and man made. There have been multiple versions of the maps published. Not all versions of the maps are held by the Geodata Centre. Those which are currently held (November 2018) are listed. Publishers OSD Government of the United Kingdom (Crown Copyright); OSD(K) Government of the United Kingdom for the Government of Kenya; OSD(T) Government of the United Kingdom for the Government of Tanzania; OSD(U) Government of the United Kingdom; USD Department of Land and Surveys Uganda; ING French National Geographic Institute for the Government of Kenya; JICA Japan International Co-operation Agency for the Government of Kenya.

Y731 (1: 50 000 scale) Topographic Maps represents the main 1: 50 000 scale mapping covering large parts of Kenya. The maps illustrate the key topographic features both natural and man made. There have been multiple versions of the maps published. Not all versions of the maps are held by the Geodata Centre. Those which are currently held (November 2018) are listed. Publishers OSD Government of the United Kingdom (Crown Copyright); OSD(K) Government of the United Kingdom for the Government of Kenya; OSD(T) Government of the United Kingdom for the Government of Tanzania; OSD(U) Government of the United Kingdom; USD Department of Land and Surveys Uganda; ING French National Geographic Institute for the Government of Kenya; JICA Japan International Co-operation Agency for the Government of Kenya.

GCOM-C/SGLI L3 Map Vegetation Roughness Index (VRI) (1-Month,1/24 deg) is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. Level 3 products are defined to be products derived from Level 1B and Level 2 products by statistically processing the Level 1B and Level 2 products in time and space domains. This dataset is 1 month map-projected statistics product. This dataset includes VRI: Vegetation Roughness Index and QA_flag. Physical quantity unit is dimensionless. The stored statistics values are average (AVE) and quality flag (QA_flag). The statistical period is 1 month, also 1 day and 8 days statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is Version 3. The Version 2 is also available.