8 datasets found

  1. The Global Digital Mapping Cameras market size was USD 248.2 Million in...

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
    Updated Mar 13, 2024
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    Cognitive Market Research (2024). The Global Digital Mapping Cameras market size was USD 248.2 Million in 2023! [Dataset]. https://www.cognitivemarketresearch.com/digital-mapping-cameras-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Mar 13, 2024
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    According to Cognitive Market Research, The Global Digital Mapping Cameras market size is USD 248.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 2.50% from 2023 to 2030.

    North America Digital Mapping Cameras held the major market of more than 40% of the global revenue with market size of USD 0.10 million in 2023 and will grow at a compound yearly growth rate (CAGR) of 0.7% from 2023 to 2030.
Europe Digital Mapping Cameras accounted for a share of over 30% of the global market size of USD 0.07 million in 2023.
Asia Pacific Digital Mapping Cameras held the market of more than 23% of the global revenue with a market size of USD 0.06 million in 2023 and will rise at a compound yearly growth rate (CAGR) of 4.5% from 2023 to 2030.
South America Digital Mapping Cameras market has more than 5% of the global revenue with a market size of USD 0.01 million in 2023 and will rise at a compound yearly growth rate (CAGR) of 1.9% from 2023 to 2030.
Middle East and Africa Digital Mapping Cameras held the major market of more than 2% of the global revenue with market size of USD XX million in 2023 and will rise at a compound yearly growth rate (CAGR) of 2.2% from 2023 to 2030.
The demand for Digital Mapping cameras is rising due to the escalating demand for accurate and high-resolution geospatial.
Demand for manned aircraft remains higher in the Digital Mapping Cameras market.
The area arrays category held the highest Digital Mapping Cameras market revenue share in 2023.

    Escalating Demand for Accurate and High-Resolution Geospatial to Provide Viable Market Output

    The surging demand for accurate and high-resolution geospatial data across different industries, including agriculture, urban planning, and environmental monitoring, is driving market growth. The rising adoption of advanced mapping technologies for applications like smart cities, autonomous vehicles, and infrastructure development is boosting the need for sophisticated digital mapping cameras. Furthermore, the integration of AI and ML in digital mapping processes is improving the capabilities of these cameras, driving market expansion.

    For instance, in September 2023, Microsoft and Bentley Systems announced a partnership to develop digital twins of infrastructure projects. This partnership will leverage the Azure cloud platform of Microsoft and expertise in infrastructure modeling of Bentley Systems to generate more precise and useful digital twins.

    (Source: www.microsoft.com/en-in/about/)

    Advancements in the Augmented Reality (AR) and Virtual Reality (VR) Technologies to Propel Market Growth

    Advancements in AR and VR technologies are fuelling the digital mapping cameras market by increasing the demand for high-quality and immersive spatial data. Digital mapping cameras play an important role in capturing the detailed visual information required for generating realistic AR and VR environments, improving the overall user experience. The integration of accurate geospatial data from these cameras allows more accurate alignment of virtual content with the real-world environment, expanding the applications of AR and VR across industries like navigation, gaming, and training simulations.

    For instance, in June 2023, Esri, a leading provider of geographic information systems (GIS) software, acquired CityGIS, a company specializing in 3D city modeling software, in order to allow Esri to provide a more comprehensive suite of solutions for urban planning and management.

    (Source: www.esri.com/en-us/home)

    Market Restraints of the Digital Mapping Cameras

    High Initial Costs of Acquisition and Implementation to Restrict Market Growth

    The high initial costs associated to acquiring and implementing advanced mapping camera technologies act as a significant barrier to entry for several businesses and organizations, mainly smaller ones with constrained budgets. This financial hurdle limits the broad adoption of digital mapping cameras, hindering their integration into different industries and applications. The reluctance to invest in these technologies because of their upfront expenses can impede the overall growth potential of the market.

    Impact of COVID-19 on the Digital Mapping Cameras Market

    The COVID-19 pandemic had a mixed effect on the Digital Mapping Cameras market. The global lockdowns and restrictions hampered manufacturing and disrupted supply chains, resu...

  2. w

    ROE Ecoregions Data

    • data.wu.ac.at
    • datasets.ai
    • +2more
    Updated Mar 31, 2016
    + more versions
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    U.S. Environmental Protection Agency (2016). ROE Ecoregions Data [Dataset]. https://data.wu.ac.at/odso/data_gov/ZWMyMTg1YmItZjZkNS00ZGRjLTgwNzAtNjBkMGMyOWVkZTJh
    Explore at:
    Dataset updated
    Mar 31, 2016
    Dataset provided by
    U.S. Environmental Protection Agency
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    b55d6bb1af278a0fd1cdb8c2a138e42572e44f0f
    Description

    This web service layer group contains multiple layers at various scale dependencies which enhances the cartographic display of ecoregion data. Each layer depicts ecoregion features drawn at specific scales as detailed in the layer name. IMPORTANT information regarding proper legend rendering in ArcMap: Due to the limitations of Graphical Device Interface (GDI) resources per application on Windows, ArcMap does not display the legend in the Table of Contents for the ArcGIS Server service layer if the legend has more than 100 items. As of December 2011, there are 968 unique legend items in the Level IV Ecoregion Polygon legend. Follow this link (http://support.esri.com/en/knowledgebase/techarticles/detail/33741) for instructions about how to increase the maximum number of ArcGIS Server service layer legend items allowed for display in ArcMap. Note the instructions at this link provide a slightly incorrect path to "Maximum Legend Count". The correct path is HKEY_CURRENT_USER > Software > ESRI > ArcMap > Server > MapServerLayer > Maximum Legend Count. When editing the "Maximum Legend Count", update the field, "Value data" to 1000. To download a PDF version of the Level IV ecoregion map and legend, go to ftp://ftp.epa.gov/wed/ecoregions/us/Eco_Level_IV_US_pg.pdf. Please read the remainder of this layer description for general information about Level IV Omernik Ecoregions. This layer represents Level IV Omernik Ecoregions. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. Compilation of the level IV maps, performed at 1:250,000 scale, has been a part of collaborative projects between US Environmental Protection Agency, National Health and Environmental Effects Laboratory (NHEERL)--Corvallis, OR, the US Forest Service, Natural Resources Conservation Service, and a variety of other state and federal resource agencies. The ecoregions and subregions are designed to serve as a spatial framework for environmental resource management. The most immediate needs by the states are for developing regional biological criteria and water resource standards, and for setting management goals for nonpoint-source pollution. Level IV ecoregions are intended for large geographic extents (i.e. states, multiple counties, or river basins). Use for smaller areas, such as individual counties or a 1:24,000 scale map boundary, is not recommended. Explanation of the methods used to delineate the ecoregions are given in Omernik (1995), Griffith et al. (1994), and Gallant et al. (1989). For more information about Omernik ecoregions or to download ecoregion maps and GIS data, go to: http://www.epa.gov/wed/pages/ecoregions.htm.

  3. d

    Contour Dataset of the Potentiometric Surface of Groundwater-Level Altitudes...

    • catalog.data.gov
    • data.usgs.gov
    • +2more
    Updated Jul 6, 2024
    + more versions
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    U.S. Geological Survey (2024). Contour Dataset of the Potentiometric Surface of Groundwater-Level Altitudes Near the Planned Highway 270 Bypass, East of Hot Springs, Arkansas, July-August 2017 [Dataset]. https://catalog.data.gov/dataset/contour-dataset-of-the-potentiometric-surface-of-groundwater-level-altitudes-near-the-plan
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    Dataset updated
    Jul 6, 2024
    Dataset provided by
    United States Geological Surveyhttp://www.usgs.gov/
    Area covered
    Hot Springs, Arkansas
    Description

    This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

  4. K

    Richardson, Texas US National Grid

    • koordinates.com
    csv, dwg, geodatabase +6
    Updated Aug 30, 2018
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    Richardson, Texas US National Grid [Dataset]. https://koordinates.com/layer/15610-richardson-texas-us-national-grid/
    Explore at:
    csv, mapinfo mif, geopackage / sqlite, geodatabase, shapefile, dwg, kml, pdf, mapinfo tabAvailable download formats
    Dataset updated
    Aug 30, 2018
    Dataset authored and provided by
    City of Richardson, Texas
    Area covered
    Description

    The USNG is an alpha-numeric reference system that overlays the UTM coordinate system. This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from http://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, http://www.nationalmap.gov.Fields to be considered:GZD: Grid Zone Designation -identifies the longitude zone number and the latitude band letter; SQID: 100,000 Meter Square ID -indicated the 100,000-meter square that is specific to the GZD.http://resources.arcgis.com/en/help/localgovernment/10.2./028s/other/DataDictionary.htm#FeatureClassUSNationalGridmetadata edited 08/2014

    © Center for Interdisciplinary Geospatial Information Technologies, Delta State University, Cleveland Mississippi 38733 This layer is sourced from maps.cor.gov.

    A map that tracks damage assessed parcels during or after an emergency. This map is meant to be used with the Operations Dasboard.

    © City of Richardson

  5. A

    Station ID, Air Temperature (deg F), Dew Point Temperature (deg F), Wind...

    • data.amerigeoss.org
    • hub.arcgis.com
    Updated Jul 5, 2017
    + more versions
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    NOAA GeoPlatform (2017). Station ID, Air Temperature (deg F), Dew Point Temperature (deg F), Wind Gust (kt), Mean Sea-Level Pressure (mb), 3-Hour Pressure Change (mb), Visibility (mi), Sea Surface Temperature (deg F), Significant Wave Height (ft) - Scale Band 6 [Dataset]. https://data.amerigeoss.org/dataset/ea45cecb-5da8-4d26-9245-60a8ac7b11ad
    Explore at:
    kml, geojson, arcgis geoservices rest api, zip, html, ogc wms, csvAvailable download formats
    Dataset updated
    Jul 5, 2017
    Dataset provided by
    NOAA GeoPlatform
    Description
    Last Updated: January 2015
    Map Information

    This nowCOAST time-enabled map service provides map depicting the latest surface weather and marine weather observations at observing sites using the international station model. The station model is method for representing information collected at an observing station using symbols and numbers. The station model depicts current weather conditions, cloud cover, wind speed, wind direction, visibility, air temperature, dew point temperature, sea surface water temperature, significant wave height, air pressure adjusted to mean sea level, and the change in air pressure over the last 3 hours. The circle in the model is centered over the latitude and longitude coordinates of the station. The total cloud cover is expressed as a fraction of cloud covering the sky and is indicated by the amount of circle filled in. (Cloud cover is not presently displayed due to a problem with the source data. Present weather information is also not available for display at this time.) Wind speed and direction are represented by a wind barb whose line extends from the cover cloud circle towards the direction from which the wind is blowing. The short lines or flags coming off the end of the long line are called barbs. The barb indicates the wind speed in knots. Each normal barb represents 10 knots, while short barbs indicate 5 knots. A flag represents 50 knots. If there is no wind barb depicted, an outer circle around the cloud cover symbol indicates calm winds. The map of observations are updated in the nowCOAST map service approximately every 10 minutes. However, since the reporting frequency varies by network or station, the observation at a particular station may have not updated and may not update until after the next hour. For more detailed information about the update schedule, please see: http://new.nowcoast.noaa.gov/help/#section=updateschedule

    Background Information

    The maps of near-real-time surface weather and ocean observations are based on non-restricted data obtained from the NWS Family of Services courtesy of NESDIS/OPSD and also the NWS Meteorological Assimilation Data Ingest System (MADIS). The data includes observations from terrestrial and maritime observing from the U.S.A. and other countries. For terrestrial networks, the platforms including but not limited to ASOS, AWOS, RAWS, non-automated stations, U.S. Climate Reference Networks, many U.S. Geological Survey Stations via NWS HADS, several state DOT Road Weather Information Systems, and U.S. Historical Climatology Network-Modernization. For over maritime areas, the platforms include NOS/CO-OPS National Water Level Observation Network (NWLON), NOS/CO-OPS Physical Oceanographic Observing Network (PORTS), NWS/NDBC Fixed Buoys, NDBC Coastal-Marine Automated Network (C-MAN), drifting buoys, ferries, Regional Ocean Observing System (ROOS) coastal stations and buoys, and ships participating in the Voluntary Ship Observing (VOS) Program. Observations from MADIS are updated approximately every 10 minutes in the map service and those from NESDIS are updated every hour. However, not all stations report that frequently. Many stations only report once per hour sometime between 15 minutes before the hour and 30 minutes past the hour. For these stations, new observations will not appear until 22 minutes past top of the hour for land-based stations and 32 minutes past the top of the hour for maritime stations.

    Time Information

    This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

    This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

    In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

    Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:

    1. Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.
    2. Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:
      • validtime: Valid timestamp.
      • starttime: Display start time.
      • endtime: Display end time.
      • reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).
      • projmins: Number of minutes from reference time to valid time.
      • desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.
      • desigprojmins: Number of minutes from designated reference time to valid time.
    3. Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation at: http://new.nowcoast.noaa.gov/help/#section=layerinfo
    References
  6. A

    Near-Real-Time Surface In-Situ Observations

    • data.amerigeoss.org
    • eo-for-disaster-management-amerigeoss.hub.arcgis.com
    esri rest, html +1
    Updated Jul 5, 2017
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    AmeriGEO ArcGIS (2017). Near-Real-Time Surface In-Situ Observations [Dataset]. https://data.amerigeoss.org/pt_BR/dataset/6882ce6e-a4fe-45fe-8d07-3a8f6c8bba2f
    Explore at:
    esri rest, ogc wms, htmlAvailable download formats
    Dataset updated
    Jul 5, 2017
    Dataset provided by
    AmeriGEO ArcGIS
    Description
    Last Updated: January 2015
    Map Information

    This nowCOAST time-enabled map service provides map depicting the latest surface weather and marine weather observations at observing sites using the international station model. The station model is method for representing information collected at an observing station using symbols and numbers. The station model depicts current weather conditions, cloud cover, wind speed, wind direction, visibility, air temperature, dew point temperature, sea surface water temperature, significant wave height, air pressure adjusted to mean sea level, and the change in air pressure over the last 3 hours. The circle in the model is centered over the latitude and longitude coordinates of the station. The total cloud cover is expressed as a fraction of cloud covering the sky and is indicated by the amount of circle filled in. (Cloud cover is not presently displayed due to a problem with the source data. Present weather information is also not available for display at this time.) Wind speed and direction are represented by a wind barb whose line extends from the cover cloud circle towards the direction from which the wind is blowing. The short lines or flags coming off the end of the long line are called barbs. The barb indicates the wind speed in knots. Each normal barb represents 10 knots, while short barbs indicate 5 knots. A flag represents 50 knots. If there is no wind barb depicted, an outer circle around the cloud cover symbol indicates calm winds. The map of observations are updated in the nowCOAST map service approximately every 10 minutes. However, since the reporting frequency varies by network or station, the observation at a particular station may have not updated and may not update until after the next hour. For more detailed information about the update schedule, please see: http://new.nowcoast.noaa.gov/help/#section=updateschedule

    Background Information

    The maps of near-real-time surface weather and ocean observations are based on non-restricted data obtained from the NWS Family of Services courtesy of NESDIS/OPSD and also the NWS Meteorological Assimilation Data Ingest System (MADIS). The data includes observations from terrestrial and maritime observing from the U.S.A. and other countries. For terrestrial networks, the platforms including but not limited to ASOS, AWOS, RAWS, non-automated stations, U.S. Climate Reference Networks, many U.S. Geological Survey Stations via NWS HADS, several state DOT Road Weather Information Systems, and U.S. Historical Climatology Network-Modernization. For over maritime areas, the platforms include NOS/CO-OPS National Water Level Observation Network (NWLON), NOS/CO-OPS Physical Oceanographic Observing Network (PORTS), NWS/NDBC Fixed Buoys, NDBC Coastal-Marine Automated Network (C-MAN), drifting buoys, ferries, Regional Ocean Observing System (ROOS) coastal stations and buoys, and ships participating in the Voluntary Ship Observing (VOS) Program. Observations from MADIS are updated approximately every 10 minutes in the map service and those from NESDIS are updated every hour. However, not all stations report that frequently. Many stations only report once per hour sometime between 15 minutes before the hour and 30 minutes past the hour. For these stations, new observations will not appear until 22 minutes past top of the hour for land-based stations and 32 minutes past the top of the hour for maritime stations.

    Time Information

    This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

    This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

    In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

    Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:

    1. Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.
    2. Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:
      • validtime: Valid timestamp.
      • starttime: Display start time.
      • endtime: Display end time.
      • reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).
      • projmins: Number of minutes from reference time to valid time.
      • desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.
      • desigprojmins: Number of minutes from designated reference time to valid time.
    3. Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation at: http://new.nowcoast.noaa.gov/help/#section=layerinfo
    References
  7. A

    Total Cloud Cover (oktas) - Scale Band 4

    • data.amerigeoss.org
    csv, esri rest +5
    Updated Jul 5, 2017
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    AmeriGEO ArcGIS (2017). Total Cloud Cover (oktas) - Scale Band 4 [Dataset]. https://data.amerigeoss.org/dataset/total-cloud-cover-oktas-scale-band-4
    Explore at:
    geojson, html, esri rest, kml, csv, ogc wms, zipAvailable download formats
    Dataset updated
    Jul 5, 2017
    Dataset provided by
    AmeriGEO ArcGIS
    Description
    Last Updated: January 2015
    Map Information

    This nowCOAST time-enabled map service provides map depicting the latest surface weather and marine weather observations at observing sites using the international station model. The station model is method for representing information collected at an observing station using symbols and numbers. The station model depicts current weather conditions, cloud cover, wind speed, wind direction, visibility, air temperature, dew point temperature, sea surface water temperature, significant wave height, air pressure adjusted to mean sea level, and the change in air pressure over the last 3 hours. The circle in the model is centered over the latitude and longitude coordinates of the station. The total cloud cover is expressed as a fraction of cloud covering the sky and is indicated by the amount of circle filled in. (Cloud cover is not presently displayed due to a problem with the source data. Present weather information is also not available for display at this time.) Wind speed and direction are represented by a wind barb whose line extends from the cover cloud circle towards the direction from which the wind is blowing. The short lines or flags coming off the end of the long line are called barbs. The barb indicates the wind speed in knots. Each normal barb represents 10 knots, while short barbs indicate 5 knots. A flag represents 50 knots. If there is no wind barb depicted, an outer circle around the cloud cover symbol indicates calm winds. The map of observations are updated in the nowCOAST map service approximately every 10 minutes. However, since the reporting frequency varies by network or station, the observation at a particular station may have not updated and may not update until after the next hour. For more detailed information about the update schedule, please see: http://new.nowcoast.noaa.gov/help/#section=updateschedule

    Background Information

    The maps of near-real-time surface weather and ocean observations are based on non-restricted data obtained from the NWS Family of Services courtesy of NESDIS/OPSD and also the NWS Meteorological Assimilation Data Ingest System (MADIS). The data includes observations from terrestrial and maritime observing from the U.S.A. and other countries. For terrestrial networks, the platforms including but not limited to ASOS, AWOS, RAWS, non-automated stations, U.S. Climate Reference Networks, many U.S. Geological Survey Stations via NWS HADS, several state DOT Road Weather Information Systems, and U.S. Historical Climatology Network-Modernization. For over maritime areas, the platforms include NOS/CO-OPS National Water Level Observation Network (NWLON), NOS/CO-OPS Physical Oceanographic Observing Network (PORTS), NWS/NDBC Fixed Buoys, NDBC Coastal-Marine Automated Network (C-MAN), drifting buoys, ferries, Regional Ocean Observing System (ROOS) coastal stations and buoys, and ships participating in the Voluntary Ship Observing (VOS) Program. Observations from MADIS are updated approximately every 10 minutes in the map service and those from NESDIS are updated every hour. However, not all stations report that frequently. Many stations only report once per hour sometime between 15 minutes before the hour and 30 minutes past the hour. For these stations, new observations will not appear until 22 minutes past top of the hour for land-based stations and 32 minutes past the top of the hour for maritime stations.

    Time Information

    This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

    This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

    In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

    Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:

    1. Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.
    2. Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:
      • validtime: Valid timestamp.
      • starttime: Display start time.
      • endtime: Display end time.
      • reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).
      • projmins: Number of minutes from reference time to valid time.
      • desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.
      • desigprojmins: Number of minutes from designated reference time to valid time.
    3. Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation at: http://new.nowcoast.noaa.gov/help/#section=layerinfo
    References

  8. Total Cloud Cover (oktas) - Scale Band 10

    • data.amerigeoss.org
    • disasters-usnsdi.opendata.arcgis.com
    Updated Jul 5, 2017
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    NOAA GeoPlatform (2017). Total Cloud Cover (oktas) - Scale Band 10 [Dataset]. https://data.amerigeoss.org/ja/dataset/total-cloud-cover-oktas-scale-band-102
    Explore at:
    csv, geojson, html, arcgis geoservices rest api, zip, ogc wms, kmlAvailable download formats
    Dataset updated
    Jul 5, 2017
    Dataset provided by
    National Oceanic and Atmospheric Administrationhttp://www.noaa.gov/
    Description
    Last Updated: January 2015
    Map Information

    This nowCOAST time-enabled map service provides map depicting the latest surface weather and marine weather observations at observing sites using the international station model. The station model is method for representing information collected at an observing station using symbols and numbers. The station model depicts current weather conditions, cloud cover, wind speed, wind direction, visibility, air temperature, dew point temperature, sea surface water temperature, significant wave height, air pressure adjusted to mean sea level, and the change in air pressure over the last 3 hours. The circle in the model is centered over the latitude and longitude coordinates of the station. The total cloud cover is expressed as a fraction of cloud covering the sky and is indicated by the amount of circle filled in. (Cloud cover is not presently displayed due to a problem with the source data. Present weather information is also not available for display at this time.) Wind speed and direction are represented by a wind barb whose line extends from the cover cloud circle towards the direction from which the wind is blowing. The short lines or flags coming off the end of the long line are called barbs. The barb indicates the wind speed in knots. Each normal barb represents 10 knots, while short barbs indicate 5 knots. A flag represents 50 knots. If there is no wind barb depicted, an outer circle around the cloud cover symbol indicates calm winds. The map of observations are updated in the nowCOAST map service approximately every 10 minutes. However, since the reporting frequency varies by network or station, the observation at a particular station may have not updated and may not update until after the next hour. For more detailed information about the update schedule, please see: http://new.nowcoast.noaa.gov/help/#section=updateschedule

    Background Information

    The maps of near-real-time surface weather and ocean observations are based on non-restricted data obtained from the NWS Family of Services courtesy of NESDIS/OPSD and also the NWS Meteorological Assimilation Data Ingest System (MADIS). The data includes observations from terrestrial and maritime observing from the U.S.A. and other countries. For terrestrial networks, the platforms including but not limited to ASOS, AWOS, RAWS, non-automated stations, U.S. Climate Reference Networks, many U.S. Geological Survey Stations via NWS HADS, several state DOT Road Weather Information Systems, and U.S. Historical Climatology Network-Modernization. For over maritime areas, the platforms include NOS/CO-OPS National Water Level Observation Network (NWLON), NOS/CO-OPS Physical Oceanographic Observing Network (PORTS), NWS/NDBC Fixed Buoys, NDBC Coastal-Marine Automated Network (C-MAN), drifting buoys, ferries, Regional Ocean Observing System (ROOS) coastal stations and buoys, and ships participating in the Voluntary Ship Observing (VOS) Program. Observations from MADIS are updated approximately every 10 minutes in the map service and those from NESDIS are updated every hour. However, not all stations report that frequently. Many stations only report once per hour sometime between 15 minutes before the hour and 30 minutes past the hour. For these stations, new observations will not appear until 22 minutes past top of the hour for land-based stations and 32 minutes past the top of the hour for maritime stations.

    Time Information

    This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

    This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

    In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

    Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:

    1. Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.
    2. Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:
      • validtime: Valid timestamp.
      • starttime: Display start time.
      • endtime: Display end time.
      • reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).
      • projmins: Number of minutes from reference time to valid time.
      • desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.
      • desigprojmins: Number of minutes from designated reference time to valid time.
    3. Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation at: http://new.nowcoast.noaa.gov/help/#section=layerinfo
    References

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Cognitive Market Research (2024). The Global Digital Mapping Cameras market size was USD 248.2 Million in 2023! [Dataset]. https://www.cognitivemarketresearch.com/digital-mapping-cameras-market-report
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The Global Digital Mapping Cameras market size was USD 248.2 Million in 2023!

Explore at:
pdf,excel,csv,pptAvailable download formats
Dataset updated
Mar 13, 2024
Dataset authored and provided by
Cognitive Market Research
License

https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

Time period covered
2021 - 2033
Area covered
Global
Description

According to Cognitive Market Research, The Global Digital Mapping Cameras market size is USD 248.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 2.50% from 2023 to 2030.

North America Digital Mapping Cameras held the major market of more than 40% of the global revenue with market size of USD 0.10 million in 2023 and will grow at a compound yearly growth rate (CAGR) of 0.7% from 2023 to 2030.
Europe Digital Mapping Cameras accounted for a share of over 30% of the global market size of USD 0.07 million in 2023.
Asia Pacific Digital Mapping Cameras held the market of more than 23% of the global revenue with a market size of USD 0.06 million in 2023 and will rise at a compound yearly growth rate (CAGR) of 4.5% from 2023 to 2030.
South America Digital Mapping Cameras market has more than 5% of the global revenue with a market size of USD 0.01 million in 2023 and will rise at a compound yearly growth rate (CAGR) of 1.9% from 2023 to 2030.
Middle East and Africa Digital Mapping Cameras held the major market of more than 2% of the global revenue with market size of USD XX million in 2023 and will rise at a compound yearly growth rate (CAGR) of 2.2% from 2023 to 2030.
The demand for Digital Mapping cameras is rising due to the escalating demand for accurate and high-resolution geospatial.
Demand for manned aircraft remains higher in the Digital Mapping Cameras market.
The area arrays category held the highest Digital Mapping Cameras market revenue share in 2023.

Escalating Demand for Accurate and High-Resolution Geospatial to Provide Viable Market Output

The surging demand for accurate and high-resolution geospatial data across different industries, including agriculture, urban planning, and environmental monitoring, is driving market growth. The rising adoption of advanced mapping technologies for applications like smart cities, autonomous vehicles, and infrastructure development is boosting the need for sophisticated digital mapping cameras. Furthermore, the integration of AI and ML in digital mapping processes is improving the capabilities of these cameras, driving market expansion.

For instance, in September 2023, Microsoft and Bentley Systems announced a partnership to develop digital twins of infrastructure projects. This partnership will leverage the Azure cloud platform of Microsoft and expertise in infrastructure modeling of Bentley Systems to generate more precise and useful digital twins.

(Source: www.microsoft.com/en-in/about/)

Advancements in the Augmented Reality (AR) and Virtual Reality (VR) Technologies to Propel Market Growth

Advancements in AR and VR technologies are fuelling the digital mapping cameras market by increasing the demand for high-quality and immersive spatial data. Digital mapping cameras play an important role in capturing the detailed visual information required for generating realistic AR and VR environments, improving the overall user experience. The integration of accurate geospatial data from these cameras allows more accurate alignment of virtual content with the real-world environment, expanding the applications of AR and VR across industries like navigation, gaming, and training simulations.

For instance, in June 2023, Esri, a leading provider of geographic information systems (GIS) software, acquired CityGIS, a company specializing in 3D city modeling software, in order to allow Esri to provide a more comprehensive suite of solutions for urban planning and management.

(Source: www.esri.com/en-us/home)

Market Restraints of the Digital Mapping Cameras

High Initial Costs of Acquisition and Implementation to Restrict Market Growth

The high initial costs associated to acquiring and implementing advanced mapping camera technologies act as a significant barrier to entry for several businesses and organizations, mainly smaller ones with constrained budgets. This financial hurdle limits the broad adoption of digital mapping cameras, hindering their integration into different industries and applications. The reluctance to invest in these technologies because of their upfront expenses can impede the overall growth potential of the market.

Impact of COVID-19 on the Digital Mapping Cameras Market

The COVID-19 pandemic had a mixed effect on the Digital Mapping Cameras market. The global lockdowns and restrictions hampered manufacturing and disrupted supply chains, resu...

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