This web map references the live tiled map service from the OpenStreetMap (OSM) project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: https://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in ESRI products under a Creative Commons Attribution-ShareAlike license. Tip: This service is one of the basemaps used in the ArcGIS.com map viewer. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10. Tip: Here are some well known locations as they appear in this web map, accessed by launching the web map with a URL that contains location parameters: Athens, Cairo, Jakarta, Moscow, Mumbai, Nairobi, Paris, Rio De Janeiro, Shanghai

The size of the India Geospatial Analytics market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 14.82% during the forecast period.Geospatial analytics in the India market uses GIS and other technologies to analyze spatial data and provide valuable insights. Actually, geospatial analytics is a practice, which involves gathering, processing, and interpreting data on locations and their attributes that go with them. This includes geographic coordinates, images, or sensor readings. It helps business and governments make informed decisions regarding resource management, urban planning, transportation, environment monitoring, and disaster response. Increasing government initiatives, growth in private sector adoption, and the advancements of AI and machine learning are making the Indian market more and more driven forward. Recent developments include: January 2023: Eris India, a company providing Geographic Information System (GIS) software and solutions, announced that the company is developing a policy map to offer data to help states and policymakers in decision-making. The Policy Maps have been designed to provide meaningful insights into various government functions., July 2022: Google announced a new partnership in India with local authorities and organizations in order to provide customized features for the diverse needs of the people in the country. Also, Google is building helpful maps that provide more visual and accurate navigation.. Key drivers for this market are: Increasing Demand of Location Based Service, Growing Availability of Spatial Data. Potential restraints include: High Initial Cost in Implementing Geospatial Analytics Solutions. Notable trends are: Increasing Demand of Location Based Service.

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The map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap focuses on bathymetry. It also includes inland waters and roads, overlaid on land cover and shaded relief imagery.The Ocean Base map currently provides coverage for the world down to a scale of ~1:577k; coverage down to ~1:72k in United States coastal areas and various other areas; and coverage down to ~1:9k in limited regional areas.The World Ocean Reference is designed to be drawn on top of this map and provides selected city labels throughout the world. This web map lets you view the World Ocean Base with the Reference service drawn on top. Article in the Fall 2011 ArcUser about this basemap: "A Foundation for Ocean GIS".The map was compiled from a variety of best available sources from several data providers, including General Bathymetric Chart of the Oceans GEBCO_08 Grid version 20100927 and IHO-IOC GEBCO Gazetteer of Undersea Feature Names August 2010 version (https://www.gebco.net), National Oceanic and Atmospheric Administration (NOAA) and National Geographic for the oceans; and Garmin, HERE, and Esri for topographic content. You can contribute your bathymetric data to this service and have it served by Esri for the benefit of the Ocean GIS community. For details on the users who contributed bathymetric data for this map via the Community Maps Program, view the list of Contributors for the Ocean Basemap. The basemap was designed and developed by Esri. The GEBCO_08 Grid is largely based on a database of ship-track soundings with interpolation between soundings guided by satellite-derived gravity data. In some areas, data from existing grids are included. The GEBCO_08 Grid does not contain detailed information in shallower water areas, information concerning the generation of the grid can be found on GEBCO's website: https://www.gebco.net/data_and_products/gridded_bathymetry_data/. The GEBCO_08 Grid is accompanied by a Source Identifier (SID) Grid which indicates which cells in the GEBCO_08 Grid are based on soundings or existing grids and which have been interpolated. The latest version of both grids and accompanying documentation is available to download, on behalf of GEBCO, from the British Oceanographic Data Centre (BODC) https://www.bodc.ac.uk/data/online_delivery/gebco/.The names of the IHO (International Hydrographic Organization), IOC (intergovernmental Oceanographic Commission), GEBCO (General Bathymetric Chart of the Oceans), NERC (Natural Environment Research Council) or BODC (British Oceanographic Data Centre) may not be used in any way to imply, directly or otherwise, endorsement or support of either the Licensee or their mapping system.Tip: Here are some famous oceanic locations as they appear this map. Each URL launches this map at a particular location via parameters specified in the URL: Challenger Deep, Galapagos Islands, Hawaiian Islands, Maldive Islands, Mariana Trench, Tahiti, Queen Charlotte Sound, Notre Dame Bay, Labrador Trough, New York Bight, Massachusetts Bay, Mississippi Sound

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Maps with wind speed, wind rose and wind power density potential in India. 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).

The Digital Geologic-GIS Map of the Pine Ridge Indian Reservation Area, South Dakota is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (prir_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (prir_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (prir_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (badl_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (badl_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (prir_geology_metadata_faq.pdf). Please read the badl_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (prir_geology_metadata.txt or prir_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:125,000 and United States National Map Accuracy Standards features are within (horizontally) 63.5 meters or 208.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 4.8023 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.5281 and 0.2874 (in million kms), corressponding to 10.9979% and 5.9838% respectively of the total road length in the dataset region. 3.9868 million km or 83.0183% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0218 million km of information (corressponding to 0.5461% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

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What is the GIS In Utility Industry Market Size?

The GIS market in the utility industry size is forecast to increase by USD 3.55 billion at a CAGR of 19.8% between 2023 and 2028. Market expansion hinges on various factors, such as the rising adoption of Geographic Information System (GIS) solutions in the utility sector, the convergence of GIS with Building Information Modeling, and the fusion of Augmented Reality with GIS technology. These elements collectively drive market growth, reflecting advancements in spatial data analytics and technological convergence. The increased adoption of GIS solutions in the utility industry underscores the importance of geospatial data in optimizing infrastructure management. Simultaneously, the integration of GIS with BIM signifies the synergy between spatial and building information for enhanced project planning and management. Additionally, the integration of AR with GIS technology highlights the potential for interactive and interactive visualization experiences in spatial data analysis. Thus, the interplay of these factors delineates the landscape for the anticipated expansion of the market catering to GIS and related technologies.

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

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Market Segmentation

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

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    Canada
    US


  Europe

    Germany
    France


  APAC

    China
    India
    Japan


  Middle East and Africa



  South America

    Brazil

Which is the largest segment driving market growth?

The software segment is estimated to witness significant growth during the forecast period. In the utility industry, the spatial context of geographic information systems (GIS) plays a pivotal role in site selection, land acquisition, planning, designing, visualizing, building, and project management. Utilities, including electricity, gas, water, and telecommunications providers, leverage GIS software to efficiently manage their assets and infrastructure. This technology enables the collection, management, analysis, and visualization of geospatial data, derived from satellite imaging, aerial photography, remote sensors, and artificial intelligence. Geospatial AI, sensor technology, and digital reality solutions are integral components of GIS, enhancing capabilities for smart city planning, urban planning, water management, mapping systems, grid modernization, transportation, and green buildings.

Get a glance at the market share of various regions. Download the PDF Sample

The software segment was valued at USD 541.50 million in 2018. Moreover, the geospatial industry continues to evolve, with startups and software solutions driving innovation in hardware, smart city planning, land use management, smart infrastructure planning, and smart utilities. GIS solutions facilitate 4D visualization, enabling stakeholders to overcome geospatial data barriers and make informed decisions. The utility industry's reliance on GIS extends to building information modeling, augmented reality, and smart urban planning, ultimately contributing to the growth of the geospatial technology market.

Which region is leading the market?

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North America is estimated to contribute 37% 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.

How do company ranking index and market positioning come to your aid?

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

AABSyS IT Pvt. Ltd. - The company offers GIS solutions such as remote sensing and computer aided design and drafting solutions for electric and gas utility.

Technavio provides the ranking index for the top 20 companies along with insights on the market positioning of:

AABSyS IT Pvt. Ltd.
Autodesk Inc.
Avineon Inc.
Bentley Systems Inc.
Blue Marble Geographics
Cadcorp Ltd.
Caliper Corp.
Environmental Systems Research Institute Inc.
General Electric Co.
Hexagon AB
Mapbox Inc.
Maxar Technologies Inc.
Mobile GIS Services Ltd.
NV5 Global Inc.
Orbital Insight Inc.
Pitney Bowes Inc.
Schneider Electric SE
SuperMap Software Co. Ltd.
Trimble Inc.
VertiGIS Ltd.

Explore our company rankings and market positioning. Request Free Sample

How can Technavio assist you in ma

Published: August 2022A vector polygon GIS file of all Indian Territory boundaries in New York State. The file was originally a compilation of U.S. Geological Survey 1:100,000-scale digital vector files and NYS Department of Transportation 1:24,000-scale and 1:75,000-scale digital vector files. Boundaries were revised to 1:24,000-scale positional accuracy. Currently, boundary changes are made as needed based on authoritative sources.

GIS data for India's direct normal irradiance (DNI) and global horizontal irradiance. Provides 10-kilometer (km) solar resource maps and data for India. The 10-km hourly solar resource data were developed using weather satellite (METEOSAT) measurements incorporated into a site-time specific solar modeling approach developed at the U.S. State University of New York at Albany. The data is made publicly available in geographic information system (GIS) format (shape files etc). The new maps and data were released in June 2013. The new data expands the time period of analysis from 2002-2007 to 2002-2011 and incorporates enhanced aerosols information to improve direct normal irradiance (DNI). These products were developed by the U.S. National Renewable Energy Laboratory (NREL) in cooperation with India's Ministry of New and Renewable Energy, through funding from the U.S. Department of Energy and U.S. Department of State.

HVDC Circuit Breakers:High voltage direct current (HVDC) technology gaining popularity for long-distance power transmissionABB, Siemens, and Alstom are leading manufacturersEarth Switch:Critical component for electrical safety and isolationEaton, Schneider Electric, and BHEL are prominent players in this segment Recent developments include: In April 2021, Siemens Energy, a global energy technology company, merged with Siemens Gamesa, a leading wind turbine manufacturer. This strategic move was intended to strengthen Siemens Energy's position in the renewable energy sector, complementing its existing gas-insulated switchgear offerings and enhancing its capabilities in the energy transition.. Key drivers for this market are: GIS offers enhanced safety features compared to air-insulated switchgear, such as reduced risk of fire, environmental protection, and better performance in harsh conditions. These benefits are increasing the adoption of GIS in critical infrastructure projects. . Potential restraints include: The high upfront cost of GIS compared to conventional AIS can be a significant barrier, especially for smaller projects or regions with budget constraints. However, the long-term benefits in terms of reduced maintenance costs and space savings often outweigh this initial cost. . Notable trends are: The GIS market in India is evolving with the integration of smart grid technologies, automation, and digitalization. These innovations allow for better monitoring, control, and protection of the power distribution network, improving system efficiency and reducing downtime. .

The India Village-Level Geospatial Socio-Economic Data Set: 1991, 2001 is a compilation of the finest level of administrative boundaries in India (village/town-level) and over 200 socio-economic variables collected during the Indian Census in 1991 and 2001. This data set was developed by digitizing village/town level boundaries from the official analog maps published by the Survey of India for 2001. This data set also utilized tabular data for 1991 and 2001 from the Primary Census Abstract (PCA) and Village Directory (VD) data series of the Indian census. The data are in UTM 44N projection and are distributed primarily as shapefiles. Separate files are provided for each of the 28 states (number of states during 1991 and 2001 census) and combined Union Territories for 1991 and 2001.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['railway'] IN ('rail','station')

Features may have these attributes:

• name
• name:en
• railway
• ele
• operator:type
• layer
• addr:full
• addr:city
• source
• name:hi
• name:ta

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

The Spatial Data from the 2011 India Census contains gridded estimates of India population at a resolution of 1 kilometer along with two spatial renderings of urban areas, one based on the official tabulations of population and settlement type (statutory town, outgrowth, census town), and the second, remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. This data set includes a constructed hybrid representation of the urban settlement continuum by cross-classifying the census and remotely-sensed data.

Information on the amount of water flowing in streams and rivers is critical to the management of water resources, emergency response to flooding, fisheries management, and many other uses. This layer provides access to real-time stream gauge readings compiled from a variety of agencies and organizations.Dataset SummaryThe Live Stream Gauges layer contains real-time measurements of water depth from multiple reporting agencies recording at sensors across the world. This layer uses GeoEvent Processor to ingest and consolidate the many live sensor feeds, and updates itself every hour. At some gauges, flow in cubic feet per second is estimated using a stage-discharge rating curve. Flow forecasts are also provided where available. These sensor feeds are owned and maintained by the GIS community. For details on the coverage in this map and the users who contributed data for this map via the Community Maps Program, view the list of Contributors for the Live Stream Gauges Service. If you want to contribute your organization's gauges, read more about the program here.

This map is designed to be used as a basemap by GIS professionals and as a reference map by anyone. The map includes administrative boundaries, cities, water features, physiographic features, parks, landmarks, highways, roads, railways, and airports overlaid on land cover and shaded relief imagery for added context. The map provides coverage for the world down to a scale of ~1:72k. Coverage is provided down to ~1:4k for the following areas: Australia and New Zealand; India; Europe; Canada; Mexico; the continental United States and Hawaii; South America and Central America; Africa; and most of the Middle East. Coverage down to ~1:1k and ~1:2k is available in select urban areas. This basemap was compiled from a variety of best available sources from several data providers, including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), U.S. National Park Service (NPS), Food and Agriculture Organization of the United Nations (FAO), Department of Natural Resources Canada (NRCAN), GeoBase, Agriculture and Agri-Food Canada, Garmin, HERE, Esri, OpenStreetMap contributors, and the GIS User Community. For more information on this map, including the terms of use, visit us online.

DATASET: Alpha version 2010, 2012, 2015, 2020, 2025, 2030, and 2035 estimates of numbers of pregnancies per grid square, with national totals adjusted to match national estimates on numbers of pregnancies made by the Guttmacher Institute (http://www.guttmacher.org/). REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated pregnancies per grid square MAPPING APPROACH: Tatem AJ, Campbell J, Guerra-Arias M, de Bernis L, Moran A, Matthews Z, 2014, Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births, International Journal of Health Geographics, 13:2 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AFG2010pregnancies.tif = Afghanistan (AFG) pregnancies count map for 2010 adjusted to match UN national estimates on numbers of pregnancies. DATE OF PRODUCTION: May 2014

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.