Digital Map Service Market Outlook

The global digital map service market size is projected to grow significantly, from approximately $18.9 billion in 2023 to an estimated $53.1 billion by 2032, reflecting a compelling Compound Annual Growth Rate (CAGR) of 12.5%. This robust growth is driven by the increasing adoption of digital mapping technologies across diverse industries and the rising demand for real-time geographic and navigation data in both consumer and enterprise applications.

One of the primary growth factors for the digital map service market is the expanding use of digital maps in the automotive sector, particularly in the development of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. These technologies rely heavily on precise and up-to-date mapping data for navigation, obstacle detection, and other functionalities, making digital maps indispensable. Additionally, the proliferation of mobile devices and the integration of mapping services in applications such as ride-sharing, logistics, and local search have significantly contributed to market expansion.

Another significant driver is the increasing reliance on Geographic Information Systems (GIS) across various industries. GIS technology enables organizations to analyze spatial information, improve decision-making processes, and enhance operational efficiencies. Industries such as government, defense, agriculture, and urban planning utilize GIS for land use planning, disaster management, and resource allocation, among other applications. The continuous advancements in GIS technology and the integration of artificial intelligence (AI) and machine learning (ML) are expected to further propel market growth.

The rising demand for real-time location data is also a crucial factor fueling the growth of the digital map service market. Real-time location data is essential for applications such as fleet management, asset tracking, and public safety. Businesses leverage this data to optimize routes, monitor assets, and enhance customer service. The increasing implementation of Internet of Things (IoT) devices and the growing importance of location-based services are likely to sustain the demand for real-time mapping solutions in the coming years.

Regionally, North America leads the digital map service market, driven by the high adoption rate of advanced technologies and the presence of major players in the region. However, the Asia Pacific region is expected to witness the fastest growth, attributed to rapid urbanization, increasing smartphone penetration, and government initiatives to develop smart cities. Europe, Latin America, and the Middle East & Africa are also anticipated to experience substantial growth, fueled by the rising demand for digital mapping solutions across various sectors.

Service Type Analysis

In the digital map service market, the service type segment includes mapping and navigation, geographic information systems (GIS), real-time location data, and others. Mapping and navigation services hold a significant share in the market, primarily due to their extensive use in personal and commercial navigation systems. These services provide detailed road maps, traffic updates, and route planning, which are essential for everyday commuting and logistics operations. The continuous advancements in navigation technologies, such as integration with AI and ML for predictive analytics, are expected to enhance the accuracy and functionality of these services.

Geographic Information Systems (GIS) represent another critical segment within the digital map service market. GIS technology is widely used in various applications, including urban planning, environmental management, and disaster response. The ability to analyze and visualize spatial data in multiple layers allows organizations to make informed decisions and optimize resource allocation. The integration of GIS with other emerging technologies, such as drones and remote sensing, is further expanding its application scope and driving market growth.

Real-time location data services are gaining traction due to their importance in applications like fleet management, asset tracking, and location-based services. These services provide up-to-the-minute information on the geographical position of assets, vehicles, or individuals, enabling businesses to improve operational efficiency and customer satisfaction. The growing adoption of IoT devices and the increasing need for real-time visibility in supply chain operations are expected to bolster the demand for real-time location data services.</p&

The statewide dataset contains a combination of land cover mapping from 2016 aerial imagery and land use derived from standardized assessor parcel information for Massachusetts. The data layer is the result of a cooperative project between MassGIS and the National Oceanic and Atmospheric Administration’s (NOAA) Office of Coastal Management (OCM). Funding was provided by the Mass. Executive Office of Energy and Environmental Affairs.

This land cover/land use dataset does not conform to the classification schemes or polygon delineation of previous land use data from MassGIS (1951-1999; 2005).In this map service layer hosted at MassGIS' ArcGIS Server, all impervious polygons are symbolized by their generalized use code; all non-impervious land cover polygons are symbolized by their land cover category. The idea behind this method is to use both cover and use codes to provide a truer picture of how land is being used: parcel use codes may indicate allowed or assessed, not actual use; land cover alone (especially impervious) does not indicate actual use.

See the full datalayer description for more details.This map service is best displayed at large (zoomed in) scales. Also available are a Feature Service and a Tile Service (cache). The tile cache will display very quickly in in ArcGIS Online, ArcGIS Desktop, and other applications that can consume tile services.

The Digital Geologic-GIS Map of the Rhoda Quadrangle, Kentucky 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 (rhod_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (rhod_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 (rhod_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). 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 (maca_abli_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (maca_abli_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 (rhod_geology_metadata_faq.pdf). Please read the maca_abli_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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: 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 (rhod_geology_metadata.txt or rhod_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 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 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 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).

Cloud Based Mapping Service Market Outlook

The global cloud-based mapping service market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth is primarily driven by the increasing demand for real-time location data and asset tracking across various industries, such as transportation and logistics, retail, and healthcare.

One of the primary growth factors in the cloud-based mapping service market is the widespread adoption of Internet of Things (IoT) devices, which generate enormous amounts of location-based data. Enterprises are increasingly leveraging cloud-based mapping services to process and visualize this data for better decision-making and operational efficiency. Additionally, the proliferation of smartphones and mobile applications has fueled the demand for geospatial services, further propelling market growth. Moreover, the advancements in artificial intelligence (AI) and machine learning are enhancing the capabilities of mapping services, making them more sophisticated and reliable.

Another significant growth driver is the rising need for efficient fleet management solutions, particularly in the transportation and logistics sector. Companies are investing heavily in cloud-based mapping services to optimize routes, reduce fuel consumption, and improve delivery times. Furthermore, governments and defense organizations are increasingly adopting these services for surveillance, border security, and disaster management, further boosting the market. The integration of cloud-based mapping with Geographic Information Systems (GIS) is also providing valuable insights for urban planning and infrastructure development.

The healthcare sector is also emerging as a critical end-user of cloud-based mapping services. Hospitals and emergency services are utilizing these services for better patient tracking, resource allocation, and route optimization for ambulances. The COVID-19 pandemic has further accelerated the adoption of these technologies, as they play a crucial role in contact tracing and managing healthcare logistics. Retail businesses are also increasingly adopting cloud-based mapping services to enhance their supply chain management, optimize store layouts, and offer personalized customer experiences.

The advent of Mobile Mapping technology is revolutionizing the way geospatial data is collected and utilized. Mobile Mapping involves the use of vehicles equipped with various sensors, such as cameras and LiDAR, to capture detailed spatial data while in motion. This technology is particularly beneficial for creating accurate maps of urban environments, as it allows for the rapid collection of data over large areas. The integration of Mobile Mapping with cloud-based services enhances the ability to process and analyze this data in real-time, providing valuable insights for applications such as urban planning, infrastructure development, and environmental monitoring. As industries continue to seek efficient and cost-effective mapping solutions, the demand for Mobile Mapping is expected to grow, further driving the expansion of the cloud-based mapping service market.

Regionally, North America holds a significant share of the cloud-based mapping service market, primarily due to the early adoption of advanced technologies and the presence of major industry players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to rapid digital transformation, increasing investments in smart city projects, and the rising adoption of IoT devices in countries like China and India. Europe is also expected to show steady growth, driven by advancements in automotive technologies and increasing government initiatives for digitalization.

Component Analysis

The cloud-based mapping service market can be segmented into software and services based on components. The software segment includes various mapping and geospatial analysis tools that allow users to gather, analyze, and visualize location-based data. This segment is experiencing significant growth due to the increasing demand for advanced mapping solutions that offer real-time data and enhanced analytics. The proliferation of mobile applications that require geospatial data is also driving the growth of the software segment. Furthermore, the integration of AI and ML algorithms into

National Ecosystem and Ecosystem Services Map – Supporting Ecosystem Service Information: Habitat Asset Register. Published by Department of Housing, Local Government and Heritage. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).This dataset contains a habitat asset register for Ireland, e.g. a national scale habitat map conflating all nationally relevant habitat data into one dataset.

This dataset is part of a dataset series that establishes an ecosystem service maps (national scale) for a set of services prioritised through stakeholder consultation and any intermediate layers created by Environment Systems Ltd in the cause of the project. The individual dataset resources in the datasets series are to be considered in conjunction with the project report: https://www.npws.ie/research-projects/ecosystems-services-mapping-and-assessment

The project provides a National Ecosystem and Ecosystem Services (ES) map for a suite of prioritised services to assist implementation of MAES (Mapping and Assessment of Ecosystems and their services) in Ireland.

This involves stakeholder consultation for identification of services to be mapped, the development of a list of indicators and proxies for mapping, as well as an assessment of limitations to ES mapping on differing scales (Local, Catchment, Region, National, EU) based on data availability. Reporting on data gaps forms part of the project outputs.

The project relied on the usage of pre-existing data, which was also utilised to create intermediate data layers to aid in ES mapping. For a full list of the data used throughout the project workings, please refer to the project report....

Financial overview and grant giving statistics of Brain Mapping Support Foundation

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

The Digital Geologic-GIS Map of Sagamore Hill National Historic Site and Vicinity, New York 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 (sahi_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 (sahi_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 (sahi_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 GIS readme file (sahi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sahi_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 (sahi_geology_metadata_faq.pdf). Please read the sahi_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: 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 (sahi_geology_metadata.txt or sahi_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 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 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).

The Forest Service National Maps experience page is designed to distribute and deliver maps to the Forest Service and public. Maps cover Forest Service lands. Map series include National; Regional; Admin; Forest; Ranger District and 24K or better known as FSTopo, and our historical product FSTopo Legacy.

Seagrass beds are critical wetlands components of shallow marine ecosystems along the Massachusetts coastline. Seagrass beds provide food and cover for a great variety of commercially and recreationally important fauna and their prey. The leaf canopy of the seagrass bed calms the water, filters suspended matter and together with extensive roots and rhizomes, stabilizes sediment. Seagrasses are often referred to as "Submerged Aquatic Vegetation" or SAV. This distinguishes them from algae, which are not classified as plants by biologists (rather they are often placed in the kingdom protista), and distinguishes them from the "emergent" saltwater plants found in salt marshes.

In Massachusetts, the dominant SAV is Zostera marina or eelgrass. The other species found in the embayments of the Massachusetts coast is Ruppia maritima, commonly called “widgeon grass,” which is present in areas of less salinity along Cape Cod and Buzzards Bay. Widgeon grass, found in the upper reaches of embayments, has a thread-like morphology that makes it difficult to identify using remotely sensed data. It can only be identified and located by on-site survey.

The Massachusetts Department of Environmental Protection (MassDEP) began a program to map the state's SAV resources in the early 1990s and since 1995 the MassDEP Eelgrass Mapping Project has produced multiple surveys of SAV along the Massachusetts coastline, as listed here:

PhaseProject YearsProject Area11995Entire MA Coast22001Coast-wide MA Coast except Elizabeth Islands (Gosnold) and Mount Hope Bay32006/07Selected embayments, coast-wide including Elizabeth Islands42010-20132010 - South Shore of Cape Cod: Woods Hole to Chatham, selected embayments, Pleasant Bay;2012 - North Shore, Boston Harbor, South Shore to Provincetown;2013 - Buzzards Bay, Elizabeth Islands, Martha's Vineyard and Nantucket52015-20172015 - South Shore of Cape Cod, Pleasant Bay, Nantucket;2016 - North Shore, Boston Harbor, South Shore to Canal;2017 - Buzzards Bay, North Shore of Cape Cod, Elizabeth Islands and Martha's Vineyard62019-20232019 - South Shore of Cape Cod, Pleasant Bay, North Shore of Nantucket2020 - Martha’s Vineyard, Buzzards Bay and Elizabeth Islands 2021 - Cape Cod Bay (Provincetown through Duxbury) 2022 - South Shore, Boston Harbor, North Shore (Marshfield through Rockport)2023 - Cape Ann to the New Hampshire border (Essex through Newburyport)

View full metadata

Also see the map service.

In 2018, the Golden Gate National Parks Conservancy (Parks Conservancy) (https://parksconservancy.org), non-profit support partner to the National Park Service (NPS) Golden Gate National Recreation Area (GGNRA), initiated a fine scale vegetation mapping project in Marin County. The GGNRA includes lands in San Francisco and San Mateo counties, and NPS expressed interest in pursuing fine scale vegetation mapping for those lands as well. The Parks Conservancy facilitated multiple meetings with potential project stakeholders and was able to build a consortium of funders to map all of San Mateo County (and NPS lands in San Francisco). The consortium included the San Francisco Public Utilities Commission (SFPUC), Midpeninsula Regional Open Space District (MROSD), Peninsula Open Space Trust (POST), San Mateo City/County Association of Governments, and various County of San Mateo departments including Parks, Agricultural Weights and Measures, Public Works/Flood Control District, Office of Sustainability, and Planning and Building. Over a 3-year period, the project, collectively referred to as the “San Mateo Fine Scale Veg Map”, has produced numerous environmental GIS products including 1-foot contours, orthophotography, and other land cover maps. A 106-class fine-scale vegetation map was completed in April 2022 that details vegetation communities and agricultural land cover types, including forests, grasslands, riparian vegetation, wetlands, and croplands. The environmental data products from the San Mateo Fine Scale Veg Map are foundational and can be used by organizations and government departments for a wide range of purposes, including planning, conservation, and to track changes over time to San Mateo County’s habitats and natural resources.Development of the San Mateo fine-scale vegetation map was managed by the Golden Gate National Parks Conservancy and staffed by personnel from Tukman Geospatial (https://tukmangeospatial.com/), Aerial Information Systems (AIS; http://www.aisgis.com/), and Kass Green and Associates. The fine-scale vegetation map effort included field surveys by a team of trained botanists including Neal Kramer, Brett Hall, Lucy Ferneyhough, Brittany Burnett, Patrick Furtado, and Rosie Frederick. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the California Native Plant Society (CNPS) Vegetation Program (https://www.cnps.org/vegetation), with support from the California Department of Fish and Wildlife Vegetation Classification and Mapping Program (VegCAMP; https://wildlife.ca.gov/Data/VegCAMP) and ecologists with NatureServe (https://www.natureserve.org/) to develop a San Mateo County-specific vegetation classification. For more information on the field sampling and vegetation classification work San Mateo County Fine Scale Vegetation Map Final Report refer to the final report (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212663) issued by CNPS and corresponding floristic descriptions (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212666 and https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212667).Existing lidar data, collected in 2017 by San Mateo County was used to support the project. The lidar point cloud, and many of its derivatives, were used extensively during the process of developing the fine-scale vegetation and habitat map. The lidar data was used in conjunction with optical data. Optical data used throughout the project included 6-inch resolution airborne 4-band imagery collected in the summer of 2018, as well as various dates of National Agriculture Imagery Program (NAIP) imagery. Key data sets used in the lifeform and the enhanced lifeform mapping process include high resolution aerial imagery from 2018, the lidar-derived Canopy Height Model (CHM), and several other lidar-derived raster and vector datasets. In addition, a number of forest structure lidar derivatives are used in the machine learning portion of the enhanced lifeform workflow.In 2020, an enhanced lifeform map was produced which serves as the foundation for the much more floristically detailed fine-scale vegetation and habitat map. The lifeform map was developed using expert systems rulesets in Trimble Ecognition®, followed by manual editing.In 2020, Tukman Geospatial staff and partners conducted countywide reconnaissance field work to support fine-scale mapping. Field-collected data were used to train automated machine learning algorithms, which produced a fully automated countywide fine-scale vegetation and habitat map. Throughout 2021, AIS manually edited the fine-scale maps, and Tukman Geospatial and AIS went to the field for validation trips to inform and improve the manual editing process. In early January of 2022, draft maps were distributed and reviewed by San Mateo County’s community of land managers and by the funders of the project. Input from these groups was used to further refine the map. The countywide fine-scale vegetation map and related data products were made public in April 2022. In total, 106 vegetation classes were mapped. During the classification development phase, minimum mapping units (MMUs) were established for the vegetation mapping project. An MMU is the smallest area to be mapped on the ground. For this project, the mapping team chose to map different features at different MMUs. The MMU is 1/4 acre for agricultural, woody riparian, and wetland herbaceous classes; 1/2 acre for woody upland, upland herbaceous, and bare land classes; 1/5 acre for developed feature types; and 400 square feet for water.Accuracy assessment plot data were collected in 2021 and 2022. Accuracy assessment results were compiled and analyzed in the April of 2022. Overall accuracy of the lifeform map is 98 percent. Overall accuracy of the fine-scale vegetation map is 83.5 percent, with an overall ‘fuzzy’ accuracy of 90.8 percent.

A map service depicting Forest Service roads and trails that are designated for motor vehicle use under the official U.S. Government Code of Federal Regulations for identifying designated roads and trails (36 CFR 212.56). Road and Trail MVUM. The difference between MVUM_01 and MVUM_02 is that MVUM_02 has trails and roads in the MVUM Symbology group labeled while MVUM_01 does not. Additional roads, such as highways, county roads or public roads, are included for mapping purposes. This map service shows the specific types of motorized vehicles allowed on the designated routes and their seasons of use. Data used in this map service are designed to be consistent with the MVUM (Motor Vehicle Use Map). The road and trail data are compiled from the GIS Data Dictionary data and Infra tabular data that the U.S. Forest Service administrative units have prepared for the creation of their MVUMs. This data is published and refreshed on a unit by unit basis as needed and approved by the individual units in order to stay in sync and consistent with the published MVUMs. Only roads with the symbol value of 1,2,3, 4, 11, 12 are Forest Service System roads and contain data concerning their availability for OHV use. Only trails with the symbol value of 5-12, 16, 1. are Forest Service System trails and contain data concerning their availability for motorized use.�Metadata and Downloads

The USGS Transportation service from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and road data from U.S. Forest Service. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structures, to produce general reference base maps. The National Map Download Client allows free downloads of public domain transportation data in either Esri File Geodatabase or Shapefile formats. For additional information on the transportation data model, go to https://nationalmap.gov/transport.html.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

This dataset contains a raster dataset showing areas' contributing to the grassland network, including how important the area is for the network (based on habitat type and proximity to the next core area).

This dataset is part of a dataset series that establishes an ecosystem service maps (national scale) for a set of services prioritised through stakeholder consultation and any intermediate layers created by Environment Systems Ltd in the cause of the project. The individual dataset resources in the datasets series are to be considered in conjunction with the project report: https://www.npws.ie/research-projects/ecosystems-services-mapping-and-assessment

The project provides a National Ecosystem and Ecosystem Services (ES) map for a suite of prioritised services to assist implementation of MAES (Mapping and Assessment of Ecosystems and their services) in Ireland.

This involves stakeholder consultation for identification of services to be mapped, the development of a list of indicators and proxies for mapping, as well as an assessment of limitations to ES mapping on differing scales (Local, Catchment, Region, National, EU) based on data availability. Reporting on data gaps forms part of the project outputs.

The project relied on the usage of pre-existing data, which was also utilised to create intermediate data layers to aid in ES mapping. For a full list of the data used throughout the project workings, please refer to the project report.

From https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map :"The National Map is a suite of products and services that provide access to base geospatial information to describe the landscape of the United States and its territories. The National Map embodies 11 primary products and services and numerous applications and ancillary services. The National Map supports data download, digital and print versions of topographic maps, geospatial data services, and online viewing. Customers can use geospatial data and maps to enhance their recreational experience, make life-saving decisions, support scientific missions, and for countless other activities. Nationally consistent geospatial data from The National Map enable better policy and land management decisions and the effective enforcement of regulatory responsibilities. The National Map is easily accessible for display on the Web through such products as topographic maps and services and as downloadable data. The geographic information available from The National Map includes boundaries, elevation, geographic names, hydrography, land cover, orthoimagery, structures, and transportation. The majority of The National Map effort is devoted to acquiring and integrating medium-scale (nominally 1:24,000 scale) geospatial data for the eight base layers from a variety of sources and providing access to theresulting seamless coverages of geospatial data. The National Map also serves as the source of base mapping information for derived cartographic products, including 1:24,000 scale US Topo maps and georeferenced digital files of scanned historic topographic maps. Data sets and products from The National Map are intended for use by government, industry, and academia—focusing on geographic information system (GIS) users—as well as the public, especially in support of recreation activities. Other types of georeferenced or mapping information can be added within The National Map Viewer or brought in with The National Map data into a GIS to create specific types of maps or map views and (or) to perform modeling or analyses."

The custom digital map service market is experiencing robust growth, driven by the increasing demand for location-based services across diverse sectors. The market, estimated at $8 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a compound annual growth rate (CAGR) of approximately 15%. This expansion is largely attributed to several key drivers. Firstly, the automotive industry's reliance on advanced navigation and driver-assistance systems is a major catalyst. Secondly, the burgeoning location services sector, encompassing ride-sharing, delivery services, and location-based advertising, fuels considerable demand for customized map solutions. Further propelling growth is the rise of business analytics, where customized maps provide invaluable insights into spatial data, optimizing logistics, resource management, and market analysis. Finally, the ongoing development of real-time map data technologies, offering dynamic updates and high accuracy, significantly enhances the value proposition of these services. While data security and privacy concerns pose some challenges, the overall market outlook remains positive. The market segmentation reveals a strong emphasis on custom map solutions, reflecting a growing need for tailored cartographic representations catering to specific business requirements. Real-time map data is another key segment, capitalizing on the demand for dynamic and up-to-date location information. Geographic distribution shows North America and Europe as leading markets, with significant growth potential in the Asia-Pacific region, particularly in rapidly developing economies like China and India. Key players in the market, including Google, TomTom, Mapbox, and others, are actively investing in research and development, pushing technological boundaries and expanding their service portfolios to maintain a competitive edge. The ongoing evolution of map technologies, including improvements in data accuracy, integration with AI/ML, and expanding functionalities like 3D mapping and augmented reality overlays, will further shape the market landscape in the coming years.

Alaska Regional Development Organizations (ARDORs), their contact information, and their Comprehensive Economic Development Strategies (CEDS).The mission of the ARDORs Program is to encourage the formation of regional development organizations to prepare and implement regional development strategies (Alaska Statute 44.33.896). Through regional development strategies, local knowledge, and coordinated implementation, ARDORs champion economic development planning for Alaska’s regions and communities by leveraging baseline support provided by the State of Alaska.ARDORs develop customized work plans that contain goals, objectives, and strategies for addressing regional economic development needs including: Facilitating development of a healthy regional economy that results in sustainable business growth, new business investment, and economic diversification. Identifying and working to eliminate regional economic development barriers. Developing and implementing a comprehensive economic development strategy. Coordinating regional planning efforts that result in new employment and business opportunities. Working to enable multiple communities to collaborate and pool limited resources. Strengthening partnerships with public, private, and non-government organizations. Providing technical assistance to encourage business startup, retention, and expansion.Source: Alaska Department of Commerce, Community & Economic Development

The project provides a National Ecosystem and Ecosystem Services (ES) map for a suite of prioritised services to assist implementation of MAES (Mapping and Assessment of Ecosystems and their services) in Ireland. This involves stakeholder consultation for identification of services to be mapped, the development of a list of indicators and proxies for mapping, as well as an assessment of limitations to ES mapping on differing scales (Local, Catchment, Region, National, EU) based on data availability. Reporting on data gaps forms part of the project outputs. The project relied on the usage of pre-existing data, which was also utilised to create intermediate data layers to aid in ES mapping. For a full list of the data used throughout the project workings, please refer to the project report.