Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary SphereExtent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaVisible Scale: 1:144,000 to 1:1,000Number of Features: 36,569,286Source: USDA Natural Resources Conservation ServicePublication Date: December 2021Data from the gSSURGO database was used to create this layer.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some mapunits have a null value for soil order, a custom script was used to populate this field using the Component Name (compname) and Mapunit Name (muname) fields. This field was created using the dominant soil order of each mapunit.Esri SymbologyHorizon TableEach map unit polygon has one or more components and each component has one or more layers known as horizons. To incorporate this field from the Horizon table into the attributes for this layer, a custom script was used to first calculate the mean value weighted by thickness of the horizon for each component and then a mean value of components weighted by the Component Percentage Representative Value field for each map unit. K-Factor Rock FreeEsri Soil OrderThese fields were calculated from the Component table using a model that included the Pivot Table Tool, the Summarize Tool and a custom script. The first 11 fields provide the sum of Component Percentage Representative Value for each soil order for each map unit. The Soil Order Dominant Condition field was calculated by selecting the highest value in the preceding 11 soil order fields. In the case of tied values the component with the lowest average slope value (slope_r) was selected. If both soil order and slope were tied the first value in the table was selected.Percent AlfisolsPercent AndisolsPercent AridisolsPercent EntisolsPercent GelisolsPercent HistosolsPercent InceptisolsPercent MollisolsPercent SpodosolsPercent UltisolsPercent VertisolsSoil Order - Dominant ConditionEsri Popup StringThis field contains a text string calculated by Esri that is used to create a basic pop-up using some of the more popular SSURGO attributes.Map Unit KeyThe Mapunit key field is found

Note: This map service contains generalized NFS Land Unit boundaries to help with map service performance. Data in this service is not as accurate as the Automated Lands Program published data and will not accurately represent the boundary.National Forest System Land Unit original accurate data can be downloaded from here.An NFS Land Unit is nationally significant classification of Federally owned forest, range, and related lands that are administered by the USDA Forest Service or designated for administration through the Forest Service. NFS Land Unit types include proclaimed national forest, purchase unit, national grassland, land utilization project, research and experimental area, national preserve, and other land area. Each NFS Land Unit is identified by a National Forest Fiscal Identifier (NFFID) code, a unique 4-digit number that is used for accounting purposes.

The North Carolina Department of Environmental Quality (DEQ), Division of Energy, Minerals, and Land Resources (DEMLR), North Carolina Geological Survey (NCGS), in cooperation with the North Carolina Center for Geographic Information and Analysis (CGIA), developed the GIS data set version of the Geologic Map of North Carolina 1985. The data represents the digital equivalent of the official State Geology map (1:500,000- scale), but was digitized from (1:250,000-scale) base maps. There is one additional data set which accompany this layer, dikes.This geology is considered deprecated by the NCGS. There is currently no replacement for it that covers the whole state. We are working on a replacement that will be made available to the public in the near future (2025). This new data set will be in the United States Geological Survey (USGS) Geologic Map Schema (GeMS). When first released this data set will only cover a portion of the State at a scale of 1:100,000. This dataset will be dynamic and expand to cover the whole state by as early as 2030 at which point this layer will be removed from service.

The 2011 Bedrock Geologic Map of Vermont (1:100,000 scale) was created to integrate detailed (1:12,000- to 1:24,000-scale) modern mapping with the theory of plate tectonics to provide a framework for geologic, tectonic, economic, hydrogeologic, and environmental characterization of the bedrock of Vermont. It supersedes the 1961 bedrock geologic map which was produced at a scale of 1:250,000 (Doll and others, 1961).Please see the metadata and readme files at the publication website:https://pubs.usgs.gov/sim/3184/

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

These maps are produced by the Alaska DNR, Division of Oil and Gas (ADOG) and they provide description of participants and activity within oil and gas units. General representation of North Slope Oil and Gas Units is also provided in a separate map. ADOG updates these maps periodically (see links below).

This data is a qualitatively-derived interpretative polygon shapefile defining the bottom types of the seafloor from Boston Harbor and the harbor approaches, Massachusetts. Approximately 170 km square of sidescan sonar and bathymetric data were collected by the National Oceanic and Atmospheric Administration (NOAA) Ship Whiting in 2000 and 2001 and reprocessed and gridded by the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS).

The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight- digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits). The data produced by GIRAS was originally collected at a scale of 1:250K. Some areas, notably major cities in the west, were recompiled at a scale of 1:100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edgematched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States. NOTE: A version of this data theme that is more throughly checked (though based on smaller-scale maps) is available here: https://water.usgs.gov/lookup/getspatial?huc2m HUC, GIRAS, Hydrologic Units, 1:250 For the most current data and information relating to hydrologic unit codes (HUCs) please see http://water.usgs.gov/GIS/huc.html. The Watershed Boundary Dataset (WBD) is the most current data available for watershed delineation. See http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset

Generalized landscape units for Kuparuk River basin. Polygon coverage of landscape units for the Kuparuk River watershed with seven units. This map was drawn by Skip Walker, derived by interpreting the CIR version of the GTOPO30 Landsat MSS image created in 1999 by Jiong Jia. Go to Website Link :: Toolik Arctic Geobotanical Atlas below for details on legend units, photos of map units and plant species, glossary, bibliography and links to ground data. Map Themes: Elevation, Hydrology, Landscape, Landsat MSS False-Color Infrared, Vegetation References Walker, D. A. 2009. Unpublished map.

Dataset for provision older form of cadastral map in digital form. These maps are not seamless and that is why they are not edited in ISKN database (Information system of Cadastre of Real Estates). Their content is however identical with data serie of cadastral map in digital form (see Cadastral map distributed by cadastral units (zonings) in the VFK format). They are step-by-step reprocessed into the seamless form and imported into the ISKN database. Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on ISKN (Information System of the Cadastre of Real Estates). Data is provided via cadastral units using JTSK coordinate system (EPSG:5514). (to the 2025-04-14 it is 4 cadastral units only). Dataset is compressed (ZIP) for downloading. Data is provided in text format VKM (older version of Czech national standard). Format description is published on webpages of the Czech Office for Surveying, Mapping and Cadastre. More in the Cadastral Act No. 256/2013 Coll., Cadastral Decree No. 357/2013 Coll., Cadastral Decree on Data Provision No. 357/2013 Coll., as amended.

Map Positioning Unit Market Outlook

The global map positioning unit market size was valued at approximately $5.8 billion in 2023 and is projected to reach around $12.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.7% during the forecast period. The market is being driven primarily by the increasing adoption of advanced navigation systems across various industries, including automotive, aerospace, and defense, among others.

One of the primary growth factors for the map positioning unit market is the rapid technological advancements in navigation and positioning systems. The integration of advanced technologies such as GPS, GNSS, and Inertial Navigation Systems (INS) has significantly improved the accuracy and reliability of these units, making them indispensable in critical applications. Furthermore, the advent of autonomous vehicles and drones has necessitated the use of highly accurate positioning systems, thereby boosting the demand for map positioning units.

Another significant growth driver is the increasing demand for real-time data and location-based services. Industries such as logistics, transportation, and retail are increasingly leveraging real-time positioning data to optimize their operations, enhance customer experience, and improve overall efficiency. Moreover, the proliferation of smartphones and IoT devices has further accelerated the demand for precise positioning systems, as these devices rely heavily on accurate location data to function effectively.

The expansion of smart cities and infrastructure development projects globally is also propelling the growth of the map positioning unit market. Governments and municipal bodies are investing heavily in advanced navigation and positioning systems to enhance urban mobility, manage traffic flow, and improve public safety. Additionally, the use of these systems in disaster management and emergency response operations is gaining traction, further contributing to market growth.

The emergence of Centimeter-level High-precision Map technology is revolutionizing the landscape of navigation and positioning systems. This advanced mapping technology offers unprecedented accuracy, making it a game-changer for industries requiring precise location data. From autonomous vehicles to drone operations, the demand for centimeter-level precision is becoming increasingly critical. This technology not only enhances the safety and efficiency of these systems but also opens new possibilities for applications in urban planning, agriculture, and environmental monitoring. As the world moves towards more interconnected and automated systems, the integration of high-precision maps is set to play a pivotal role in shaping the future of navigation solutions.

Regionally, North America holds a significant share of the map positioning unit market, driven by the presence of leading technology firms and high adoption rates of advanced navigation systems. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapid industrialization, increasing urbanization, and significant investments in infrastructure development projects. The growing automotive and defense sectors in countries like China and India are also contributing to the market's expansion in this region.

Component Analysis

The map positioning unit market can be segmented by component into hardware, software, and services. The hardware segment, which includes GPS modules, antennas, and other physical components, currently holds a substantial share of the market. This dominance can be attributed to the ongoing advancements in hardware technology, which are enhancing the accuracy, reliability, and durability of map positioning units. Companies are investing heavily in the development of next-generation hardware components to meet the increasing demand for high-precision navigation systems.

Software, another critical component, is also witnessing significant growth. The software segment encompasses the algorithms, data processing modules, and user interfaces that enable the effective functioning of map positioning units. Innovations in software are crucial for improving the performance and efficiency of these systems. For instance, the integration of artificial intelligence and machine learning algorithms is revolutionizing the way map positioning units process and interpret data, leading to more accurate

Abstract: This data set is a complete digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for California. This data set consists of geo-referenced digital data and associated attributes created in accordance with the "FGDC Proposal, Version 1.0 - Federal Standards For Delineation of Hydrologic Unit Boundaries 3/01/02"(http://www.ftw.nrcs.usda.gov/huc_data.html). Polygons are attributed with hydrologic unit codes for 4th level sub-basins, 5th level watersheds, 6th level subwatersheds, name, size, downstream hydrologic unit, type of watershed, non-contributing areas and flow modification. Arcs are attributed with the highest hydrologic unit code for each watershed, linesource and a metadata reference file.

Purpose: The Watershed and Subwatershed hydrologic unit boundaries provide a uniquely identified and uniform method of subdividing large drainage areas. The smaller sized 6th level sub-watersheds (up to 250,000 acres) are useful for numerous application programs supported by a variety of local, State, and Federal Agencies. This data set is intended to be used as a tool for water-resource management and planning activities, particularly for site-specific and localized studies requiring a level of detail provided by large-scale map information. The dataset will be appended to a larger seamless nationally consistent geospatial database as other states complete their portion of the watershed boundary dataset.

Use_Constraints: The distributor shall not be held liable for improper or incorrect use of this data, based on the description of appropriate/inappropriate uses described in this metadata document. It is strongly recommended that this data is directly acquired from the distributor and not indirectly through other sources which may have changed the data in some way. These data should not be used at scales greater than 1:24,000 for the purpose of identifying hydrographic watershed boundary feature locations in the U.S.A. The NRCS should be acknowledged as the data source in products derived from these data. The Watershed Boundary Dataset is public information and may be interpreted by all organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application of the data. Federal, State, or local regulatory bodies are not to reassign to the Natural Resources Conservation Service any authority for the decisions they make. The Natural Resources Conservation Service will not perform any evaluations of these maps or purposes related solely to State or local regulatory programs. Photographic or digital enlargement of these maps to scales greater than that at which they were originally delineated can result in misrepresentation of the data. If enlarged, the maps will not include the fine detail that would be appropriate for mapping at the small scale. Digital data files are periodically updated. Files are dated, and users are responsible for obtaining the latest version of the data from the source distributor.

© Natural Resources Conservation Service (NRCS) USDA

Location and HD Map Unit Market Outlook

The global market size for Location and HD Map Unit was valued at $8.3 billion in 2023 and is projected to reach $26.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.8% from 2024 to 2032. This robust growth is primarily driven by advancements in autonomous driving technologies, increasing integration of Artificial Intelligence (AI) in navigation systems, and the rising demand for high-precision mapping solutions across various industries.

One of the primary growth factors for the Location and HD Map Unit market is the surge in demand for autonomous vehicles. Automotive manufacturers and technology providers are heavily investing in developing self-driving cars, which rely on accurate and real-time location data as well as HD maps. These maps are essential for the proper functioning of advanced driver assistance systems (ADAS) that ensure the safety and efficiency of autonomous driving. The growing emphasis on reducing traffic accidents and enhancing road safety is also a key driver for market growth.

Another significant factor propelling market expansion is the rise of smart cities. Governments and urban planners are increasingly focusing on integrating cutting-edge technologies to improve urban infrastructure and transportation. Location and HD map units play a crucial role in the development of smart city initiatives by providing precise geospatial data that helps in efficient traffic management, urban planning, and the deployment of public services. The increasing adoption of Internet of Things (IoT) technologies further amplifies the need for high-accuracy mapping solutions.

Moreover, the logistics and fleet management sectors are witnessing substantial growth due to the adoption of Location and HD map units. With the advent of e-commerce and the need for efficient delivery systems, logistics companies are leveraging high-definition maps to optimize their routes, reduce delivery times, and enhance customer satisfaction. Fleet management systems also benefit from real-time mapping solutions for effective vehicle tracking, fuel management, and predictive maintenance, thereby driving the market forward.

High Accuracy Map technology is becoming increasingly vital in the development of autonomous vehicles and smart city infrastructures. These maps provide precise geospatial data that is crucial for navigation and real-time decision-making. By offering detailed information about road conditions, traffic patterns, and environmental factors, High Accuracy Maps enable vehicles to operate safely and efficiently in complex urban environments. The integration of AI and machine learning further enhances the capabilities of these maps, allowing for continuous updates and improvements in accuracy. As the demand for autonomous driving and smart city solutions grows, the importance of High Accuracy Maps in ensuring seamless and safe navigation cannot be overstated.

From a regional perspective, North America holds the dominant share in the Location and HD Map Unit market, thanks to the presence of major technology companies and automotive giants. The region's well-established infrastructure and early adoption of advanced technologies contribute to its leading position. However, Asia Pacific is expected to witness the highest growth rate during the forecast period owing to rapid urbanization, increasing investments in smart cities, and the burgeoning automotive sector, particularly in countries like China, Japan, and South Korea.

Component Analysis

The Location and HD Map Unit market is segmented by component into hardware, software, and services. The hardware segment includes sensors, cameras, and LiDAR systems, which are crucial for capturing real-time data for mapping. The software segment encompasses the algorithms and platforms used for processing and analyzing the collected data to create high-definition maps. Services include installation, maintenance, and consulting services offered by solution providers.

In the hardware segment, sensors and LiDAR systems are witnessing significant demand due to their ability to capture high-resolution spatial data. These components are essential for the functioning of autonomous vehicles and robotics. The growing adoption of LiDAR technology in various applications such as automotive, drones, and smart cities is contributing to the expansion of the hardware segment. Additionally, advancements in sensor tec

Soil Landscape Map Units have recognizable topographic features formed on a particular geological material and containing a limited and defined range of soils. They therefore have similar land and soil characteristics and land suitability. Each Soil Landscape Map Unit incorporates a Land System, and a Soil Landscape Unit specific to that Land System. Each Soil Landscape Unit has an assigned Land Type.

Dataset for provision of cadastral map in digital form in SHP format. Data comes from ISKN (Information System of Cadastre of Real Estates). Cadastral map includes planimetric and descriptive component. Planimetry contains boundaries of parcels, cadastral and administrative units, perimeters of buildings and geodetic control points. Descriptive elements contain lettering (parcel numbers, geographical names etc.), map symbols (symbols of nature of land use etc.) and lines (boundaries of protected zones etc.). Some information of digital map is omitted during the data conversion into SHP format (information on point number, quality code linked to planimetry points, line symbols etc.). Dataset is provided as Open Data (licence CC-BY 4.0). Data is based on ISKN (Information System of the Cadastre of Real Estates). Cadastral map is provided via cadastral units using JTSK coordinate system (EPSG:5514). Data is available for cadastral units with digital form of cadastral map only - (to the 2025-06-30 it is % of the territory of the Czech Republic, i.e. km2). Data is provided in SHP format (Windows-1250 character encoding). Dataset is compressed (ZIP) for downloading. More in the Cadastral Act No. 256/2013 Coll., Cadastral Decree No. 357/2013 Coll., Cadastral Decree on Data Provision No. 357/2013 Coll., as amended.

This digital data release presents contour data from multiple subsurface geologic horizons as presented in previously published summaries of the regional subsurface configuration of the Michigan and Illinois Basins. The original maps that served as the source of the digital data within this geodatabase are from the Geological Society of America’s Decade of North American Geology project series, “The Geology of North America” volume D-2, chapter 13 “The Michigan Basin” and chapter 14 “Illinois Basin Region”. Contour maps in the original published chapters were generated from geophysical well logs (generally gamma-ray) and adapted from previously published contour maps. The published contour maps illustrated the distribution sedimentary strata within the Illinois and Michigan Basin in the context of the broad 1st order supercycles of L.L. Sloss including the Sauk, Tippecanoe, Kaskaskia, Absaroka, Zuni, and Tejas supersequences. Because these maps represent time-transgressive surfaces, contours frequently delineate the composite of multiple named sedimentary formations at once. Structure contour maps on the top of the Precambrian basement surface in both the Michigan and Illinois basins illustrate the general structural geometry which undergirds the sedimentary cover. Isopach maps of the Sauk 2 and 3, Tippecanoe 1 and 2, Kaskaskia 1 and 2, Absaroka, and Zuni sequences illustrate the broad distribution of sedimentary units in the Michigan Basin, as do isopach maps of the Sauk, Upper Sauk, Tippecanoe 1 and 2, Lower Kaskaskia 1, Upper Kaskaskia 1-Lower Kaskaskia 2, Kaskaskia 2, and Absaroka supersequences in the Illinois Basins. Isopach contours and structure contours were formatted and attributed as GIS data sets for use in digital form as part of U.S. Geological Survey’s ongoing effort to inventory, catalog, and release subsurface geologic data in geospatial form. This effort is part of a broad directive to develop 2D and 3D geologic information at detailed, national, and continental scales. This data approximates, but does not strictly follow the USGS National Cooperative Geologic Mapping Program's GeMS data structure schema for geologic maps. Structure contour lines and isopach contours for each supersequence are stored within separate “IsoValueLine” feature classes. These are distributed within a geographic information system geodatabase and are also saved as shapefiles. Contour data is provided in both feet and meters to maintain consistency with the original publication and for ease of use. Nonspatial tables define the data sources used, define terms used in the dataset, and describe the geologic units referenced herein. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and accompanying nonspatial tables.

This Open-File report is a digital geologic map database. This digital map database is prepared from a previously published map by Brabb (1989). The geologic map database delineates map units that are identified by general age, lithology and clast size following the stratigraphic nomenclature of the U. S. Geological Survey.

The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration.

Geographical grid pattern based on the 1:1000000 international map series for the State of New South Wales facilitating the description of resource exploration title areas.\r A UNIT identifies an area of 1 minute of Latitude by 1 minute of Longitude and is referenced by the 1:1000000 international map series name, a number from 1 through 3456, and the letters ‘a’ through ‘z’, excluding the letter ‘i’.\r Map Units are further described by the NSW Mining Regulation 2016, Part 1, Clause 10: https://www.legislation.nsw.gov.au/#/view/regulation/2016/498/part1/sec10\r And by Schedule 4 – Sections 1 through 3: https://www.legislation.nsw.gov.au/#/view/regulation/2016/498/sch4

The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS (Natural Resources Conservation Service). The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings. The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses. SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database. A complete SSURGO dataset consists of:

GIS data (as ESRI® Shapefiles) attribute data (dbf files - a multitude of separate tables) database template (MS Access format - this helps with understanding the structure and linkages of the various tables) metadata

Resources in this dataset:Resource Title: SSURGO Metadata - Tables and Columns Report. File Name: SSURGO_Metadata_-_Tables_and_Columns.pdfResource Description: This report contains a complete listing of all columns in each database table. Please see SSURGO Metadata - Table Column Descriptions Report for more detailed descriptions of each column.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Metadata - Table Column Descriptions Report. File Name: SSURGO_Metadata_-_Table_Column_Descriptions.pdfResource Description: This report contains the descriptions of all columns in each database table. Please see SSURGO Metadata - Tables and Columns Report for a complete listing of all columns in each database table.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Data Dictionary. File Name: SSURGO 2.3.2 Data Dictionary.csvResource Description: CSV version of the data dictionary