The geospatial data presented here as ArcGIS layers denote landcover/landuse classifications to support field sampling efforts that occurred within the Cache Creek Settling Basin (CCSB) from 2010-2017. Manual photointerpretation of a National Agriculture Imagery Program (NAIP) dataset collected in 2012 was used to characterize landcover/landuse categories (hereafter habitat classes). Initially 9 categories were assigned based on vegetation structure (Vegtype1). These were then parsed into two levels of habitat classes that were chosen for their representativeness and use for statistical analyses of field sampling. At the coarsest level (Landcover 1), five habitat classes were assigned: Agriculture, Riparian, Floodplain, Open Water, and Road. At the more refined level (Landcover 2), ten habitat classes were nested within these five categories. Agriculture was not further refined within Landcover 2, as little consistency was expected between years as fields rotated between corn, pumpkin, tomatoes, and other row crops. Riparian habitat, marked by large canopy trees (such as Populus fremontii (cottonwood)) neighboring stream channels, also was not further refined. Floodplain habitat was separated into two categories: Mixed NonWoody (which included both Mowed and Barren habitats) and Mixed Woody. This separation of the floodplain habitat class (Landcover1) into Woody and NonWoody was performed with a 100 m2 moving window analysis in ArcGIS, where habitats were designated as either ≥50% shrub or tree cover (Woody) or <50%, and thus dominated by herbaceous vegetation cover (NonWoody). Open Water habitat was refined to consider both agricultural Canal (created) and Stream (natural) habitats. Road habitat was refined to separate Levee Roads (which included both the drivable portion and the apron on either side) and Interior roads, which were less managed. The map was tested for errors of omission and commission on the initial 9 categories during November 2014. Random points (n=100) were predetermined, and a total of 80 were selected for field verification. Type 1 (false positive) and Type 2 (false negative) errors were assessed. The survey indicated several corrections necessary in the final version of the map. 1) We noted the presence of woody species in “NonWoody” habitats, especially Baccharus salicilifolia (mulefat). Habitats were thus classified as “Woody” only with ≥50% presence of canopy species (e.g. tamarisk, black willow) 2) Riparian sites were over-characterized, and thus constrained back to “near stream channels only”. Walnut (Juglans spp) and willow stands alongside fields and irrigation canals were changed to Mixed Woody Floodplain. Fine tuning the final habitat distributions was thus based on field reconnaissance, scalar needs for classifying field data (sediment, water, bird, and fish collections), and validation of data categories using species observations from scientist field notes. Calibration was made using point data from the random survey and scientist field notes, to remove all sources of error and reach accuracy of 100%. The coverage “CCSB_Habitat_2012” is provided as an ARCGIS shapefile based on a suite of 7 interconnected ARCGIS files coded with the suffixes: cpg, dbf, sbn, sbx, shp, shx, and prj. Each file provides a component of the coverage (such as database or projection) and all files are necessary to open the “CCSB_Habitat_2012.shp” file with full functionality.

Vehicle Edge Cache for Map Updates Market Outlook

According to our latest research, the global Vehicle Edge Cache for Map Updates market size reached USD 1.32 billion in 2024, demonstrating robust growth driven by the surging adoption of connected vehicles and the escalating need for real-time map data. The market is set to expand at a CAGR of 17.6% from 2025 to 2033, with the market forecasted to reach USD 6.11 billion by 2033. The primary growth factor is the increasing integration of advanced navigation and autonomous driving technologies, which demand reliable, low-latency map updates delivered at the vehicle edge.

The exponential growth in connected and autonomous vehicles is a significant driver for the Vehicle Edge Cache for Map Updates market. As automotive manufacturers and technology companies race to enable seamless navigation and improved safety features, the demand for up-to-date, high-resolution map data has intensified. Edge caching solutions are pivotal in ensuring that vehicles receive timely map updates without the latency and bandwidth constraints of centralized cloud architectures. This trend is further amplified by the proliferation of smart infrastructure and vehicle-to-everything (V2X) communication, which necessitate rapid data processing and distribution at the network edge. The ability of edge caches to reduce data transmission costs and improve reliability is making them indispensable in the evolving automotive ecosystem.

Another prominent growth factor is the rising complexity of urban mobility and logistics networks. Cities worldwide are experiencing increased traffic congestion, dynamic road changes, and frequent infrastructure upgrades, all of which require real-time updates to in-vehicle navigation systems. Edge caching facilitates localized data storage and instant distribution of map updates, enabling navigation systems to reflect the latest road conditions, construction zones, and traffic incidents. This capability is critical for not only passenger vehicles but also commercial fleets and ride-sharing platforms that rely on accurate routing and efficient fleet management. As urbanization and e-commerce continue to surge, the market is poised to benefit from the growing need for dynamic, location-specific map data.

Technological advancements in edge computing and artificial intelligence are accelerating the adoption of vehicle edge cache solutions. The integration of AI-powered algorithms enables predictive caching, which anticipates the data needs of vehicles based on driving patterns, location, and historical usage. This proactive approach minimizes latency and ensures that vehicles have access to the most relevant map updates, even in areas with limited connectivity. Furthermore, the ongoing shift toward electric and autonomous vehicles is creating new opportunities for edge cache providers to collaborate with OEMs and technology partners, driving innovation and expanding the addressable market. These trends underscore the critical role of edge caching in enabling the next generation of intelligent transportation systems.

Regionally, the Vehicle Edge Cache for Map Updates market exhibits strong momentum in North America, Europe, and Asia Pacific, with each region contributing significantly to overall market growth. North America leads in terms of technological adoption and investment in connected vehicle infrastructure, while Europe benefits from stringent safety regulations and a robust automotive industry. Asia Pacific, on the other hand, is experiencing rapid expansion due to the rising penetration of connected vehicles and government initiatives supporting smart mobility. Latin America and the Middle East & Africa are gradually catching up, driven by improvements in network infrastructure and increasing vehicle electrification. The regional dynamics are expected to shape market growth patterns over the forecast period, with Asia Pacific projected to record the highest growth rate.

Component Analysis

The Vehicle Edge Cache for Map Update

With our display services, you can use Lantmäteriet’s maps in your own systems or applications. The display services can be combined with our other geodata services. The service provides access to a topographic web map that is customised for screen viewing. The map information is displayed with a harmonised cartography between the scales. The service is very similar in terms of content to the service Topographic Webmap Viewing but has higher performance and slightly less timeliness. The information is updated at different intervals depending on the information type and scale level. Scale ranges up to 1:20,000 are updated at least daily. Scale ranges above 1:20,000 are updated at least quarterly.

Vehicle Edge Cache for Map Updates Market Outlook

According to our latest research, the Global Vehicle Edge Cache for Map Updates market size was valued at $1.8 billion in 2024 and is projected to reach $6.7 billion by 2033, expanding at a robust CAGR of 15.7% during the forecast period of 2025–2033. One of the primary factors driving this impressive growth is the surge in adoption of connected vehicles and autonomous driving technologies, which demand real-time, reliable, and frequently updated map data for safe and efficient operation. The increasing complexity of road networks, urban mobility solutions, and the proliferation of electric and autonomous vehicles are further catalyzing the need for advanced edge caching solutions to ensure seamless, low-latency delivery of critical map updates directly to vehicles.

Regional Outlook

North America currently dominates the Vehicle Edge Cache for Map Updates market, accounting for the largest share of global revenue, estimated at over 38% in 2024. This leadership is attributed to the region’s mature automotive sector, widespread deployment of advanced driver-assistance systems (ADAS), and a robust ecosystem of technology providers and automotive OEMs. The presence of major players investing heavily in edge computing infrastructure, coupled with supportive regulatory frameworks promoting connected and autonomous vehicle adoption, further strengthens North America's position in the market. Additionally, the region benefits from early adoption of 5G networks, which enhances the capability of edge caching technologies to deliver real-time map updates, thus meeting the stringent requirements of next-generation navigation and safety applications.

In contrast, the Asia Pacific region is projected to be the fastest-growing market, with a forecasted CAGR exceeding 18.5% during 2025–2033. This rapid growth is primarily driven by the acceleration of smart city initiatives, burgeoning automotive production, and increasing investments in electric and autonomous vehicles across countries such as China, Japan, and South Korea. The region’s massive population and urbanization trends are fostering demand for advanced navigation and fleet management solutions, necessitating efficient map update mechanisms. Moreover, aggressive government policies supporting connected mobility and substantial funding for 5G and edge computing infrastructure are enabling rapid deployment of vehicle edge cache technologies throughout Asia Pacific.

Emerging economies in Latin America and Middle East & Africa are beginning to recognize the importance of vehicle edge caching for map updates, though adoption is still at a nascent stage. Challenges such as inadequate digital infrastructure, limited access to high-speed connectivity, and varying regulatory standards pose significant barriers. However, localized demand is rising, particularly among fleet operators and logistics companies seeking to optimize routes and reduce operational costs. Policy reforms aimed at boosting smart transportation and investments in digital infrastructure are expected to gradually unlock new opportunities in these regions, albeit at a slower pace compared to North America, Europe, and Asia Pacific.

Report Scope

Overview map of the Uinta-Wasatch-Cache National Forest.

This map shows specific water-quality items and hydrologic data site information which come from QWDATA (Water Quality) and GWSI (Ground Water Information System). Both QWDATA and GWSI are subsystems of NWIS (National Water Inventory System)of the USGS (United States Geologic Survey). This map is for Cache County, Utah. The scope and purpose of NWIS is defined on the web site: http://water.usgs.gov/public/pubs/FS/FS-027-98/

Land and Property Information (LPI’s) Cached map service is a rasterised topographic maps covering NSW. This service contains the current standard Topographic maps from the 1:100,000; 1:50,000 and 1:25,000 series. Where coverage exists at multiple scales the largest scale map is displayed. It compromises the “collars off” tiff images for the current (1:100000, 1:50000 and 1:25000) Topo maps, and replaces the old “Topographic maps (Current Series)” shown in the old six viewer. Land and Property Information (LPI’s) Cached map service is a rasterised topographic maps covering NSW. This service contains the current standard Topographic maps from the 1:100,000; 1:50,000 and 1:25,000 series. Where coverage exists at multiple scales the largest scale map is displayed. It compromises the “collars off” tiff images for the current (1:100000, 1:50000 and 1:25000) Topo maps, and replaces the old “Topographic maps (Current Series)” shown in the old six viewer.

description: This map was produced by the Division of Realty to depict landownership at Cache River National Wildlife Refuge. It was generated from rectified aerial photography, cadastral surveys and recorded documents.; abstract: This map was produced by the Division of Realty to depict landownership at Cache River National Wildlife Refuge. It was generated from rectified aerial photography, cadastral surveys and recorded documents.

The Land Information Ontario (LIO) Topographic Data Cache is a collection of topographic data built using a series of pre-drawn maps. The maps are compressed for fast, seamless display at predefined scales. The topographic data includes constructed and natural features that make up Ontario’s landscape. The cache provides limited data from areas outside Ontario’s boundaries, such as the United States and adjacent provinces and territories. Technical information The cache is updated every year, but the contributing data layers may have different maintenance and update cycles. Some cache layers have been processed in a way that makes it easier for them to be displayed in a mapping product. Other layers are unchanged from the authoritative data. The cartographic symbology used in the data cache is intentionally muted to allow users to showcase their data. The LIO Topographic Data Cache is created from many source datasets as described in the LIO Topographic Data Cache user guide. If you are interested in obtaining this authoritative data, you can download it from Geospatial Ontario. Contact Geospatial Ontario at geospatial@ontario.ca to get a copy of the cache for use in mapping applications. Additional DocumentationLIO Topographic Data Cache - User Guide (DOCX) LIO Topographic Data Cache - layer file linking to web serviceLIO Web Service User GuideStatus On going: Data is continually being updated Maintenance and Update Frequency Annually: Data is updated every year Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

This map shows the transportation connectivity conditions in the South Cache County study area. This data is for visualization purposes only, and should be verified if used for other purposes.

North Dakota GIS Hub cached base map services include a composite of aerial photography and one for boundary, water, and transportation data. The aerial photography includes the latest statewide NAIP data overlaid by the best possible and most recent local imagery. These services are best used with web-based mapping applications but they can also be used with ArcGIS Desktop. Caching has been done to level 20 for selected cities and counties. The base map services utilize Web Mercator Auxiliary Sphere.

Constraints:
Not to be used for navigation, for informational purposes only. See full disclaimer for more information.

The topographic data includes constructed and natural features that make up Ontario’s landscape.

The cache provides limited data from areas outside Ontario’s boundaries, such as the United States and adjacent provinces and territories.

Technical information Two versions of the LIO Topographic Data Cache are available:

The traditional raster version is available for a variety of GIS applications and is updated annually. The vector version is suitable for online web map applications as well as modern GIS software and is updated twice a year. Contributing data layers may have different maintenance and update cycles.

Some cache layers have been processed in a way that makes it easier for them to be displayed in a mapping product. Other layers are unchanged from the authoritative data.

The cartographic symbology used in the data cache is intentionally muted to allow users to showcase their data.The LIO Vector Topographic Data Cache is created from many source datasets as described in the LIO Topographic Data Cache user guide. If you are interested in obtaining this authoritative data, you can download it from the Ontario GeoHub.

Additional Documentation

LIO Topographic Data Cache - User Guide (DOCX)

LIO Vector Topographic Data Cache - Tile Layer

Status

On going: Data is continually being updated

Maintenance and Update Frequency

Biannually: data is updated twice each year

Contact

Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

The Elevation Contours tile cache service displays at large scales contour lines at 3-meter intervals created from Digital Terrain Model (DTM) data points collected during the production of the 1:5,000 Black and White Digital Orthophoto images. See https://www.mass.gov/info-details/massgis-data-elevation-contours-15000 for more details.At smaller scales the service displays contour lines at 30-foot intervals originally developed at a scale of 1:250,000 and distributed by the U.S. Geological Survey. Please see https://www.mass.gov/info-details/massgis-data-elevation-contours-1250000 for more details.For both datasets, the index contours are labeled in feet above sea level.

Tjenesten inneholder topografiske kart i målestokken 1:500 til 1:10M. Tjenesten inneholder kartdata, fbk og vbase data, men ikke matrikkel data. Matrikkel data kan bli funnet i en egen wms-tjeneste kalt Matrikkel Enkel WMS

Example kall: https://cache.kartverket.no/topograatone/v1/wmts/1.0.0/?layer=topograatone&style=default&tilematrixset=utm33n&Service=WMTS&Request=GetTile&Version=1.0.0&Format=image%2Fpng&TileMatrix=10&TileCol=506&TileRow=435

This cached map service provides access to the Kentucky Topographic Map Series (KyTopo) images in a seamless manner. The underlying data will be updated on a periodic basis. This Web Mercator-based service is intended for use in a web mapping framework.

This Land Cover-Land Use Tile Cache may be used for fast display in ArcGIS Online, ArcGIS Desktop, and other applications that can consume tile services.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 hosted tile cache layer, 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.Also available are a Map Service and a Feature Service. They provide attribute query, although they will not display as quickly as the tile cache at smaller (zoomed out) scales.Add the Land Cover-Land Use Legend Map Service to an ArcGIS Online map along with this tile service to have a legend appear.

This map shows the USGS (United States Geologic Survey), NWIS (National Water Inventory System) Hydrologic Data Sites for Cache County, Utah.

The scope and purpose of NWIS is defined on the web site:

http://water.usgs.gov/public/pubs/FS/FS-027-98/

The National Hydrography Dataset (NHD) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000-scale maps and referred to as high resolution NHD, and the other based on 1:100,000-scale maps and referred to as medium resolution NHD. Additional selected areas in the United States are available based on larger scales, such as 1:5,000-scale or greater, and referred to as local resolution NHD. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance and stewardship. For additional information, go to https://nhd.usgs.gov.

The map shows the sky brightness in North Holland in 2011-2012 with greenhouse lighting off. If there is no direct lighting nearby, the night sky determines the degree of darkness. The sky is illuminated by upward beaming light from a city or greenhouse from an area of ​​about 30 kilometers radius. In 2011 and 2012, the brightness of the sky at the point straight up (zenith) was measured at 225 locations in the province of Noord-Holland, as a measure of the 'darkness'. Previous measurements in Amsterdam and on the island of Texel have also been incorporated into this darkness map.

Cache County, UT has a C wealth grade. Median household income: $78,212. Unemployment rate: 2.4%. Income grows 6.0% yearly.