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. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

Prohlížecí služba (WMS) znázorňující stav a postup digitalizace katastrálních území, přehled kladu mapových listů a území s analogovou/vektorovou katastrální mapou. Služba splňuje standard OGC WMS 1.3.0.

OpenDRIVE Map Conversion Services Market Outlook

According to our latest research, the OpenDRIVE Map Conversion Services market size reached USD 412.7 million in 2024, reflecting robust industry growth. The sector is expected to expand at a CAGR of 15.2% from 2025 to 2033, with the market projected to attain a value of USD 1,241.6 million by 2033. This remarkable growth trajectory is primarily driven by the rising adoption of autonomous driving technologies, the proliferation of simulation platforms, and the increasing need for interoperability across various geospatial and navigation systems worldwide.

The primary growth factor fueling the OpenDRIVE Map Conversion Services market is the accelerating demand for high-fidelity digital maps in autonomous vehicle development and advanced driver-assistance systems (ADAS). As automotive manufacturers and technology companies intensify their investments in autonomous mobility, the requirement for accurate, standardized, and interoperable map formats has become paramount. OpenDRIVE, as a widely accepted open standard for road network representation, plays a critical role in simulation, validation, and deployment of autonomous vehicles. The necessity to convert proprietary or legacy map data into the OpenDRIVE format to ensure seamless integration with simulation tools, hardware-in-the-loop (HIL) setups, and real-world vehicle systems is driving the growth of map conversion service providers. Furthermore, the ongoing evolution of OpenDRIVE standards and the introduction of new features to support complex road geometries and dynamic elements underscore the need for specialized conversion and validation services.

Another significant growth driver is the expanding application of OpenDRIVE map conversion services in transportation planning, logistics, and urban development. City planners, logistics companies, and infrastructure developers increasingly rely on digital twins and simulation platforms for traffic management, smart city initiatives, and infrastructure optimization. The conversion of diverse mapping data into the OpenDRIVE format enables seamless interoperability between various simulation, visualization, and planning tools, enhancing the efficiency and accuracy of urban mobility solutions. The integration of OpenDRIVE with other standards, such as OpenSCENARIO and OpenCRG, further amplifies its utility, making map conversion services indispensable for a broad spectrum of stakeholders beyond the automotive industry.

Moreover, the growing focus on quality assurance, data validation, and regulatory compliance within the digital mapping ecosystem is propelling the demand for OpenDRIVE map conversion services. As regulatory bodies and industry consortia establish stringent guidelines for the validation and safety of autonomous systems, the importance of accurate, validated, and compliant map data conversion has surged. Service providers are expanding their offerings to include comprehensive validation and quality assurance processes, ensuring that converted maps meet the highest standards of accuracy, consistency, and reliability. This trend is expected to further accelerate market growth, as stakeholders prioritize safety and compliance in the deployment of advanced mobility solutions.

From a regional perspective, North America currently leads the OpenDRIVE Map Conversion Services market, driven by the presence of major automotive OEMs, technology innovators, and simulation software providers. Europe follows closely, benefiting from strong regulatory support for autonomous vehicle testing and a vibrant ecosystem of research institutions and mobility startups. The Asia Pacific region is witnessing rapid growth, fueled by significant investments in smart city projects, urban mobility, and digital infrastructure development. Latin America and the Middle East & Africa are emerging markets, gradually adopting advanced mapping and simulation technologies to support urbanization and transportation modernization initiatives.

Service Type Analysis

Sixty-seven maps from Indian Land Cessions in the United States, compiled by Charles C. Royce and published as the second part of the two-part Eighteenth Annual Report of the Bureau of American Ethnology to the Secretary of the Smithsonian Institution, 1896-1897 have been scanned, georeferenced in JPEG2000 format, and digitized to create this feature class of cession maps. The mapped cessions and reservations included in the 67 maps correspond to entries in the Schedule of Indian Land Cessions, indicating the number and location of each cession by or reservation for the Indian tribes from the organization of the Federal Government to and including 1894, together with descriptions of the tracts so ceded or reserved, the date of the treaty, law or executive order governing the same, the name of the tribe or tribes affected thereby, and historical data and references bearing thereon, as set forth in the subtitle of the Schedule. Go to this URL for full metadata: https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.TRIBALCEDEDLANDS.xml Each Royce map was georeferenced against one or more of the following USGS 1:2,000,000 National Atlas Feature Classes contained in \NatlAtlas_USGS.gdb: cities_2mm, hydro_ln_2mm, hydro_pl_2mm, plss_2mm, states_2mm. Cessions were digitized as a file geodatabase (GDB) polygon feature class, projected as NAD83 USA_Contiguous_Lambert_Conformal_Conic, which is the same projection used to georeference the maps. The feature class was later reprojected to WGS 1984 Web Mercator (auxiliary sphere) to optimize it for the Tribal Connections Map Viewer. Polygon boundaries were digitized as to not deviate from the drawn polygon edge to the extent that space could be seen between the digitized polygon and the mapped polygon at a viewable scale. Topology was maintained between coincident edges of adjacent polygons. The cession map number assigned by Royce was entered into the feature class as a field attribute. The Map Cession ID serves as the link referencing relationship classes and joining additional attribute information to 752 polygon features, to include the following: 1. Data transcribed from Royce's Schedule of Indian Land Cessions: a. Date(s), in the case of treaties, the date the treaty was signed, not the date of the proclamation; b. Tribe(s), the tribal name(s) used in the treaty and/or the Schedule; and c. Map Name(s), the name of the map(s) on which a cession number appears; 2. URLs for the corresponding entry in the Schedule of Indian Land Cessions (Internet Archive) for each unique combination of a Date and reference to a Map Cession ID (historical references in the Schedule are included); 3. URLs for the corresponding treaty text, including the treaties catalogued by Charles J. Kappler in Indian Affairs: Laws and Treaties (HathiTrust Digital Library), executive order or other federal statute (Library of Congress and University of Georgia) identified in each entry with a reference to a Map Cession ID or IDs; 4. URLs for the image of the Royce map(s) (Library of Congress) on which a given cession number appears; 5. The name(s) of the Indian tribe or tribes related to each mapped cession, including the name as it appeared in the Schedule or the corresponding primary text, as well as the name of the present-day Indian tribe or tribes; and 6. The present-day states and counties included wholly or partially within a Map Cession boundary. During the 2017-2018 revision of the attribute data, it was noted that 7 of the Cession Map IDs are missing spatial representation in the Feature Class. The missing data is associated with the following Cession Map IDs: 47 (Illinois 1), 65 (Tennessee and Bordering States), 128 (Georgia), 129 (Georgia), 130 (Georgia), 543 (Indian Territory 3), and 690 (Iowa 2), which will be updated in the future. This dataset revises and expands the dataset published in 2015 by the U.S. Forest Service and made available through the Tribal Connections viewer, the Forest Service Geodata Clearinghouse, and Data.gov. The 2018 dataset is a result of collaboration between the Department of Agriculture, U.S. Forest Service, Office of Tribal Relations (OTR); the Department of the Interior, National Park Service, National NAGPRA Program; the U.S. Environmental Protection Agency, Office of International and Tribal Affairs, American Indian Environmental Office; and Dr. Claudio Saunt of the University of Georgia. The Forest Service and Dr. Saunt independently digitized and georeferenced the Royce cession maps and developed online map viewers to display Native American land cessions and reservations. Dr. Saunt subsequently undertook additional research to link Schedule entries, treaty texts, federal statutes and executive orders to cession and reservation polygons, which he agreed to share with the U.S. Forest Service. OTR revised the data, linking the Schedule entries, treaty texts, federal statues and executive orders to all 1,172 entries in the attribute table. The 2018 dataset has incorporated data made available by the National NAGPRA Program, specifically the Indian tribe or tribes related to each mapped cession, including the name as it appeared in the Schedule or the corresponding primary text and the name of the present-day Indian tribe or tribes, as well as the present-day states and counties included wholly or partially within a Map Cession boundary. This data replaces in its entirety the National NAGPRA data included in the dataset published in 2015. The 2015 dataset incorporated data presented in state tables compiled from the Schedule of Indian Land Cessions by the National NAGPRA Program. In recent years the National NAGPRA Program has been working to ensure the accuracy of this data, including the reevaluation of the present-day Indian tribes and the provision of references for their determinations. Changes made by the OTR have not been reviewed or approved by the National NAGPRA Program. The Forest Service will continue to collaborate with other federal agencies and work to improve the accuracy of the data included in this dataset. Errors identified since the dataset was published in 2015 have been corrected, and we request that you notify us of any additional errors we may have missed or that have been introduced. Please contact Rebecca Hill, Policy Analyst, U.S. Forest Service, Office of Tribal Relations, at rebeccahill@fs.usda.gov with any questions or concerns with regard to the data included in this dataset.

OpenDRIVE Map Conversion Services Market Outlook

According to our latest research, the OpenDRIVE Map Conversion Services market size globally reached USD 268.7 million in 2024, driven by the rapid advancements in automotive simulation and autonomous driving technologies. The market is showing a robust upward trajectory, with a projected CAGR of 15.2% from 2025 to 2033. By the end of the forecast period, the market is expected to reach USD 822.1 million in 2033. The primary growth factor fueling this expansion is the increasing adoption of high-fidelity digital mapping for autonomous vehicle development and smart city infrastructure.

One of the most significant growth drivers for the OpenDRIVE Map Conversion Services market is the accelerating demand for advanced simulation environments in the automotive sector. With the rise of autonomous vehicles, automotive OEMs and simulation software providers are placing a premium on accurate, interoperable, and high-resolution digital road maps. The OpenDRIVE standard, which provides a comprehensive format for describing road networks, is becoming increasingly integral to testing and validating autonomous driving algorithms. As a result, the need for seamless conversion services that can translate diverse map formats into OpenDRIVE-compatible files is surging. This trend is further amplified by the proliferation of simulation platforms and the necessity for cross-compatibility among different data sources, ensuring a consistent and realistic simulation experience.

Another pivotal factor propelling the OpenDRIVE Map Conversion Services market is the expanding role of digital twins and smart city initiatives in urban planning and traffic management. Urban planners and municipal authorities are leveraging detailed digital maps for infrastructure development, traffic flow optimization, and emergency response planning. OpenDRIVE’s ability to standardize road network data makes it a preferred choice for integrating various data streams, from LiDAR scans to satellite imagery, into a unified format. This capability is particularly valuable in large-scale urban environments where interoperability and scalability are crucial. As cities worldwide invest in intelligent transportation systems and connected infrastructure, the demand for professional map conversion services is expected to rise exponentially.

The increasing emphasis on data quality and validation is also shaping the growth trajectory of the OpenDRIVE Map Conversion Services market. As the complexity of simulation scenarios and autonomous driving use cases grows, so does the need for accurate and error-free map data. Service providers are investing in advanced validation tools and quality assurance processes to ensure that converted maps meet stringent industry standards. This focus on reliability is not only critical for regulatory compliance but also for the safety and performance of autonomous vehicles. Furthermore, the integration of artificial intelligence and machine learning in map conversion workflows is enhancing efficiency, reducing turnaround times, and minimizing human error, thereby driving further adoption across end-user segments.

From a regional perspective, North America and Europe are currently leading the OpenDRIVE Map Conversion Services market, owing to their early adoption of autonomous vehicle technologies and robust automotive research ecosystems. However, Asia Pacific is emerging as a formidable contender, with countries like China, Japan, and South Korea making significant investments in smart mobility and digital infrastructure. These regions are witnessing increased collaboration between government bodies, research institutes, and private sector players to accelerate the deployment of next-generation transportation solutions. Latin America and the Middle East & Africa are also showing steady growth, albeit from a smaller base, as they gradually embrace digital mapping for urban planning and traffic management. The global competitive landscape is marked by a mix of established technology providers and innovative startups, each striving to capture a share of this rapidly expanding market.

Service Type Analysis

The Service Type segment in the OpenDRIVE Map Conversion Services market encompasses a range of offerings, including Format Conversion, Data Validation, Customization, Integration, and Others. Format Conversion remains the cornerstone of this segment, as organizations increasing

OpenDRIVE Map Conversion Services Market Outlook

According to our latest research, the Global OpenDRIVE Map Conversion Services market size was valued at $178.5 million in 2024 and is projected to reach $521.7 million by 2033, expanding at a CAGR of 12.7% during 2024–2033. The primary growth driver for the OpenDRIVE Map Conversion Services market globally is the rapid proliferation of autonomous vehicle development and advanced driver-assistance systems (ADAS), which heavily depend on high-fidelity, interoperable digital maps for simulation, testing, and real-world deployment. The increasing demand for seamless integration of mapping data across diverse platforms and the necessity to ensure map accuracy and compatibility are compelling automotive OEMs, simulation companies, and urban planners to invest in robust OpenDRIVE map conversion solutions. This trend is further amplified by the growing complexity of urban mobility ecosystems and the need for standardized, high-quality digital mapping data to facilitate next-generation mobility solutions.

Regional Outlook

North America holds the largest share of the OpenDRIVE Map Conversion Services market, accounting for approximately 38% of the global market value in 2024. This dominance is attributed to the region’s mature automotive and technology sectors, significant investments in autonomous vehicle research, and a strong presence of leading mapping service providers and automotive OEMs. The United States, in particular, has established itself as a hub for simulation and testing of autonomous driving technologies, with numerous pilot programs and partnerships between tech companies, automotive manufacturers, and government agencies. Favorable regulatory frameworks and a high adoption rate of advanced mobility solutions further bolster the region’s market leadership. Additionally, North America’s robust IT infrastructure and early adoption of digital mapping standards ensure that OpenDRIVE map conversion services are in high demand for both commercial and municipal applications.

The Asia Pacific region is projected to be the fastest-growing market for OpenDRIVE Map Conversion Services, with a forecasted CAGR of 15.3% during 2024–2033. This impressive growth trajectory is fueled by substantial investments in smart mobility, rapid urbanization, and the emergence of major automotive manufacturing hubs in countries such as China, Japan, and South Korea. Governments in the region are actively supporting autonomous vehicle trials and urban infrastructure modernization, driving the need for high-quality digital mapping and data integration services. The proliferation of local mapping service providers, coupled with the expansion of global automotive OEMs into Asia Pacific, is creating a vibrant ecosystem for map conversion solutions. As the region continues to prioritize smart city initiatives and next-generation transportation networks, the demand for OpenDRIVE map conversion services is expected to surge, particularly in metropolitan areas undergoing rapid technological transformation.

Emerging economies in Latin America and Middle East & Africa are witnessing gradual adoption of OpenDRIVE Map Conversion Services, though growth is tempered by challenges such as limited digital infrastructure, lower penetration of autonomous vehicle technology, and regulatory uncertainties. However, localized demand is rising as urban centers in these regions begin to invest in smart city projects and advanced traffic management systems. Governments are recognizing the value of standardized digital mapping for urban planning and mobility optimization. Policy reforms and international collaborations are slowly paving the way for increased adoption, but the market is still in its nascent stages compared to North America, Europe, and Asia Pacific. Overcoming infrastructural and regulatory hurdles will be key to unlocking the full potential of OpenDRIVE map conversion services in these regions over the forecast period.

Report Scope

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. James W. Sewall Company developed a complete GIS coverage for the park and revised the preliminary vegetation map classes to better match the results from the cluster analysis and NMS ordination. Polygons representing vegetation stands were digitized on-screen in ArcGIS 8.3, and later in ArcMap 9.1 and 9.2, using lines drawn on the acetate overlays, base layers of 1:8,000 CIR aerial photography, orthorectified photo composite image, and plot location and data. The minimum map unit used was 0.5 ha (1.24 ac). Stereo pairs were used to double check stand signatures during the digitizing process. Photo interpretation and polygon digitization extended outside the NPS boundary, especially where vegetation units were arbitrarily truncated by the boundary. Each polygon was attributed with the name of a vegetation map class or an Anderson Level II land use category based on plot data, field observations, aerial photography signatures, and topographic maps. Data fields identifying the USNVC association inclusions within the vegetation map class were attributed to the vegetation polygons in the shapefile. The GIS coverages and shapefiles were projected to Universal Transverse Mercator (UTM) Zone 19 North American Datum 1983 (NAD83). FGDC compliant metadata (FGDC 1998a) were created with the NPS-MP ESRI extension and included with the vegetation map shapefile. A photointerpretation key to the map classes for the 2006 draft vegetation map is included as Appendix A. The composite vegetation coverage was clipped to the NPS 2002 MIMA boundary shapefile for accuracy assessment (AA). After the 2006 vegetation map was completed, the thematic accuracy of this map was assessed.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Parcel boundaries in Onslow County with no related tax information except Parid & Map Number. The parcel data is continuously updated and maintained by Onslow County. New parcels will show up in the map service within minutes of being digitized by the County GIS. Any questions please call the Onslow County GIS Department at 1-910-937-1190, Monday - Friday 8am - 5pm.

Document Scanning Services Market size was valued at USD 4.69 Billion in 2024 and is projected to reach USD 6.88 Billion by 2031, growing at a CAGR of 4.9% from 2024 to 2031.Global Document Scanning Services Market DriversThe document scanning services market is propelled by various crucial aspects that signify the growing need for effective document management solutions in the present digital age. The reasons behind these drives include technical improvements, regulatory compliance requirements, cost-effectiveness, environmental concerns, and the desire for improved productivity.Technological advances are crucial in driving the growth of the document scanning services industry. Technological advancements, including high-speed scanners, optical character recognition (OCR) software, and cloud-based document management systems, have greatly enhanced the effectiveness and precision of document scanning procedures. These technologies provide the rapid conversion of vast quantities of documents into digital format, ensuring the accuracy and reliability of the data. As a result, document scanning services are increasingly being embraced by different businesses.In addition, regulatory compliance requirements enforce strict standards for the retention, storage, and accessibility of documents. Industries such as healthcare, banking, legal, and government are required to comply with regulatory standards such as HIPAA, GDPR, Sarbanes-Oxley, and other relevant regulations. Document scanning services assist organisations in adhering to these rules by guaranteeing secure storage, effortless retrieval, and appropriate destruction of confidential information, hence reducing legal risks and penalties.Cost-effectiveness is a significant determinant for the growth of the document scanning services industry. Businesses are progressively acknowledging the enduring financial benefits linked to the digitization of their papers. Companies can achieve cost savings and efficiency gains by adopting digital document management solutions, which eliminate the need for physical storage space, cut printing expenses, and minimise the amount of manual labour involved in managing documents. In addition, digitised documents enable remote access, collaboration, and workflow automation, resulting in enhanced operational efficiency and decreased overhead costs.Environmental considerations also play a role in the expansion of document scanning services. The adoption of paperless workflows supports sustainability objectives by decreasing paper usage, minimising waste production, and reducing carbon emissions linked to conventional document management methods. Document scanning services allow organisations to adopt environmentally friendly methods and improve their corporate social responsibility efforts, which can attract environmentally concerned consumers and stakeholders.

Map Streaming Services Market Outlook

According to our latest research, the global map streaming services market size reached USD 6.72 billion in 2024, driven by the rapid proliferation of connected devices and the growing reliance on real-time geospatial data across industries. The market is exhibiting robust momentum, with a recorded compound annual growth rate (CAGR) of 18.4% from 2025 to 2033. By the end of 2033, the map streaming services market is forecasted to achieve a value of USD 36.8 billion, reflecting the substantial demand for dynamic mapping solutions in navigation, fleet management, and location-based service applications. The market’s growth is fueled by technological advancements, increasing urbanization, and the expanding adoption of smart mobility solutions globally.

The primary growth driver for the map streaming services market is the increasing integration of real-time mapping and navigation functionalities into consumer and enterprise applications. As mobile devices become omnipresent, users and businesses alike require up-to-date mapping data for navigation, delivery tracking, and ride-hailing services. The proliferation of Internet of Things (IoT) devices and connected vehicles further intensifies the need for seamless, always-available map streaming, enabling applications such as automated route optimization, real-time traffic monitoring, and geofencing. Furthermore, the advancement of 5G networks is significantly enhancing the speed and reliability of map streaming, allowing for richer, more interactive mapping experiences and supporting the development of autonomous vehicle ecosystems.

Another key factor propelling the map streaming services market is the surge in demand for location-based services (LBS) across diverse sectors. Retailers leverage map streaming to offer hyperlocal promotions, while logistics companies depend on real-time geospatial data for efficient fleet management and asset tracking. The healthcare sector is also adopting map streaming services for emergency response and patient transport optimization. The integration of artificial intelligence and machine learning into mapping platforms is enabling predictive analytics, personalized recommendations, and improved accuracy, further expanding the applicability of these services. As businesses strive to enhance operational efficiency and customer engagement, the adoption of advanced map streaming services is expected to accelerate.

The evolution of smart cities and the digitization of public infrastructure are also contributing to the expansion of the map streaming services market. Governments and municipal authorities are leveraging real-time mapping solutions for urban planning, traffic management, and disaster response. The deployment of sensors and connected devices in public spaces generates vast amounts of spatial data, which is streamed and visualized through advanced mapping platforms. This trend is particularly pronounced in regions experiencing rapid urbanization, where the need for efficient transportation systems and responsive public services is paramount. As urban environments become increasingly interconnected, the demand for scalable and reliable map streaming services will continue to rise.

From a regional perspective, North America currently leads the map streaming services market, owing to the presence of major technology providers, high smartphone penetration, and early adoption of connected vehicle technologies. Europe follows closely, supported by robust investments in smart mobility and public infrastructure digitization. The Asia Pacific region is poised for the fastest growth, driven by burgeoning urban populations, expanding e-commerce, and government initiatives aimed at building smart cities. Latin America and the Middle East & Africa are also witnessing rising adoption, as businesses and governments recognize the value of real-time geospatial intelligence. Regional disparities in infrastructure and digital maturity, however, may influence the pace of adoption and market development.

Component Analysis

The component segment of the map streaming services market is divided into software, hardware, and services, each playing a crucial role in the delivery and consumption of map data. Software forms the backbone of map streaming services, encompassing mapping APIs, geospatial analytics engines, and visualization tools that enable real-time rendering and interaction with spatial data

Status: COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at https://cms3.tucsonaz.gov/planning/plans/ Supplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only. 2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan. Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactJohn BeallCity of Tucson Development Services520-791-5550John.Beall@tucsonaz.govUpdate FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

A digital map of all North Carolina water system service-area boundaries based on information available in 2019. See the readme file for information on data sources, digitization process, coverage, and important provisos. When using this geopackage, please cite: Gonsenhauser, R., Hansen, K., Grimshaw, W., Morris, J., Albertin, K. and Mullin, M. (2020), Digitizing a Statewide Map of Community Water System Service Areas. J Am Water Works Assoc, 112: 56-61. https://doi.org/10.1002/awwa.1595

Reconstructing past landscapes from historical maps requires quantifying the accuracy and completeness of these sources. The accuracy and completeness of two historical maps of the same period covering the same area in Israel were examined: the 1:63,360 British Palestine Exploration Fund map (1871-1877) and the 1:100,000 French Levés en Galilée (LG) map (1870). These maps cover the mountainous area of the Galilee (northern Israel), a region with significant natural and topographical diversity, and a long history of human presence. Land-cover features from both maps, as well as the contours drawn on the LG map, were digitized. The overall correspondence between land-cover features shown on both maps was 59% and we found that the geo-referencing method employed (transformation type and source of control points) did not significantly affect these correspondence measures. Both maps show that in the 1870s, 35% of the Galilee was covered by Mediterranean maquis, with less than 8% of the area used for permanent agricultural cropland (e.g., plantations). This article presents how the reliability of the maps was assessed by using two spatial historical sources, and how land-cover classes that were mapped with lower certainty and completeness are identified. Some of the causes that led to observed differences between the maps, including mapping scale, time of year, and the interests of the surveyors, are also identified.

HD Map Update Services for Work Zones Market Outlook

According to our latest research, the Global HD Map Update Services for Work Zones market size was valued at $1.3 billion in 2024 and is projected to reach $6.7 billion by 2033, expanding at a CAGR of 19.8% during 2024–2033. The rapid proliferation of autonomous and connected vehicles is a major growth factor, as these vehicles require highly accurate, up-to-date maps to safely navigate dynamic work zones and construction areas. As governments and private entities invest heavily in intelligent transportation systems and smart infrastructure, the need for reliable HD map update services for work zones is increasing dramatically worldwide.

Regional Outlook

North America currently holds the largest share of the HD Map Update Services for Work Zones market, accounting for approximately 38% of global revenue in 2024. This dominance stems from the region’s mature automotive and technology sectors, widespread adoption of autonomous and connected vehicle technologies, and robust government initiatives supporting smart mobility. The United States, in particular, has seen significant investments in infrastructure digitization and stringent safety regulations, driving demand for real-time and accurate map updates in work zones. Major automotive OEMs and mapping service providers headquartered in North America further reinforce the region’s leadership through continuous innovation, strategic partnerships, and pilot deployments of advanced HD mapping solutions across key urban corridors and highway networks.

The Asia Pacific region is poised to be the fastest-growing market, projected to expand at a remarkable CAGR of 23.2% between 2024 and 2033. This growth is fueled by the rapid urbanization, escalating vehicle production, and ambitious government-led smart city initiatives across countries such as China, Japan, and South Korea. The surge in investments by both public and private sectors in next-generation transportation infrastructure, coupled with the increasing penetration of autonomous and connected vehicles, is creating fertile ground for HD map update services tailored for work zones. Additionally, the presence of tech-savvy populations and the emergence of local mapping startups are accelerating the adoption of innovative map update methods, including crowdsourced and sensor-based approaches.

Emerging economies in Latin America and Middle East & Africa are gradually embracing HD Map Update Services for Work Zones, though adoption is at an earlier stage compared to established markets. These regions face unique challenges such as fragmented infrastructure, limited digitalization, and regulatory uncertainties. However, localized demand is rising as governments recognize the importance of smart mobility for economic growth and urban development. Policy reforms, targeted investments in pilot projects, and partnerships with global technology providers are helping bridge adoption gaps. Nevertheless, the pace of market expansion remains contingent on overcoming infrastructural bottlenecks and establishing clear regulatory frameworks for data sharing and road safety.

Report Scope

1922 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1922. In 2002, NOAA published digitized shorelines for T-sheet (T-3920), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

This data archive is a collection of GIS files and FGDC metadata prepared in 1995 for the Northampton County Planning Office by the Virginia Coast Reserve LTER project at the University of Virginia with support from the Virginia Department of Environmental Quality (DEQ) and the National Science Foundation (NSF). Original data sources include: 1:100,000-scale USGS digital line graph (DLG) hydrography and transportation data; 1:6,000-scale boundary, road, and railroad data for the town of Cape Charles from VDOT; 1:190,000-scale county-wide general soil map data and 1:15,540-scale detailed soil data for the Cape Charles area digitized from printed USDA soil survey maps; a land use and vegetation cover dataset (30 m. resolution) created by the VCRLTER derived from a 1993 Landsat Thematic Mapper satellite image; 1:20,000-scale plant association maps for 10 seaside barrier and marsh islands between Hog and Smith Islands, inclusive, prepared by Cheryl McCaffrey for TNC in 1975 and published in the Virginia Journal of Science in 1990; and 1993 colonial bird nesting site data collected by The Center for Conservation Biology (with partners The Nature Conservancy, College of William and Mary, University of Virginia, USFWS, VA-DCR, and VA-DGIF). Contents: HYDROGRAPHY Based on USGS 1:100,000 Digital Line Graph (DLG) data. Files: h100k_arc_u84 (streams, shorelines, etc.) and h100k_poly_u84 (marshes, mudflats, etc.). Note that the hydrographic data has been superseded by the more recent and more detailed USGS National Hydrography Dataset, available for the entire state of Virginia at "ftp://nhdftp.usgs.gov/DataSets/Staged/States/FileGDB/HighResolution/NHDH_VA_931v210.zip" (see http://nhd.usgs.gov/data.html for more information). A static 2013 version of the NHD data that includes shapefiles extracted from the original ESRI geodatabase format data and covering just the watersheds of the Eastern Shore of VA can also be found in the VCRLTER Data Catalog (dataset VCR14223). TRANSPORTATION Based on USGS 1:100,000 Digital Line Graph (DLG) data for the full county, and 1:6,000 VDOT data for the Cape Charles township. Files: 1:100k Transportation (lines) from USGS DLG data: rtf100k_arc_u84 (roads), rrf100k_arc_u84 (railroads), and mtf100k_arc_u84 (airports and utility transmission lines). Files: 1:6000 street, boundary, and rail line data for the town of Cape Charles, 1984, prepared by Virginia Department of Highways and Transportation Information Services (Division 1221 East Broad Street, Richmond, Virginia 23219). Streets correct through December 31,1983. Georeferencing corrected in 2014 for shapefiles only, using same methodology described for VCR14218 dataset. File : town_u84_adj (town_arc_u84old is the older unadjusted data). Note that the transportation data has been superseded by more recent and more detailed data contained in dataset VCR14222 of the VCRLTER Data Catalog. The VCR14222 data contains 2013 U.S. Census Bureau TIGER/Line road and airfield data supplemented by railroad and transmission lines digitized from high resolution VGIN-VBMP 2013 aerial imagery and additionally has boat launch locations not available here. SOILS General soil map for Northampton county (1:190k), and detailed soil map for Cape Charles and Cheriton areas (1:15,540) from published the USDA Soil Conservation Service's 1989 "Soil Survey of Northampton County, Virginia" digitized at UVA by Ray Dukes Smith: soilorig_poly_u84 (uses original shorelines from source maps), soil_poly_u84 (substitutes shorelines from 1993 landcover classification data), and cc_soil_poly_u84 (Cape Charles & Cheriton detailed data, map sheets 13 and 14). Note that the soil data has been superseded by more recent and more detailed SSURGO soil data from the USDA's Natural Resources Conservation Service (NRCS), which has seamless soil data from the 1:15,540 map series in tabular and GIS formats for the full county, as well as for all counties in VA and other states. A static 2013 version of the SSURGO data that contains merged data for Accomack and Northampton Counties can be found in the VCRLTER Data Catalog (dataset VCR14220). LANDUSE/LANDCOVER VCR Landuse and Vegetation Cover, 1993, created by Guofan Shao (VCRLTER) based on 30m resolution Landsat Thematic Mapper (TM) satellite imagery taken on July 28, 1993. Cropped to include just Northampton County. Landcover is divided into 5 classifications: (1) Forest or shrub, (2) Bare Land or Sand, (3) Planted Cropland, Grassland, or Upland Marsh, (4) Open Water, and (5) Low Salt Marsh. File = nhtm93s3_poly_u84. No spatial adjustments necessary. An outline of the county showing the shorelines based on the above 1993 TM classification is included as the shapefile:outline_poly_u84; however, no spatial adjustment has been applied. Note that a similar landuse/landcover classification based on the same 1993 Landsat

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. Vegetation map development for KNRI has somewhat different protocols than for other Parks. Normally photointerpretation is preceded by extensive field work which includes plot selection and vegetation sampling using detailed descriptions which are subsequently analyzed using ordination and other statistical techniques. The data are then summarized and association descriptions are assigned to each plot or, if the association is previously unrecognized, then a new association name is assigned. Subsequently, the plots locations are compared to its photographic signature and a photointerpretive key is developed. Given the very small size of KNRI and the extensive historical impact and alteration of the vegetation a simplified technique was used. NatureServe developed a list of potential vegetation types prior to any field work. This list was referenced during the field visit and modified after comparison of site characteristics and vegetation descriptions. Aerial photographs were viewed prior to the field visit and areas of like signature were differentiated. All vegetation and land-use information was then transferred to a GIS database using the latest grayscale USGS digital orthophoto quarter-quads as the base map and using a combination of on-screen digitizing and scanning techniques. Overall thematic map accuracy for the Park is considered 100% as all interpreted polygons received a filed visit for verification.

The entire Vermont extent of the National Flood Hazard Layer (NFHL) as acquired 12/15/15 from the FEMA Map Service Center msc.fema.gov upon publication 12/2/2015 and converted to VSP.The FEMA DFIRM NFHL database compiles all available officially-digitized Digital Flood Insurance Rate Maps. This extract from the FEMA Map Service Center includes all of such data in Vermont including counties and a few municipalities. This data includes the most recent map update for Bennington County effective 12/2/2015.DFIRM - Letter of Map Revision (LOMR) DFIRM X-Sections DFIRM Floodways Special Flood Hazard Areas (All Available)