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Our location data powers the most advanced address validation solutions for enterprise backend and frontend systems.

A global, standardized, self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries.

All geospatial data for address data validation is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Address Validation at Zip Code Level Database (Geospatial data)

• Address capture and address validation

• Address autocomplete

• Address verification

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Product Features

• Dedicated features to deliver best-in-class user experience

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

Data export methodology

Our location data packages are offered in variable formats, including .csv. All geospatial data for address validation are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why do companies choose our location databases

• Enterprise-grade service

• Full control over security, speed, and latency

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

• Seamlessly integrated into your software

Note: Custom address validation packages are available. Please submit a request via the above contact button for more details.

MAP Data Authoring and Validation Market Outlook

According to our latest research, the global MAP Data Authoring and Validation market size reached USD 2.47 billion in 2024, propelled by the increasing demand for accurate geospatial data across numerous industries. The market is experiencing robust growth, with a CAGR of 13.2% anticipated from 2025 to 2033, projecting the market to reach USD 7.23 billion by 2033. This surge is primarily driven by the proliferation of smart city projects, autonomous vehicle development, and the integration of advanced mapping solutions in various sectors, as per our most recent analysis.

One of the most significant growth factors for the MAP Data Authoring and Validation market is the escalating adoption of location-based services and real-time navigation systems. Industries such as automotive, telecommunications, and urban planning are increasingly reliant on precise mapping data to enable advanced functionalities, including autonomous driving, network planning, and infrastructure development. The evolution of smart transportation and the need for enhanced situational awareness in both civilian and defense sectors further amplify the demand for high-quality map data. Additionally, the integration of artificial intelligence and machine learning algorithms in map data authoring processes has significantly improved the accuracy and speed of data validation, making these solutions indispensable for organizations aiming to maintain a competitive edge in a data-driven landscape.

Another prominent driver is the growing importance of geographic information systems (GIS) in decision-making processes across multiple verticals. As businesses and governments increasingly leverage spatial data analytics for strategic planning, the need for robust map data authoring and validation tools has surged. The expansion of 5G networks and the Internet of Things (IoT) ecosystem has also necessitated the deployment of detailed and up-to-date geospatial datasets to optimize network performance and resource allocation. Furthermore, regulatory frameworks mandating the use of accurate geospatial data for safety and compliance purposes in sectors such as aviation and maritime are fueling the adoption of advanced map data validation solutions.

The market is also witnessing substantial investments in research and development aimed at enhancing the capabilities of map data authoring platforms. Technological advancements, such as cloud-based geospatial data management and the incorporation of real-time data feeds from satellites, drones, and sensors, are transforming the landscape of map data creation and validation. These innovations facilitate the generation of high-resolution, dynamic maps that are critical for applications ranging from urban mobility to environmental monitoring. As the complexity and volume of geospatial data continue to grow, the demand for scalable and automated map data authoring and validation solutions is expected to escalate, further accelerating market expansion.

Regionally, North America continues to dominate the MAP Data Authoring and Validation market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology providers, high adoption rates of advanced mapping solutions, and substantial investments in smart infrastructure projects are key factors driving regional growth. Asia Pacific, in particular, is emerging as a high-growth region, fueled by rapid urbanization, government initiatives to digitize infrastructure, and the expansion of automotive and telecommunications sectors. Meanwhile, Europe’s focus on sustainable urban development and stringent regulatory standards for geospatial data accuracy further bolster market prospects in the region. Latin America and the Middle East & Africa, while currently accounting for smaller shares, are expected to witness increased adoption of map data solutions as digital transformation initiatives gain momentum.

Component Analysis

The MAP Data Authoring and Validation market is segmented by component into Software and Services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of map data authoring and validation, offering robust platforms for data creation, editing, and verification. These tools leverage advanced algorithms, machine learning, and artificial intelligence to streamline the proce

Data producers or those who maintain parcel data can use this tool to validate their data against the state Geospatial Advisory Committee (GAC) Parcel Data Standard. The validations within the tool were originally created as part of a MetroGIS Regional Parcel Dataset workflow.

Counties using this tool can obtain a schema geodatabase from Parcel Data Standard page hosted by MnGeo (link below). All counties, cities or those maintaining authoritative data on a local jurisdiction's behalf, are encouraged to use and modify the tool as needed to support local workflows.

Parcel Data Standard Page
http://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

Specific validation information and tool requirements can be found in the following documents included within this resource.
Readme_HowTo.pdf
Readme_Validations.pdf

This dashboard depicts the status of NG9-1-1 data submitted to Datmark VEP, a data validation software solution.

MAP Data Authoring and Validation Market Outlook

According to our latest research, the global MAP Data Authoring and Validation market size in 2024 is valued at USD 4.28 billion, reflecting the rapidly increasing demand for accurate mapping data across multiple industries. The market is experiencing a robust growth trajectory, with a recorded CAGR of 13.2% from 2025 to 2033. By 2033, the MAP Data Authoring and Validation market is forecasted to reach an impressive USD 12.42 billion. This growth is primarily driven by the proliferation of autonomous vehicles, smart city initiatives, and the critical importance of precise geospatial data in modern applications, as per our latest research findings.

The MAP Data Authoring and Validation market is witnessing exponential expansion due to the escalating integration of advanced mapping solutions in the automotive and transportation sectors. The surge in demand for autonomous vehicles and connected mobility solutions necessitates highly accurate and real-time map data, which, in turn, propels the adoption of authoring and validation tools. Furthermore, the evolution of smart cities and IoT-enabled urban infrastructure is generating an unprecedented need for reliable geospatial data to optimize city planning, utility management, and emergency response systems. These factors collectively contribute to the sustained growth of the market, as organizations increasingly prioritize data accuracy, consistency, and validation to enhance operational efficiency and decision-making processes.

Another significant growth factor for the MAP Data Authoring and Validation market is the rapid digital transformation across industries, particularly in logistics, utilities, and government sectors. The shift towards digital workflows and automation, coupled with regulatory mandates for accurate geospatial information, is compelling enterprises to invest in sophisticated software and services for map data creation and validation. Additionally, the rise of location-based services, mobile mapping applications, and real-time navigation solutions is accelerating the market’s momentum. The growing adoption of cloud-based platforms further amplifies accessibility and scalability, enabling organizations to manage large volumes of spatial data efficiently while ensuring compliance with evolving data standards.

Technological advancements are also playing a pivotal role in driving the MAP Data Authoring and Validation market. The integration of artificial intelligence, machine learning, and big data analytics into mapping solutions is enhancing the precision, automation, and speed of data authoring and validation processes. These technologies facilitate the rapid detection and correction of errors, automate data enrichment, and enable predictive analytics for proactive decision-making. Moreover, the increasing interoperability of mapping platforms with other enterprise systems, such as ERP and GIS, is unlocking new opportunities for cross-functional data utilization and business intelligence. As industries continue to embrace digital innovation, the demand for advanced MAP Data Authoring and Validation solutions is expected to accelerate further.

From a regional perspective, the MAP Data Authoring and Validation market exhibits robust growth across North America, Europe, and Asia Pacific, with North America currently holding the largest market share. The region’s dominance is attributed to the early adoption of advanced mapping technologies, strong presence of automotive and technology giants, and significant investments in smart infrastructure projects. Europe follows closely, driven by stringent regulatory frameworks and a thriving automotive sector. Meanwhile, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, expanding transportation networks, and government-led digitalization initiatives. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a more measured pace, as regional players increase their focus on geospatial data management and smart city development.

BIEN data validation and standardization tools.

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. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

We provide geospatial data across mobile apps with 30+ enriched attributes, including demographics, devices, app activity, intent and geospatial insights. We help marketers, agencies, and platforms build precise geospatial audience segments, optimize geospatial targeting, attribute locations, and understand cross-device journeys. Our continuously updated geospatial datasets deliver real-time geospatial insights that power smarter geospatial campaigns and future-ready strategies.

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This data includes all datasets and codes for adversarial validation in geospatial machine learning prediction and corresponding experiments. Except for datasets (Brazil Amazon basion AGB dataset and synthetic species abundance dataset) and code, Reademe.txt explains each file's meaning.

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. A draft hard copy vegetation map at the 1:12,000 scale was printed and checked against the interpreted aerial photographs. As a final internal accuracy check, we applied photointerpretative observations and classification relevés over the vegetation map to determine if the polygon labels matched the field data. Finally, map validation occurred prior to the accuracy assessment. Staff from RSGIG conducted a field trip in conjunction with other meetings in Flagstaff, AZ in January 2001 to refine and assess the initial mapping effort. On this trip we collected additional photointerpretative observations and ground-truthed aerial photograph signatures using landmarks and GPS waypoints. Map classes were lumped or split to account for inadequacies in the final photointerpretation. Metadata are required for all spatial data produced by the federal government. RSGIG used SIMMS™ software and CPRS used ArcCatalogue software to create the FGDC-compliant metadata files attached to the spatial databases and to this report (see Appendix A). The metadata files explain the vegetation coverage and ancillary coverages created by RSGIG, the classification relevé data coverage created by CPRS, and the accuracy assessment observation data created by CPRS.

Data producers or those who maintain park and trail data can use this tool to validate their data against the MetroGIS Park and Trail data standard. The validations within the tool were created to support the Metro Park and Trail datasets (see associated datasets below).

MetroGIS Park and Trail Data Page
https://www.metrogis.org/projects/park-and-trail.aspx

Specific validation information and tool requirements can be found in the following documents included within this resource.
Readme_HowTo.pdf
Readme_Validations.pdf

This is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation, dissimilarity quantification method, and dissimilarity-adaptive cross-validation.

Utility GIS Data Quality Services Market Outlook

According to our latest research, the global Utility GIS Data Quality Services market size reached USD 1.37 billion in 2024 and is projected to grow at a robust CAGR of 12.8% from 2025 to 2033, reaching an estimated USD 4.08 billion by 2033. The primary growth factor driving this market is the increasing demand for accurate, real-time geospatial data to optimize utility operations and comply with stringent regulatory requirements. The surge in smart grid deployments and digital transformation initiatives across the utility sector is significantly boosting the adoption of specialized GIS data quality services.

One of the core growth drivers for the Utility GIS Data Quality Services market is the accelerating shift toward digital infrastructure in the utilities sector. Utilities, including electric, water, and gas providers, are increasingly relying on Geographic Information Systems (GIS) for asset management, network optimization, and outage management. However, the effectiveness of these systems is heavily dependent on the accuracy and integrity of the underlying data. As utilities modernize their grids and expand their service offerings, the need for comprehensive data cleansing, validation, and enrichment becomes paramount. This trend is further amplified by the proliferation of IoT devices and smart meters, which generate vast volumes of spatial and operational data, necessitating advanced GIS data quality services to ensure consistency and reliability across platforms.

Another significant factor propelling market growth is the evolving regulatory landscape. Governments and regulatory bodies worldwide are imposing stricter requirements on utilities to maintain high-quality, up-to-date geospatial records for compliance, safety, and disaster response. Inaccurate or outdated GIS data can lead to costly penalties, service interruptions, and reputational damage. As a result, utility companies are investing heavily in data quality services to achieve regulatory compliance and mitigate operational risks. The integration of artificial intelligence and machine learning technologies into GIS data quality processes is also enhancing the efficiency and accuracy of data validation, migration, and integration, further supporting market expansion.

Moreover, the increasing complexity of utility networks and the growing emphasis on sustainability and resilience are driving utilities to adopt advanced GIS data quality services. Utilities are under pressure to optimize resource allocation, minimize losses, and enhance customer service, all of which require high-quality geospatial data. The rise of distributed energy resources, such as solar and wind, and the need to manage bi-directional power flows are adding new layers of complexity to utility networks. GIS data quality services enable utilities to maintain a comprehensive, accurate digital twin of their infrastructure, supporting better planning, predictive maintenance, and rapid response to outages or emergencies. These factors collectively contribute to the sustained growth of the Utility GIS Data Quality Services market.

From a regional perspective, North America currently dominates the Utility GIS Data Quality Services market, driven by large-scale investments in smart grid projects and the presence of major utility companies adopting advanced GIS solutions. However, Asia Pacific is expected to witness the fastest growth over the forecast period, fueled by rapid urbanization, infrastructure development, and government initiatives to modernize utility networks. Europe also presents significant opportunities, with increasing focus on sustainability, regulatory compliance, and cross-border energy integration. The Middle East & Africa and Latin America are gradually catching up, with investments in utility infrastructure and digital transformation initiatives gaining momentum. Overall, the global market is poised for substantial growth, underpinned by technological advancements, regulatory mandates, and the evolving needs of the utility sector.

Service Type Analysis

The Utility GIS Data Quality Services market is segmented by service type into data cleansing, data validation, data integration, data migration, data enrichment, and others. Data cleansing services form the backbone of this segment, as they address the critical need to remove inaccuracies, inconsistencies, and redundancies from utility GIS databases. Wit

Map Data Quality Assurance Market Outlook

According to our latest research, the global Map Data Quality Assurance market size in 2024 stands at USD 1.87 billion, exhibiting robust demand across industries reliant on geospatial accuracy. The market is projected to grow at a CAGR of 13.4% from 2025 to 2033, reaching a forecasted value of USD 5.74 billion by 2033. This growth is primarily driven by the increasing integration of geospatial data into critical business operations, rapid urbanization, and the proliferation of location-based services. As per our latest research, advancements in artificial intelligence and automation are further accelerating the need for high-quality map data validation and assurance across diverse sectors.

The burgeoning demand for enhanced accuracy in digital mapping is a primary growth factor for the Map Data Quality Assurance market. Organizations across transportation, logistics, urban planning, and emergency response sectors are increasingly dependent on precise geospatial data to optimize operations and decision-making. The surge in autonomous vehicles and smart city projects has amplified the emphasis on reliable and up-to-date map data, necessitating rigorous quality assurance processes. Furthermore, the rise of real-time navigation and location-based services, fueled by the proliferation of mobile devices and IoT sensors, has made map data quality a mission-critical component for businesses seeking to enhance customer experiences and operational efficiency.

Another significant driver contributing to the growth of the Map Data Quality Assurance market is the continuous evolution of data collection technologies. The integration of satellite imagery, aerial drones, and advanced remote sensing techniques has led to an exponential increase in the volume and complexity of geospatial data. As a result, organizations are investing heavily in sophisticated quality assurance solutions to ensure data consistency, accuracy, and reliability. Regulatory and compliance requirements, especially in sectors such as government, utilities, and environmental monitoring, have further heightened the need for robust quality assurance frameworks, thereby bolstering market expansion.

Technological advancements in artificial intelligence, machine learning, and automation are revolutionizing the Map Data Quality Assurance landscape. Automated data validation, anomaly detection, and error correction tools are enabling organizations to process large datasets with greater speed and precision, significantly reducing manual intervention and associated costs. The shift towards cloud-based solutions has democratized access to high-quality map data assurance tools, making them affordable and scalable for organizations of all sizes. The growing trend of integrating geospatial analytics with enterprise resource planning (ERP) and customer relationship management (CRM) systems is also driving demand for seamless, accurate, and real-time map data validation.

From a regional perspective, North America remains the most dominant market for Map Data Quality Assurance, driven by early adoption of advanced geospatial technologies and a strong presence of leading industry players. Asia Pacific is emerging as a high-growth region, fueled by rapid urbanization, infrastructure development, and increasing investments in smart cities. Europe continues to witness steady growth, supported by stringent regulatory frameworks and widespread adoption of digital mapping solutions across government and commercial sectors. Latin America and the Middle East & Africa are gradually catching up, with increased focus on improving urban infrastructure and expanding digital services. Each region presents unique opportunities and challenges, shaping the global dynamics of the Map Data Quality Assurance market.

Component Analysis

The Map Data Quality Assurance market is segmented by component into software and services, each playing a pivotal role in ensuring the integrity and reliability of geospatial data. The software segment encompasses a wide array of solutions, including data validation tools, error detection algorithms, and automated correction platforms. These software solutions are designed to handle vast and complex datasets, providing real-time analytics and reporting capabilities to end-users. The increasing adoption of cloud-based software has made these solutions more accessible and scalable, catering to the needs of both

Note: This LCMS CONUS Cause of Change image service has been deprecated. It has been replaced by the LCMS CONUS Annual Change image service, which provides updated and consolidated change data.Please refer to the new service here: https://usfs.maps.arcgis.com/home/item.html?id=085626ec50324e5e9ad6323c050ac84dThis product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. https://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

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Leverage our geospatial data solutions for the following use cases: - Geospatial Data Validation & Model Building - Cultural & Seasonal Campaign Insights - Targeted, Data-Driven Geospatial Advertising - Travel & Location-Based Targeting - Trial & Partnership Transparency

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Links to recordings of the Integrated Services Program and 9-1-1 & Geospatial Services Bureau webinar series, including NG9-1-1 GIS topics such as: data preparation; data provisioning and maintenance; boundary best practices; and extract, transform, and load (ETL). Offerings include:Topic: Virginia Next Generation 9-1-1 Dashboard and Resources Update Description: Virginia recently updated the NG9-1-1 Dashboard with some new tabs and information sources and continues to develop new resources to assist the GIS data work. This webinar provides an overview of changes, a demonstration of new functionality, and a guide to finding and using new resources that will benefit Virginia public safety and GIS personnel with roles in their NG9-1-1 projects. Wednesday 16 June 2021. Recording available at: https://vimeo.com/566133775Topic: Emergency Service Boundary GIS Data Layers and Functions in your NG9-1-1 PSAP Description: Law, Fire, and Emergency Medical Service (EMS) Emergency Service Boundary (ESB) polygons are required elements of the NENA NG9-1-1 GIS data model stack that indicate which agency is responsible for primary response. While this requirement must be met in your Virginia NG9-1-1 deployment with AT&T and Intrado, there are quite a few ways you could choose to implement these polygons. PSAPs and their GIS support must work together to understand how this information will come into a NG9-1-1 i3 PSAP and how it will replace traditional ESN information in order to make good choices while implementing these layers. This webinar discusses:the function of ESNs in your legacy 9-1-1 environment, the role of ESBs in NG9-1-1, and how ESB information appears in your NG9-1-1 PSAP. Wednesday, 22 July 2020. Recording available at: https://vimeo.com/441073056#t=360sTopic: "The GIS Folks Handle That": What PSAP Professionals Need to Know about the GIS Project Phase of Next Generation 9-1-1 DeploymentDescription: Next Generation 9-1-1 (NG9-1-1) brings together the worlds of emergency communication and spatial data and mapping. While it may be tempting for PSAPs to outsource cares and concerns about road centerlines and GIS data provisioning to 'the GIS folks', GIS staff are crucial to the future of emergency call routing and location validation. Data required by NG9-1-1 usually builds on data that GIS staff already know and use for other purposes, so the transition requires them to learn more about PSAP operations and uses of core data. The goal of this webinar is to help the PSAP and GIS worlds come together by explaining the role of the GIS Project in the Virginia NG9-1-1 Deployment Steps, exploring how GIS professionals view NG9-1-1 deployment as a project, and fostering a mutual understanding of how GIS will drive NG9-1-1. 29 January 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225474Topic: Getting Your GIS Data from Here to There: Processes and Best Practices for Extract, Transform and Load (ETL) Description: During the fall of 2019, VITA-ISP staff delivered workshops on "Tools and Techniques for Managing the Growing Role of GIS in Enterprise Software." This session presents information from the workshops related to the process of extracting, transforming, and loading data (ETL), best practices for ETL, and methods for data schema comparison and field mapping as a webinar. These techniques and skills assist GIS staff with their growing role in Next Generation 9-1-1 but also apply to many other projects involving the integration and maintenance of GIS data. 19 February 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225007Topic: NG9-1-1 GIS Data Provisioning and MaintenanceDescription: VITA ISP pleased to announce an upcoming webinar about the NG9-1-1 GIS Data Provisioning and Maintenance document provided by Judy Doldorf, GISP with the Fairfax County Department of Information Technology and RAC member. This document was developed by members of the NG9-1-1 GIS workgroup within the VITA Regional Advisory Council (RAC) and is intended to provide guidance to local GIS and PSAP authorities on the GIS datasets and associated GIS to MSAG/ALI validation and synchronization required for NG9-1-1 services. The document also provides guidance on geospatial call routing readiness and the short- and long-term GIS data maintenance workflow procedures. In addition, some perspective and insight from the Fairfax County experience in GIS data preparation for the AT&T and West solution will be discussed in this webinar. 31 July 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224774Topic: NG9-1-1 Deployment DashboardDescription: I invite you to join us for a webinar that will provide an overview of our NG9-1-1 Deployment Dashboard and information about other online ISP resources. The ISP website has been long criticized for being difficult to use and find information. The addition of the Dashboard and other changes to the website are our attempt to address some of these concerns and provide an easier way to find information especially as we undertake NG9-1-1 deployment. The Dashboard includes a status map of all Virginia PSAPs as it relates to the deployment of NG9-1-1, including the total amount of funding requested by the localities and awards approved by the 9-1-1 Services Board. During this webinar, Lyle Hornbaker, Regional Coordinator for Region 5, will navigate through the dashboard and provide tips on how to more effectively utilize the ISP website. 12 June 2019. Recording not currently available. Please see the Virginia Next Generation 9-1-1 Dashboard and Resources Update webinar recording from 16 June 2021. Topic: PSAP Boundary Development Tools and Process RecommendationDescription: This webinar will be presented by Geospatial Program Manager Matt Gerike and VGIN Coordinator Joe Sewash. With the release of the PSAP boundary development tools and PSAP boundary segment compilation guidelines on the VGIN Clearinghouse in March, this webinar demonstrates the development tools, explains the process model, and discusses methods, tools, and resources available for you as you work to complete PSAP boundary segments with your neighbors. 15 May 2019. Recording available at: https://www.youtube.com/watch?v=kI-1DkUQF9Q&feature=youtu.beTopic: NG9-1-1 Data Preparation - Utilizing VITA's GIS Data Report Card ToolDescription: This webinar, presented by VGIN Coordinator Joe Sewash, Geospatial Program Manager Matt Gerike, and Geospatial Analyst Kenny Brevard will provide an overview of the first version of the tools that were released on March 25, 2019. These tools will allow localities to validate their GIS data against the report card rules, the MSAG and ALI checks used in previous report cards, and the analysis listed in the NG9-1-1 migration proposal document. We will also discuss the purpose of the tools, input requirements, initial configuration, how to run them, and how to make sense of your results. 10 April 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224495Topic: NG9-1-1 PSAP Boundary Best Practice WebinarDescription: During the months of November and December, VITA ISP staff hosted regional training sessions about best practices for PSAP boundaries as they relate to NG9-1-1. These sessions were well attended and very interactive, therefore we feel the need to do a recap and allow those that may have missed the training to attend a makeup session. 30 January 2019. Recording not currently available. Please see the PSAP Boundary Development Tools and Process Recommendation webinar recording from 15 May 2019.Topic: NG9-1-1 GIS Overview for ContractorsDescription: The Commonwealth of Virginia has started its migration to next generation 9-1-1 (NG9-1-1). This migration means that there will be a much greater reliance on geographic information (GIS) to locate and route 9-1-1 calls. VITA ISP has conducted an assessment of current local GIS data and provided each locality with a report. Some of the data from this report has also been included in the localities migration proposal, which identifies what data issues need to be resolved before the locality can migrate to NG9-1-1. Several localities in Virginia utilize a contractor to maintain their GIS data. This webinar is intended for those contractors to review the data in the report, what is included in the migration proposal and how they may be called on to assist the localities they serve. It will still ultimately be up to each locality to determine whether they engage a contractor for assistance, but it is important for the contractor community to understand what is happening and have an opportunity to ask questions about the intent and goals. This webinar will provide such an opportunity. 22 August 2018. Recording not currently available. Please contact us at NG911GIS@vdem.virginia.gov if you are interested in this content.

Introduction and Rationale:Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update SPAN. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in the final version of SPAN.Contents:Spatial dataNational rasters of land cover in the conterminous United States: 2012-2021Rasters of pixels mismatched between CDL and NVC: 2012-2021Resources in this dataset:Resource Title: SPAN land cover in the conterminous United States: 2012-2021 - SCINet File Name: KammererNationalRasters.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). Spatial Products for Agriculture and Nature ('SPAN') land cover in the conterminous United States from 2012-2021. This raster dataset is available in GeoTIFF format and was created by joining agricultural classes from the USDA-NASS Cropland Data Layer (CDL) to national vegetation from the LANDFIRE National Vegetation Classification v2.0 ('Remap'). Pixels of national vegetation are the same in all rasters provided here and represent land cover in 2016. Agricultural pixels were taken from the CDL in the specified year, so depict agricultural land from 2012-2021. Resource Title: Rasters of pixels mismatched between CDL and NVC: 2012-2021 - SCINet File Name: MismatchedNational.zip Resource Description: GeoTIFF rasters showing location of pixels that are mismatched between 2016 NVC and specific year of CDL (2012-2021). This dataset includes pixels that were classified as agriculture in the NVC but, in the CDL, were not agriculture (or were a conflicting agricultural class). For more details, we refer users to the linked publication describing our geospatial processing and validation workflow.SCINet users: The files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node455886/ See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

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. In late May and June of 1997, after a preliminary vegetation map had been prepared by the photointerpreter, a map validation step was performed in which further data were collected to obtain more information on the vegetation types and to better correlate the vegetation with the signatures on the aerial photographs. With the exception of two communities, every polygon within the Monument boundaries that had not been sampled the previous year was visited. This resulted in the collection of thirty-eight observation points. At each point, the dominant species in each vegetation stratum were recorded with an estimated cover class. These extra points gave a better understanding of the variation within vegetation types and allowed sampling of three types that had not been found the previous field season.