The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and NRM Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only roads with a SYMBOL attribute value of 1, 2, 3, 4, 11, and 12 are Forest Service System roads and contain data concerning their availability for OHV (Off Highway Vehicle) use. This data is published and refreshed on a unit by unit basis as needed. Data for each individual unit must be verified and proved consistent with the published MVUMs prior to publication.The Forest Service's Natural Resource Manager (NRM) Infrastructure (Infra) is the agency standard for managing and reporting information about inventory of constructed features and land units as well as the permits sold to the general public and to partners. Metadata

The data contain a polygon shapefile (.shp format) with 122,567 polygons corresponding to the vehicles (car, bus, truck, and boat) in 0.24m spatial resolution image with 57,856x42,496 pixel dimensions. The original image was obtained by the Infraestrutura de Dados Espaciais do Distrito Federal - IDE/DF(2022). Geoportal/DF. Available at: https://www.geoportal.seduh.df.gov.br/geoportal/ (accessed on January 8, 2022). The vectors correspond to the image taken in 2016.

Total Cars: Describes the total amount of cars present at the time share. {String} Company: Describes the car share company/owner. {String}

Detection and Semantic Segmentation of vehicles in drone aerial orthomosaics has applications in different fields like security, traffic and parking management, urban planning, logistics, and transportation, among many others. This paper presents the HAGDAVS dataset fusing RGB spectral channel and Digital Surface Model DSM for the detection and segmentation of vehicles from aerial drone images including three vehicle classes: car, motorcycle, and ghosts (motorcycle or car). We supply DSM as an additional variable to be included in deep learning and computer vision models for increasing its accuracy. RGB orthomosaic, RG-DSM fusion, and multi-label mask are provided in Tag Image File Format. Geo-located vehicle bounding boxes are provided in GeoJSON vector format. It also describes the acquisition of drone data, the derived products, and the workflow to produce the dataset. Researchers would benefit from using the proposed dataset to improve results in the case of vehicle occlusion, geo-location, and the need for cleaning ghost vehicles. As far as we know, this is the first openly available dataset for vehicle detection and segmentation, comprising RG-DSM drone data fusion, and different color masks for motorcycles, cars, and ghosts.

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only trails with the symbol value of 5-12, 16, 17 are Forest Service System trails and contain data concerning their availability for motorized use. This data is published and refreshed on a unit by unit basis as needed. Individual unit's data must be verified and proved consistent with the published MVUMs prior to publication in the EDW. Click this link for full metadata description: Metadata _

This layer shows household size by number of vehicles available. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The automotive geospatial analytics market is experiencing robust growth, driven by increasing demand for advanced driver-assistance systems (ADAS), autonomous vehicles, and precise location-based services. The market's expansion is fueled by the integration of GPS, mapping data, and sensor technologies to enhance vehicle safety, optimize logistics, and improve the overall driving experience. The convergence of big data analytics with geospatial data enables the creation of sophisticated applications for route optimization, predictive maintenance, and real-time traffic management. Key market segments include software and solutions, and services, with the software and solutions segment currently holding a larger market share due to increasing adoption of cloud-based platforms and the development of innovative algorithms for data processing and visualization. The automotive industry's shift towards electrification and connected vehicles further propels the demand for sophisticated geospatial analytics capabilities to manage charging infrastructure, monitor vehicle performance remotely, and improve fleet management efficiency. North America and Europe currently dominate the market, owing to the high adoption rates of advanced automotive technologies and well-established infrastructure. However, rapidly developing economies in Asia-Pacific are witnessing significant growth, presenting lucrative opportunities for market players. Growth is projected to continue, spurred by government initiatives promoting autonomous driving and smart city infrastructure development. However, the market faces challenges including data security concerns, the high cost of implementation, and the need for skilled professionals to manage and analyze complex geospatial data. Leading players in the market are actively investing in research and development to overcome these challenges and capitalize on emerging opportunities. This includes strategic partnerships, acquisitions, and the development of innovative solutions tailored to meet the specific requirements of the automotive industry. The market's future trajectory will likely be shaped by the rate of adoption of autonomous driving technologies, advancements in sensor technology, and the increasing availability of high-quality geospatial data. The overall market outlook remains positive, indicating substantial growth potential over the next decade.

The global digital map ecosystem is experiencing robust growth, driven by the increasing demand for location-based services (LBS) across various sectors. The market, estimated at $150 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $450 billion by 2033. This expansion is fueled by several key factors, including the proliferation of smartphones and connected devices, advancements in mapping technologies like AI-powered map creation and real-time updates, and the growing adoption of autonomous vehicles. The automotive industry, particularly the self-driving car segment, is a major driver, demanding high-precision maps with detailed information about road infrastructure, traffic patterns, and environmental conditions. Furthermore, the increasing integration of digital maps into diverse applications, including navigation, logistics, urban planning, and augmented reality experiences, contributes significantly to market growth. Competitive pressures among established players like Google, TomTom, and Baidu, as well as emerging technology companies, are fostering innovation and expanding the capabilities of digital mapping solutions. However, challenges remain. Data privacy concerns and the need for robust data security are crucial considerations as the amount of location data collected and processed continues to escalate. Furthermore, maintaining the accuracy and timeliness of map data in a rapidly changing world requires substantial investment in data collection, processing, and update mechanisms. Regulatory hurdles and varying data accessibility across different regions also pose obstacles to seamless global deployment. Market segmentation reveals the significant contribution of both civil and military applications, with acquisition, production, and release systems constituting the core technology components. The Asia-Pacific region, particularly China and India, is anticipated to experience the most significant growth due to rapid urbanization, increasing smartphone penetration, and burgeoning digital economies. North America and Europe, while already having mature markets, will also contribute substantially, driven by technological advancements and increased demand for sophisticated mapping solutions in the autonomous vehicle and smart city sectors.

Fireline GIS Mapping in Vehicles Market Outlook

As per our latest research, the global Fireline GIS Mapping in Vehicles market size was valued at USD 1.48 billion in 2024 and is expected to reach USD 4.26 billion by 2033, expanding at a robust CAGR of 12.6% during the forecast period. This remarkable growth is primarily driven by the increasing frequency and intensity of wildfires, the urgent need for real-time situational awareness in emergency response, and the rapid integration of advanced geospatial technologies into first responder vehicles. The adoption of GIS mapping solutions is transforming how fire departments and emergency services coordinate, strategize, and execute high-stakes operations, making these systems indispensable for modern firefighting and disaster response efforts.

One of the pivotal growth factors propelling the Fireline GIS Mapping in Vehicles market is the escalating incidence of wildfires and large-scale urban fires globally. Climate change has led to longer, more severe fire seasons, particularly in regions such as North America, Australia, and Southern Europe. As a result, fire departments and emergency agencies are under immense pressure to deploy resources more efficiently and ensure the safety of both personnel and affected communities. GIS mapping technologies, when integrated into vehicles, provide real-time data visualization, enable dynamic route planning, and facilitate the rapid identification of fire perimeters, hotspots, and safe zones. This capability significantly enhances decision-making and operational effectiveness, fueling increased demand for advanced GIS solutions across public and private sectors.

Another significant driver is the technological advancement in vehicle-mounted hardware and software ecosystems. The proliferation of high-speed mobile internet, robust onboard computing platforms, and sophisticated sensors has made it feasible to deploy high-resolution mapping and analytics tools directly within fire trucks, emergency response vehicles, and utility vehicles. Vendors are increasingly offering interoperable solutions that seamlessly integrate with existing command and control systems, mobile data terminals, and satellite communication devices. These innovations not only improve the accuracy and timeliness of geospatial data but also support advanced features such as predictive modeling, automated resource allocation, and remote collaboration among multiple agencies. Such technological convergence is expected to further accelerate market expansion over the coming years.

In addition, the growing emphasis on inter-agency collaboration and data sharing is catalyzing the adoption of Fireline GIS Mapping in Vehicles. Government mandates and industry standards are evolving to promote the interoperability of GIS platforms, ensuring that fire departments, government agencies, and private contractors can coordinate seamlessly during complex incidents. Cloud-based deployment models and service-oriented architectures are making it easier to scale GIS solutions, provide secure access to critical data, and enable rapid deployment in both urban and remote environments. This trend is particularly pronounced in regions with fragmented emergency response infrastructures, where centralized, real-time mapping is essential for effective disaster management.

From a regional perspective, North America currently dominates the global Fireline GIS Mapping in Vehicles market, accounting for over 38% of the total market share in 2024. This leadership is attributed to substantial investments in wildfire management technologies, a high incidence of catastrophic fires, and strong government support for digital transformation initiatives within public safety agencies. Europe follows closely, driven by stringent regulatory frameworks and the increasing adoption of smart city solutions. The Asia Pacific region is emerging as the fastest-growing market, with a projected CAGR of 14.2% through 2033, fueled by rapid urbanization, rising awareness of disaster preparedness, and expanding government initiatives in countries such as Australia, Japan, and China.

Automotive Geospatial Analytics Market Outlook

The global automotive geospatial analytics market size is projected to reach USD 20 billion by 2032, up from USD 8.5 billion in 2023, exhibiting a CAGR of 10.2% during the forecast period. The growing integration of advanced geospatial technologies in automotive systems is one of the primary factors driving market growth. Increasing demand for real-time location data for navigation, fleet management, and traffic optimization is further fueling this market's expansion.

One of the primary growth factors in the automotive geospatial analytics market is the rising adoption of connected and autonomous vehicles. These vehicles rely heavily on precise geospatial data to navigate, avoid obstacles, and optimize routes. The advancements in sensor technologies, such as LiDAR and high-resolution cameras, contribute significantly to the enhancement of geospatial analytics capabilities. Moreover, the increasing trend of vehicle electrification demands more sophisticated geospatial data to manage battery life efficiently and optimize charging station locations.

Another significant growth driver is the heightened need for enhanced safety and security features in vehicles. Geospatial analytics play a crucial role in advanced driver-assistance systems (ADAS), enabling features like lane departure warnings, adaptive cruise control, and collision avoidance systems. These analytics are also vital for emergency response services, providing precise location data to ensure timely and accurate assistance. As regulatory bodies across various regions impose stringent safety norms, the adoption of geospatial analytics in the automotive sector is expected to rise.

The surge in demand for efficient fleet management solutions also propels the market. Fleet operators can leverage geospatial analytics to monitor vehicle locations, track routes, optimize fuel usage, and ensure timely deliveries. The analytics also assist in predictive maintenance, reducing downtime and enhancing operational efficiency. Furthermore, with the rise of e-commerce and logistics services, the need for advanced fleet management solutions is escalating, thereby boosting the automotive geospatial analytics market.

Geospatial Analytics is becoming increasingly vital in the automotive industry, particularly with the rise of connected and autonomous vehicles. These vehicles depend on accurate geospatial data to function efficiently and safely. The integration of geospatial analytics allows for real-time data processing and decision-making, which is crucial for navigation and obstacle avoidance. As the automotive industry continues to innovate, the role of geospatial analytics is expected to expand, providing new opportunities for enhancing vehicle safety and performance. This technology not only aids in navigation but also plays a critical role in fleet management, traffic optimization, and emergency response services, making it an indispensable tool for modern automotive systems.

Regionally, North America and Europe are leading the market due to the early adoption of advanced automotive technologies and the presence of major automotive manufacturers and tech companies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth is driven by the expanding automotive industry in countries like China, India, and Japan, along with increasing investments in smart city projects and infrastructure development.

Component Analysis

In the automotive geospatial analytics market, the component segment comprises software, hardware, and services. The software segment holds a significant share due to the increasing demand for advanced geospatial analytics solutions that can process complex data and provide actionable insights. These software solutions include Geographic Information Systems (GIS), data visualization tools, and spatial data management platforms. The rising integration of cloud-based platforms is also enhancing the scalability and accessibility of these software solutions, further driving their adoption.

The hardware segment is also witnessing substantial growth, primarily driven by the advancements in sensor technologies. Devices such as GPS receivers, LiDAR sensors, and high-resolution cameras are integral to capturing precise geospatial data. The continuous development in sensor miniaturization and cost reduction is making these hardware components more

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the Motor Vehicle Use Map (MVUM). Only roads with a SYMBOL attribute value of 1, 2, 3, 4, 11, and 12 are Forest Service System roads and contain data concerning their availability for OHV (Off Highway Vehicle) use. This data is published and refreshed on a unit by unit basis as needed. Information for each individual unit must be verified as to be consistent with the published MVUMs prior to inclusion in this data. Not every National Forest has data included in this feature class.

Fireline GIS Mapping in Vehicles Market Outlook

According to our latest research, the Global Fireline GIS Mapping in Vehicles market size was valued at $1.2 billion in 2024 and is projected to reach $3.4 billion by 2033, expanding at a CAGR of 12.7% during 2024–2033. The primary factor propelling the growth of this market globally is the increasing frequency and intensity of wildfires, which has prompted fire departments, government agencies, and private contractors to adopt advanced Geographic Information System (GIS) solutions for real-time situational awareness and enhanced response capabilities. As wildfires become more unpredictable due to climate change, the integration of GIS mapping technologies in vehicles is rapidly becoming indispensable for efficient wildfire management, emergency response, and asset tracking, driving robust market expansion across multiple regions and industry verticals.

Regional Outlook

North America currently dominates the Fireline GIS Mapping in Vehicles market, accounting for the largest share, with an estimated market value of $540 million in 2024 and expected to reach $1.5 billion by 2033. This region’s leadership position is primarily attributed to its mature emergency response infrastructure, widespread adoption of advanced vehicle telematics, and robust government policies supporting wildfire management. The United States, in particular, has invested heavily in GIS mapping technologies for both public and private firefighting fleets, leveraging real-time data analytics and cloud-based platforms to enhance coordination during wildfire events. Additionally, the region benefits from a high level of technological innovation and a proactive approach to integrating emerging digital solutions into first responder operations, further cementing its market dominance.

Asia Pacific is projected to be the fastest-growing region in the Fireline GIS Mapping in Vehicles market, with a remarkable CAGR of 15.8% from 2024 to 2033. This growth is fueled by increasing wildfire incidents across Australia, Southeast Asia, and parts of China, coupled with rapid urbanization and expanding vehicle fleets dedicated to emergency response. Significant investments in smart city initiatives and government mandates for disaster preparedness are driving the adoption of GIS mapping technologies in emergency vehicles. Furthermore, the proliferation of cloud-based solutions and mobile GIS applications is enabling even smaller municipalities and rural fire departments to access advanced mapping tools, thereby accelerating market penetration and technological advancement in the region.

Emerging economies in Latin America, the Middle East, and Africa are witnessing gradual but steady adoption of Fireline GIS Mapping in Vehicles solutions, although growth is tempered by budgetary constraints, limited technological infrastructure, and inconsistent policy frameworks. In these regions, localized demand is primarily concentrated in countries prone to seasonal wildfires and where international aid or government-backed modernization programs are in place. Challenges such as lack of skilled personnel, inadequate data connectivity, and the need for region-specific customization of GIS platforms persist. However, ongoing efforts to strengthen disaster response capabilities and increasing awareness of the benefits of real-time GIS mapping are expected to create new opportunities for market growth in these emerging markets over the forecast period.

Report Scope

Some of the most vulnerable populations don’t have the network or the financial means necessary to evacuate themselves during a catastrophic disaster. Understanding where these people are is critical information for first responders so that they can provide the necessary support and aid to everyone. This is extremely important if these individuals are living in isolated areas that are difficult to access; if residents have no way of evacuating themselves (no vehicle available); or if the residents have special transportation needs due to disability or medical issues.This map shows counts and percents of households that have no vehicle available by state, county, and tract. Vehicles include passenger cars, vans, and pickup or panel trucks kept at home and available for use of household members. Motorcycles, other recreational vehicles, dismantled or immobile vehicles, and vehicles used only for business purposes are excluded. Map starts in New Orleans, but zoom, pan, or use the search bar to get to your city, county, or neighborhood. Hover over the bar chart in the pop-up to see information about household size.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available. Other uses of this data:When the data is used in conjunction with place-of-work and journey-to-work data, the information can provide insight into vehicle travel and aid in forecasting future travel and its effect on transportation systems. The data also serve to aid in forecasting future energy consumption and needs.

This New Zealand car detection Deep Learning Package will detect cars from high resolution imagery. This model is re-trained from the Esri Car Detection - USA Deep Learning Package and is trained to work better within the New Zealand geography.The model precision had also improved from 0.81 to 0.89. The package is trained to be more aggressive in terms of car detecting and is able to detect most cars that are fully covered in shade or partially blocked by tree canopy. This deep learning model is used to detect cars in high resolution drone or aerial imagery. Car detection can be used for applications such as traffic management and analysis, parking lot utilization, urban planning, etc. It can also be used as a proxy for deriving economic indicators and estimating retail sales. High resolution aerial and drone imagery can be used for car detection due to its high spatio-temporal coverage.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst and ArcGIS 3D Analyst extensions for ArcGIS ProArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution RGB imagery (7.5 centimetre spatial resolution)OutputFeature class containing detected carsApplicable geographiesThe model is expected to work well with the New Zealand localised data.Model architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS Pro Arcpy.Accuracy metricsThis model has an average precision score of 0.89.Sample resultsHere are a few results from the model.(Post processing are recommended to filter out False Positive Object.e.g (confidence >= x | 0.95) |& ((shape_area/shape_length) >= x | 0.5) |& (class == Car) |& Regularize(feature)3% of detected object will need to be filtered out averagely .To learn how to use this model, see this story

Car Parks in York. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

The DWR Enterprise image server has hundreds of image services, but there is no interface for searching or querying the server. The image server index contains footprints of the geographic extent of each available image service, as well as relevant attributes that describe the image service. There are also related tables for most types of image services that contain information specific to that type of data, such as specification numbers for design drawings or beam types for bathymetry data.

The dataset contains locations and attributes of car sharing locations. The points are the general locations where the Car Sharing currently exist. A database provided by the District Department of Transportation identified Car sharing locations.

This dataset contains polygons features of police car districts.

This dataset is available using the link : https://norfolkgisdata-orf.opendata.arcgis.com/datasets/f6d461c4ede64f3ca895b9dc028e02e6_2/about

The global map navigation service market is experiencing robust growth, driven by the increasing penetration of smartphones, the proliferation of connected cars, and the rising demand for location-based services (LBS). The market's expansion is fueled by advancements in mapping technologies, such as high-definition (HD) maps and real-time traffic updates, which enhance user experience and safety. Furthermore, the integration of map navigation with other services, like ride-hailing apps and delivery platforms, is creating new avenues for growth. While challenges exist, such as data privacy concerns and the need for accurate map data in remote areas, the overall market outlook remains positive. We project a Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033, with significant regional variations driven by factors such as infrastructure development, smartphone adoption rates, and government regulations. The market is segmented by service type (in-car, mobile, etc.), application (consumer, commercial), and technology (GPS, satellite, etc.), each exhibiting unique growth trajectories. Key players are strategically investing in research and development, mergers and acquisitions, and partnerships to strengthen their market positions and meet the evolving needs of consumers and businesses. The competitive landscape is highly fragmented, with numerous established players and emerging startups vying for market share. Companies like Google, TomTom, Garmin, and others are continually innovating to enhance their map data, user interfaces, and overall service offerings. The focus on providing personalized experiences, incorporating augmented reality (AR) features, and leveraging artificial intelligence (AI) for route optimization and traffic prediction is transforming the map navigation service market. The integration of autonomous driving technology presents a significant long-term growth opportunity, as accurate and reliable map data is crucial for the safe and efficient operation of self-driving vehicles. However, maintaining data accuracy, addressing cybersecurity threats, and ensuring compliance with evolving regulations will be critical for sustained success in this dynamic market.

The service meant to track current zero-emission vehicle (ZEV) reach codes and spotlight current efforts to promote the transition to clean transportation. This map provides an overview of current ZEV reach codes present across the state. Selecting any highlighted jurisdiction will provide more details on the related ZEV reach code