This dataset contains a list of visa-free countries for travelers with citizenship of China, as well as the maximum number of days they can stay without a visa.

185 Active Global Map suppliers, manufacturers list and Global Map exporters directory compiled from actual Global export shipments of Map.

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Digital Map Market Size 2024-2028

The digital map market size is forecast to increase by USD 19.75 billion at a CAGR of 26.06% between 2023 and 2028.

What will be the Size of the Digital Map Market During the Forecast Period?

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The market In the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries. The proliferation of connected devices, including PDAs, Cortana, Siri, Amazon Echo, and Google Now, has increased the demand for digital maps in real-time mapping applications and map analytics. Real-time tracking systems are gaining popularity in sectors such as energy & power, automobile, telecommunication, and transportation, providing valuable spatial data on terrain, roads, buildings, rivers, and other features. APIs enable seamless integration of digital maps into various applications, enhancing user experience and ROI.
The internet has made digital maps accessible from anywhere, further fueling market growth. Overall, the market is poised for significant expansion, offering numerous opportunities for businesses and innovators alike.

How is this Digital Map Industry segmented and which is the largest segment?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Geography

  APAC

    China
    India
    Japan


  North America

    US


  Europe

    Germany


  South America



  Middle East and Africa

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period.

Digital maps play a crucial role in various industries, particularly in automotive applications for driver assistance systems. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. The increasing use of connected cars and the development of Long-Term Evolution (LTE) technologies are driving the demand for digital maps. These maps provide real-time traffic information, helping drivers navigate urban areas with high population density and traffic congestion more efficiently. Additionally, digital maps are essential for transportation route planning, public services, agriculture, and conservation efforts. In agriculture, digital maps help determine soil types, nutrient levels, and crop yields.

Waste reduction and the protection of sensitive ecosystems and habitats are also facilitated by digital maps. Overall, digital maps offer valuable insights for urban planning, emergency situations, and various industries, making them an indispensable tool for businesses and individuals alike.

Get a glance at the Digital Map Industry report of share of various segments. Request Free Sample

The navigation segment was valued at USD 4.58 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 43% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

In the Asia-Pacific (APAC) region, the market for digital maps is experiencing growth due to the increasing use of Internet of Things (IoT) devices and real-time mapping technologies. Countries such as Japan, China, and South Korea, along with a few Southeast Asian nations, are key contributors to this market expansion. IoT devices, including GPS-enabled PDAs, professional assistants, and smart home devices, are being integrated into digital maps to provide real-time data. This data can be used to develop real-time dashboards, enabling organizations and local governments to effectively manage traffic, monitor oil field equipment, and more.

The growing digital connectivity landscape in APAC is fueling the demand for digital maps and related technologies, including APIs, SDKs, and mapping solutions from providers such as Nearmap, ESRI, and INRIX.

Digital Map Market Dynamics

Our digital map market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise In the adoption of Digital Map Industry?

Adoption of intelligent PDAs is the key driver of the market.

The markets encompass a range of advanced technologies and applications that leverage Geographic Information Systems (

China EQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data was reported at 60.874 Average 12 Mths PY=100 in Dec 2024. This records a decrease from the previous number of 66.300 Average 12 Mths PY=100 for Nov 2024. China EQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data is updated monthly, averaging 82.495 Average 12 Mths PY=100 from Feb 2018 (Median) to Dec 2024, with 70 observations. The data reached an all-time high of 372.400 Average 12 Mths PY=100 in Mar 2020 and a record low of 35.451 Average 12 Mths PY=100 in Feb 2019. China EQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: HS4 Classification.

As the second largest producer of maize, China contributes 23% of global maize production and plays an important role in guaranteeing maize markets stability. In spite of its importance, there is no 30 m spatial resolution distribution map of maize for all of China. This study used a time-weighted dynamic time warping method to identify planting areas of maize by comparing the similarity of time series of a satellite-based vegetation index at each pixel with a standard time series derived from known maize fields and mapped maize distribution from 2016 to 2020 over 22 provinces accounting for more than 99% of the maize planting area in China. Based on 18800 field-surveyed pixels at 30-meter spatial resolution, the distribution map yields 76.15% and 81.59% of producer’s and user’s accuracies averaged over the entire investigated provinces, respectively. Municipality- and county-level census data also show a good performance in reproducing the spatial distribution of maize. This study provides an approach to mapping maize over large areas based on a small volume of field survey data.classification system:1: maize0: non-maizeRuoque Shen, Jie Dong, Wenping Yuan, Wei Han, Tao Ye, Wenzhi Zhao, "A 30 m Resolution Distribution Map of Maize for China Based on Landsat and Sentinel Images", Journal of Remote Sensing, vol. 2022, Article ID 9846712, 12 pages, 2022. https://doi.org/10.34133/2022/9846712

This web map provides a detailed vector reference basemap for the world symbolized with a custom navigation map style. The map was created from the customized vector tile layer. In this example, the World Navigation Map tile layer was updated simply by changing the text-field value for a few layers in the map. The underlying data has local language displayed at large scale in a number of places around the world.

China IQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data was reported at 107.200 Average 12 Mths PY=100 in Dec 2024. This records an increase from the previous number of 34.900 Average 12 Mths PY=100 for Nov 2024. China IQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data is updated monthly, averaging 87.692 Average 12 Mths PY=100 from Jan 2018 (Median) to Dec 2024, with 65 observations. The data reached an all-time high of 373.600 Average 12 Mths PY=100 in Nov 2021 and a record low of 25.200 Average 12 Mths PY=100 in Oct 2024. China IQI: MoM: HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Quantum Index: MoM: HS4 Classification.

This web map provides a detailed vector reference basemap for the world symbolized with a classic Esri topographic map style. This map is designed for use with World Hillshade. The map was created from the customized vector tile layer. In this example, the World Topographic Map tile layer was updated simply by changing the text-field value for a few layers in the map. The underlying data has local language displayed at large scale in a number of places around the world.

Geologic maps of the Moon provide comprehensive information about the geologic strata, structural features, lithologies, and chronology of the lunar crustal surface, which reflect the evolution of the lunar crust under igneous processes, catastrophic impacts, and volcanic activities. The map in this repository is the first 1:2,500,000-scale lunar global geologic map, which incorporates the most comprehensive knowledge about the Moon by taking advantage of the latest exploration results and scientific findings. An updated lunar time scale is employed in this map to better reflect the dynamic evolution of the Moon. The map provides a state-of-the-art illustration of impact basins and craters of different periods, the distributions of 17 types of rocks and 14 types of structures. The map is free to use for non-commercial forms including scientific research and science promotion under proper citation. This data is provided for understanding the associated research paper (https://doi.org/10.1016/j.scib.2022.05.021). The geologic map will be officially published in both Chinese and English copies by the Geological Publishing House after being proofed.

This collection contains the land cover map (30m) derived from GlobeLand30 used in our study ‘Forestation at the right time with the right species can generate persistent carbon benefits in China’.

Original maps were download from http://www.globallandcover.com/. The map was merged in ArcGIS 10.8 for the region of China (70°E-140°E, 15°N-55°N).

Reference:
C. Jun, Y. Ban, S. Li, Open access to Earth land-cover map. Nature 514, 434–434 (2014). DOI:10.1038/514434c.

This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

To use the data, please cite this paper or contact the corresponding author for more details.

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...

This web map provides a detailed basemap for the world symbolized with a classic Esri street map style. The map was created from the customized vector tile layer. In this example, the World Street Map tile layer was updated simply by changing the text-field value for a few layers in the map. The underlying data has local language (Traditional Chinese) values populated in Hong Kong area.

109326 Global export shipment records of Map with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

China Trade Index: YoY: Unit Value: Import HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data was reported at 52.482 Prev Year=100 in Dec 2024. This records a decrease from the previous number of 144.400 Prev Year=100 for Nov 2024. China Trade Index: YoY: Unit Value: Import HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data is updated monthly, averaging 108.700 Prev Year=100 from Jan 2018 (Median) to Dec 2024, with 64 observations. The data reached an all-time high of 176.000 Prev Year=100 in Feb 2021 and a record low of 50.400 Prev Year=100 in Oct 2024. China Trade Index: YoY: Unit Value: Import HS4: Maps and Hydrographic or Similar Charts of All Kinds, Including Atlases, Wall Maps, Topographical Plans and Globes, Printed. data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JE: Unit Value Index: YoY: HS4 Classification.

Sichuan Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for Sichuan Province, China. The top level directory sichuan-dataset is a GRASS GIS location for WGS 84 / UTM zone 48N with EPSG code 32648. Inside the location there is the PERMANENT mapset, color tables, category tables, a license file, and readme file.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• ALOS Global Digital Surface Model
• Anthromes
• NASA Shuttle Radar Topography Mission (SRTM)
• ESA CCI Landcover
• European Soil Data Centre (ESDAC)
• Database of Global Administrative Areas (GADM)
• Harvard World Map
• IUCN Red List
• Protected Planet
• NASA Blue Marble
• Natural Earth
• Open Street Map
• WWF HydroSHEDS
• WWF Terrestrial Ecoregions of the World

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

Hcropland30:A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model

***Please note this dataset is undergoing peer review***

Version: 1.0

Authors: Qiong Hu ^{a, 1}, Zhiwen Cai ^{b, 1}, Liangzhi You ^{c, d}, Steffen Fritz ^e, Xinyu Zhang ^c, He Yin ^f, Haodong Wei^c, Jingya Yang ^g, Zexuan Li ^a, Qiangyi Yu ^g, Hao Wu ^a, Baodong Xu ^{b *}, Wenbin Wu ^{g, *}

^a Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

^b College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

^c Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China

^d International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA

^e Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria

^f Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA

^g State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Introduction

We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.

Dataset

1. Hcropland30: A hybrid 30-m global cropland map in 2020

****Data format: GeoTiff

****Spatial resolution: 30 m

****Projection: EPSG: 4326 (WGS84)

****Values: 1 denotes cropland and 0 denotes non-cropland

The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.

2. 1°×1° Grids: This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.

****Data format: ESRI shapefile

****Projection: EPSG: 4326 (WGS84)

****Attribute Fields:

Id: The grid’s ID number.

area: The area of the grid.

mode: Indicates the representative sample grid.

climate: The climate type the grid belongs to.

dem: Average DEM value of the grid.

ndvi_s1 to ndvi_s4: Average NDVI values for four seasons within the grid.

esa, esri, fcs30, fromglc, glad, globeland30: Proportion of cropland pixels of different publicly available cropland products.

inconsistent: Proportion of inconsistent pixels within the grid according to different public cropland products.

hcropland30: Proportion of cropland pixels of our Hcropland30 dataset.

3. Samples: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.

****Data format: ESRI shapefile

****Projection: EPSG: 4326 (WGS84)

****Attribute Fields:

type: 1 denotes cropland sample and 0 denotes non-cropland sample.

Citation

If you use this dataset, please cite the following paper:

Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.

License

The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Disclaimer

This dataset is provided as-is, without any warranty, express or implied. The dataset author is not

responsible for any errors or omissions in the data, or for any consequences arising from the use

of the data.

Contact

If you have any questions or feedback regarding the dataset, please contact the dataset author

Qiong Hu (huqiong@ccnu.edu.cn)

This is data generated in the paper, "Substantial contribution of trees outside forests to above-ground carbon across China".

Authors

Yang Su a, b, c, Tianqi Shi b, Xianglin Zhang c, d, Yidi Xu b, Kai Cheng e,f, Siyu Liu g, Ge Han h, i, Xin Ma j, Songchao Chen d, k, Xiaowei Tong l, Wei Li m, Wei Gong j, n, o, Qinghua Guo e, f, Martin Brandt g, Shilong Piao p, q, Alexandre d'Aspremont a , Philippe Ciais b

Affiliations

a Département d'Informatique, École Normale Supérieure – PSL, 45 Rue d'Ulm, 75005 Paris, France

b Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ Orme des Merisiers, 91190 Gif-sur-Yvette, France

c UMR ECOSYS, INRAE AgroParisTech, Université Paris-Saclay, 91120 Palaiseau, France

d College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China

e Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, 100871 Beijing, China

f Institute of Ecology, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China

g Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

h Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

i Perception and Effectiveness Assessment for Carbon‐neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China

j State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

k ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, 311215 Hangzhou, China

l Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.

m Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China

n Electronic Information School, Wuhan University, Wuhan, China

o Wuhan Institute of Quantum Technology, Wuhan, China

p State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

q Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

Corresponding Author

Yang Su

yang.su@ens.fr

+33 1 89 10 07 67

École Normale Supérieure – PSL

To use the data, please cite this paper or contact the corresponding author for more details.

Funding

Artificial Intelligence for forest monitoring from space – AI4Forests

Agence Nationale de la Recherche

Find out more...

This web map provides a detailed reference basemap for the world symbolized with a custom "night time" street map style. The map was created from the customized vector tile layer. In this example, the World Street Map (Night) tile layer was updated simply by changing the text-field value for a few layers in the map. The underlying data has local language (Traditional Chinese) displayed at large scale in Hong Kong area.

Snapshot img

HD Map For Autonomous Vehicles Market Size 2024-2028

The HD map for autonomous vehicles market size is forecast to increase by USD 13.39 billion at a CAGR of 46.02% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing adoption of autonomous vehicles and the development of advanced connected infrastructure. The integration of high-definition maps into autonomous systems enables vehicles to navigate complex environments more accurately and efficiently, reducing the risk of accidents and improving overall performance. HD map creation for autonomous vehicles is a complex process involving data acquisition, aggregation, and integration of advanced technologies such as AI and machine learning. However, the high cost associated with the technology remains a significant challenge for market expansion. Manufacturers must continue to innovate and find cost-effective solutions to make HD maps an essential component of autonomous vehicles, rather than a luxury. Companies seeking to capitalize on this market opportunity should focus on collaborating with infrastructure providers, developing scalable and cost-effective HD mapping technologies, and ensuring seamless integration with autonomous systems. By addressing these challenges and leveraging the growing demand for autonomous vehicles and advanced infrastructure, market participants can effectively navigate the strategic landscape and drive long-term success.

What will be the Size of the Market during the forecast period?

Request Free Sample

The market is experiencing significant growth as the global push towards advanced driver-assistance systems (ADAS) and fully autonomous vehicles (AVs) continues. HD Maps, which utilize technologies such as Lidar, SLAM (Simultaneous Localization and Mapping), and digital cameras, play a crucial role in enabling AVs to navigate roads safely and efficiently. These maps provide real-time, high-precision data to AV systems, allowing them to identify and respond to road conditions, obstacles, and other vehicles in real time. The market is expected to reach a substantial size in the coming years, driven by the increasing demand for shared mobility services, including ride-sharing and robo-taxi services.
The integration of 5G networks is also expected to accelerate the adoption of HD Maps, as they enable faster and more reliable data transmission between vehicles and maps. The market is witnessing continuous innovation, with companies investing heavily in research and development to improve the accuracy and coverage of HD Maps. Additionally, the integration of HD Maps with other technologies, such as sensor fusion and deep learning algorithms, is expected to further enhance the capabilities of AVs. Overall, the HD Map market for autonomous vehicles is a dynamic and rapidly evolving market, poised for significant growth in the coming years.

How is this Industry segmented?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Solution

  Cloud-based
  Embedded


Vehicle Type

  Passenger
  Commercial


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Solution Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. HD maps are a critical component in the advancement of autonomous vehicles. These high-definition maps offer enhanced accuracy and precision for navigation, while their cloud-based infrastructure ensures accessibility and ease of updates. This enables autonomous vehicles to navigate complex and unfamiliar environments more effectively. Notable industry players, such as NavInfo Co. Ltd. (Navinfo), HERE Global BV (HERE), TomTom NV (TomTom), and NVIDIA Corp. (NVIDIA), prioritize cloud-based solutions and real-time services for their HD mapping offerings. The integration of 5G networks further enhances the capabilities of HD maps, contributing to the growth of autonomous driving technology in passenger and commercial vehicles.

Get a glance at the market report of share of various segments Request Free Sample

The cloud-based segment was valued at USD 825.20 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 38% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The market in North America is primarily driven by the United States, where the increasing deployme

This web map provides a customized world basemap uniquely symbolized. It takes its inspiration from a printed atlas plate and pull-down scholastic classroom maps. The map emphasizes the geographic and political features in the design. This vector tile layer is built using the same data sources used for the World Topographic Map and other Esri basemaps. The use of country level polygons are preassigned with eight different colors. It also includes the global graticule features as well as landform labels of physical features. This map is designed for use with and includes the shaded relief layer. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog.Fonts available for use in the style resource directory are under the OFL, Open Font License.This map was designed and created by Cindy Prostak.