MAP-CC

🌐 Homepage | 🤗 MAP-CC | 🤗 CHC-Bench | 🤗 CT-LLM | 📖 arXiv | GitHub An open-source Chinese pretraining dataset with a scale of 800 billion tokens, offering the NLP community high-quality Chinese pretraining data.

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This model, developed for academic purposes, employs rigorously compliance-checked training data to uphold the highest standards of integrity and compliance. Despite our efforts, the inherent complexities of data and the broad spectrum of… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/MAP-CC.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

This web map references the live tiled map service from the OpenStreetMap (OSM) project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: https://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in ESRI products under a Creative Commons Attribution-ShareAlike license. Tip: This service is one of the basemaps used in the ArcGIS.com map viewer. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10. Tip: Here are some well known locations as they appear in this web map, accessed by launching the web map with a URL that contains location parameters: Athens, Cairo, Jakarta, Moscow, Mumbai, Nairobi, Paris, Rio De Janeiro, Shanghai

The Digital Geologic Map of the U.S. Geological Survey Mapping in the Western Portion of Amistad National Recreation Area, Texas is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Eddie Collins, Amanda Masterson and Tom Tremblay (Texas Bureau of Economic Geology); Rick Page (U.S. Geological Survey); Gilbert Anaya (International Boundary and Water Commission). Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (wpam_metadata.txt; available at http://nrdata.nps.gov/amis/nrdata/geology/gis/wpam_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (wpam_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 14N. The data is within the area of interest of Amistad National Recreation Area.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Point Sur to Point Arguello map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Point Sur to Point Arguello map area data layers. Data layers are symbolized as shown on the associated map sheets.

The USGS Topo base map service from The National Map is a combination of contours, shaded relief, woodland and urban tint, along with vector layers, such as geographic names, governmental unit boundaries, hydrography, structures, and transportation, to provide a composite topographic base map. Data sources are the National Atlas for small scales, and The National Map for medium to large scales.

For many people data is seen as abstract information. It is therefore valuable to use Matrixian Map, an interactive map that shows an enormous amount of data in one figure. It helps to make complex analyzes understandable, to see new opportunities and to make data-driven decisions.

With our large amount of consumer, real estate, mobility and logistics data we can design very extensive maps. Whether it concerns a map that shows your (potential) customers, shows on which roofs solar panels can be placed or indicates when shopping areas can be supplied, with our knowledge of households, companies and objects, almost anything is possible!

The Nova Map (World Edition) web map provides a detailed world basemap featuring a dark background with glowing blue symbology and colors that are reminiscent of science-fiction shows, where one is looking at a map of the world on a 'head's up' device or a map that would be projected from a transparent glass wall. The map is designed with a grid pattern across the ocean and stripes or square stippled patterns for land use features visible at larger scales. Additional graphics in the oceans presents a futuristic user interface. The futuristic and less terrestrial feel theme continues with the geometric patterns, starburst city dot symbols, and cool color scheme. The fonts displayed are clean and squarish (san serif) with a futuristic, science-fiction, or high technology appearance.This basemap, included in the ArcGIS Living Atlas of the World, uses the Nova vector tile layer.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.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 referenced in this map.

ICDAR 2021 Competition on Historical Map Segmentation — Dataset

This is the dataset of the ICDAR 2021 Competition on Historical Map Segmentation (“MapSeg”). This competition ran from November 2020 to April 2021. Evaluation tools are freely available but distributed separately.

Official competition website: https://icdar21-mapseg.github.io/

The competition report can be cited as:

Joseph Chazalon, Edwin Carlinet, Yizi Chen, Julien Perret, Bertrand Duménieu, Clément Mallet, Thierry Géraud, Vincent Nguyen, Nam Nguyen, Josef Baloun, Ladislav Lenc, and Pavel Král, "ICDAR 2021 Competition on Historical Map Segmentation", in Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR'21), September 5-10, 2021, Lausanne, Switzerland.

BibTeX entry:

@InProceedings{chazalon.21.icdar.mapseg, author = {Joseph Chazalon and Edwin Carlinet and Yizi Chen and Julien Perret and Bertrand Duménieu and Clément Mallet and Thierry Géraud and Vincent Nguyen and Nam Nguyen and Josef Baloun and Ladislav Lenc and and Pavel Král}, title = {ICDAR 2021 Competition on Historical Map Segmentation}, booktitle = {Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR'21)}, year = {2021}, address = {Lausanne, Switzerland}, }

We thank the City of Paris for granting us with the permission to use and reproduce the atlases used in this work.

The images of this dataset are extracted from a series of 9 atlases of the City of Paris produced between 1894 and 1937 by the Map Service (“Service du plan”) of the City of Paris, France, for the purpose of urban management and planning. For each year, a set of approximately 20 sheets forms a tiled view of the city, drawn at 1/5000 scale using trigonometric triangulation.

Sample citation of original documents:

Atlas municipal des vingt arrondissements de Paris. 1894, 1895, 1898, 1905, 1909, 1912, 1925, 1929, and 1937. Bibliothèque de l’Hôtel de Ville. City of Paris. France.

Motivation

This competition aims as encouraging research in the digitization of historical maps. In order to be usable in historical studies, information contained in such images need to be extracted. The general pipeline involves multiples stages; we list some essential ones here:

segment map content: locate the area of the image which contains map content;

extract map object from different layers: detect objects like roads, buildings, building blocks, rivers, etc. to create geometric data;

georeference the map: by detecting objects at known geographic coordinate, compute the transformation to turn geometric objects into geographic ones (which can be overlaid on current maps).

Task overview

Task 1: Detection of building blocks

Task 2: Segmentation of map content within map sheets

Task 3: Localization of graticule lines intersections

Please refer to the enclosed README.md file or to the official website for the description of tasks and file formats.

Evaluation metrics and tools

Evaluation metrics are described in the competition report and tools are available at https://github.com/icdar21-mapseg/icdar21-mapseg-eval and should also be archived using Zenodo.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This is a dataset download, not a document. The Open button will start the download.In 2015, the Oregon Biodiversity Information Center at Portland State University worked with the Oregon Department of Fish and Wildlife (ODFW), to assist in their 2015 conservation strategy update. This work involved updating the maps of each of ODFW’s conservation strategy habitats originally created for the first strategy in 2006,and integrating these into a 2015 strategy habitat map. The updated maps took advantage of new data and spatial modeling tools. However, strategy habitats only represent only 11 of the approximately 77 Oregon habitats, and are only mapped in the ecoregions in which they are conservation priorities. As a result, there was a strong interest in using this 2015 data to create a statewide, comprehensive habitat map. In 2017, the Oregon Department of Administrative Services, Geographic Enterprise Office (DAS-GEO), through their Framework Implementation program, with additional support from ODFW, funded the completion of a statewide habitat map, which was completed at the end of 2018. The habitat map is a compilation of a number of recent regional and ecosystem focused vegetation-mapping efforts. It includes the best available data for each of the habitat types. As a result, different parts of the map rely on varied methods and data. For detailed methodology please see the enclosed PDF document.

This web map is a component of the CrowdMag Visualization App.NOAA's CrowdMag is a crowdsourced data collection project that uses a mobile app to collect geomagnetic data from the magnetometers that modern smartphones use as part of their navigation systems. NCEI collects these data from citizen scientists around the world and provides quality control services before making them available through a series of aggregated maps and charts. These data have the potential to provide a high resolution alternative to geomagnetic satellite data, as well as near real-time information about changes in the magnetic field.This map shows data collected from phones around the world! Displayed are the Crowdsourced magnetic data within a tolerance level of prediction by World Magnetic Model. We have added some uncertainty to each data point shown to ensure the privacy of our contributors. The data points are grouped together (or "aggregated") into small areas , and we display the median data value across all the readings for each point.

This map is updated every day. Layers are available for Median Intensity, Median Horizontal Component (Y), and Median Vertical Component (Z).


Use the time slider to select the date range. Select the different layers under the "Crowdmag Observations" menu. View a color scale using the legend tool. Zoom to your location using the "Find my Location" tool. Click or tap on a data point to view a popup containing more information.

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of location-based services (LBS) and geographic information systems (GIS) across industries like real estate, tourism, logistics, and urban planning is a major catalyst. Businesses are increasingly leveraging interactive maps to enhance customer engagement, improve operational efficiency, and gain valuable insights from geospatial data. Furthermore, advancements in mapping technologies, including the integration of AI and machine learning for improved data analysis and visualization, are contributing to market growth. The accessibility of user-friendly tools, coupled with the decreasing cost of cloud-based solutions, is also making interactive map creation more accessible to a wider range of users, from individuals to large corporations. However, the market also faces certain challenges. Data security and privacy concerns surrounding the use of location data are paramount. The need for specialized skills and expertise to effectively utilize advanced mapping technologies may also hinder broader adoption, particularly among smaller businesses. Competition among established players like Mapbox, ArcGIS StoryMaps, and Google, alongside emerging innovative solutions, necessitates constant innovation and differentiation. Nevertheless, the overall market outlook remains positive, with continued technological advancements and rising demand for data visualization expected to propel growth in the coming years. Specific market segmentation data, while unavailable, can be reasonably inferred from existing market trends, suggesting a strong dominance of enterprise-grade solutions, but with substantial growth expected from simpler, more user-friendly tools designed for individuals and small businesses.

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the map feature extraction challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

This map provides a colorized representation of slope, generated dynamically using server-side slope function on the Terrain layer. The degree of slope steepness is depicted by light to dark colors - flat surfaces as gray, shallow slopes as light yellow, moderate slopes as light orange and steep slopes as red-brown. A scaling is applied to slope values to generate appropriate visualization at each map scale. This service should only be used for visualization, such as a base layer in applications or maps. Note: If access to non-scaled slope values is required, use the Slope Degrees or Slope Percent functions, which return values from 0 to 90 degrees, or 0 to 1000%, respectively.Units: DegreesUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: Yes. This colorized slope is appropriate for visualizing the steepness of the terrain at all map scales. This layer can be added to applications or maps to enhance contextual understanding. Use for Analysis: No. 8 bit color values returned by this service represent scaled slope values. For analysis with non-scaled values, use the Slope Degrees or Slope Percent functions.For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single export image request.

This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Digital Map Service Market Outlook

The global digital map service market size is projected to grow significantly, from approximately $18.9 billion in 2023 to an estimated $53.1 billion by 2032, reflecting a compelling Compound Annual Growth Rate (CAGR) of 12.5%. This robust growth is driven by the increasing adoption of digital mapping technologies across diverse industries and the rising demand for real-time geographic and navigation data in both consumer and enterprise applications.

One of the primary growth factors for the digital map service market is the expanding use of digital maps in the automotive sector, particularly in the development of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. These technologies rely heavily on precise and up-to-date mapping data for navigation, obstacle detection, and other functionalities, making digital maps indispensable. Additionally, the proliferation of mobile devices and the integration of mapping services in applications such as ride-sharing, logistics, and local search have significantly contributed to market expansion.

Another significant driver is the increasing reliance on Geographic Information Systems (GIS) across various industries. GIS technology enables organizations to analyze spatial information, improve decision-making processes, and enhance operational efficiencies. Industries such as government, defense, agriculture, and urban planning utilize GIS for land use planning, disaster management, and resource allocation, among other applications. The continuous advancements in GIS technology and the integration of artificial intelligence (AI) and machine learning (ML) are expected to further propel market growth.

The rising demand for real-time location data is also a crucial factor fueling the growth of the digital map service market. Real-time location data is essential for applications such as fleet management, asset tracking, and public safety. Businesses leverage this data to optimize routes, monitor assets, and enhance customer service. The increasing implementation of Internet of Things (IoT) devices and the growing importance of location-based services are likely to sustain the demand for real-time mapping solutions in the coming years.

Regionally, North America leads the digital map service market, driven by the high adoption rate of advanced technologies and the presence of major players in the region. However, the Asia Pacific region is expected to witness the fastest growth, attributed to rapid urbanization, increasing smartphone penetration, and government initiatives to develop smart cities. Europe, Latin America, and the Middle East & Africa are also anticipated to experience substantial growth, fueled by the rising demand for digital mapping solutions across various sectors.

Service Type Analysis

In the digital map service market, the service type segment includes mapping and navigation, geographic information systems (GIS), real-time location data, and others. Mapping and navigation services hold a significant share in the market, primarily due to their extensive use in personal and commercial navigation systems. These services provide detailed road maps, traffic updates, and route planning, which are essential for everyday commuting and logistics operations. The continuous advancements in navigation technologies, such as integration with AI and ML for predictive analytics, are expected to enhance the accuracy and functionality of these services.

Geographic Information Systems (GIS) represent another critical segment within the digital map service market. GIS technology is widely used in various applications, including urban planning, environmental management, and disaster response. The ability to analyze and visualize spatial data in multiple layers allows organizations to make informed decisions and optimize resource allocation. The integration of GIS with other emerging technologies, such as drones and remote sensing, is further expanding its application scope and driving market growth.

Real-time location data services are gaining traction due to their importance in applications like fleet management, asset tracking, and location-based services. These services provide up-to-the-minute information on the geographical position of assets, vehicles, or individuals, enabling businesses to improve operational efficiency and customer satisfaction. The growing adoption of IoT devices and the increasing need for real-time visibility in supply chain operations are expected to bolster the demand for real-time location data services.</p&

This story map explains how to use two attributes to make a map using both color and size using the smart mapping capability within ArcGIS Online and ArcGIS Enterprise. You can easily select two attributes, and one will be shown in your map using color, while the other will be used to represent size. This mapping technique can help to show relationships you might not have known existed. This method can also help turn multiple maps into a single map to share with others. This story map walks you through multiple examples, which can help get you started with smart mapping color and size.

The 3D Visualisation Map (Individualised models) are a set of digital data of 3D models featuring geometry models and texture maps to represent the geometrical shape, appearance and position of different types of ground objects, including building, infrastructure, vegetation, site, waterbody, terrain and generic (others). The dataset covers the whole territory of Hong Kong. You can click the link below to access the 3D Visualisation Map (https://3d.map.gov.hk/).

The Modern Antique Map (World Edition) web map provides a world basemap symbolized with a unique antique styled map, with a modern flair -- including the benefit of multi-scale mapping. The comprehensive map data includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. This basemap, included in the ArcGIS Living Atlas of the World, uses the Modern Antique vector tile layer and World Hillshade.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.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 layers referenced in this map.

The Economic Development web map is used to author the Economic Development Experience Builder application. It displays the economic development districts, enterprise zones, industrial areas, economic development zones, Baton Rouge Airport property, and Louisiana Opportunity Zones data in East Baton Rouge Parish, Louisiana.