At Driver Technologies, we specialize in collecting high-quality, highly-anonymized, driving data crowdsourced using our dash cam app. Our Traffic Light Map Video Data is built from the millions of miles of driving data captured and is optimized to be trained for whatever computer vision models you need and enhancing various applications in transportation and safety.
What Makes Our Data Unique? What sets our Traffic Light Map Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes road signs, pedestrians, vehicles, traffic signs, and road conditions, resulting in rich, annotated datasets that can be used for a range of applications.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.
Primary Use-Cases and Verticals The Traffic Light Map Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:
Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.
Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic light placement. Our data can also aid in making sure municipalities have an accurate count of signs in their area.
Integration with Our Broader Data Offering The Traffic Light Map Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and computer vision models.
In summary, Driver Technologies' Traffic Light Map Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Traffic Light Map Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).
SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.
SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation. Easily ingest this data to power your map products today.
Digital Map Market Size 2025-2029
The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.
The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.
What will be the Size of the Digital Map Market during the forecast period?
Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.
Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. 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.
How is this Digital Map Industry segmented?
The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
Application
Navigation
Geocoders
Others
Type
Outdoor
Indoor
Solution
Software
Services
Deployment
On-premises
Cloud
Geography
North America
US
Canada
Europe
France
Germany
UK
APAC
China
India
Indonesia
Japan
South Korea
Rest of World (ROW)
By Application Insights
The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.
Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
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Depicted on this map is British North America less than one hundred years after the fall of New France. It also shows the emergence of British influence prior to Confederation. British North America circa 1823 was comprised of Lower Canada, Upper Canada, New Brunswick, Nova Scotia, Prince Edward Island, and Newfoundland (including the Labrador Coast). The Northwest Territories were considered British possessions, while the Hudson’s Bay Company controlled Rupert’s Land. The United States and Britain jointly administered the Oregon Territory. This map along with New France circa 1740 shows the settlement and population in Canada for two important periods in Canadian history prior to Confederation.
Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
Layered GeoPDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features.
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National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...
Geographic Information System Analytics Market Size 2024-2028
The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.
The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.
What will be the Size of the GIS Analytics Market during the forecast period?
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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.
How is this Geographic Information System Analytics Industry segmented?
The geographic information system analytics 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.
End-user
Retail and Real Estate
Government
Utilities
Telecom
Manufacturing and Automotive
Agriculture
Construction
Mining
Transportation
Healthcare
Defense and Intelligence
Energy
Education and Research
BFSI
Components
Software
Services
Deployment Modes
On-Premises
Cloud-Based
Applications
Urban and Regional Planning
Disaster Management
Environmental Monitoring Asset Management
Surveying and Mapping
Location-Based Services
Geospatial Business Intelligence
Natural Resource Management
Geography
North America
US
Canada
Europe
France
Germany
UK
APAC
China
India
South Korea
Middle East and Africa
UAE
South America
Brazil
Rest of World
By End-user Insights
The retail and real estate segment is estimated to witness significant growth during the forecast period.
The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.
The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,
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Geologic Map of North America (Southern Sheet). Map published by Geological Society of America; database by U.S. Geological Survey. See Catalogue of Registered Services for more information.
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License information was derived automatically
Thematic map of foreign guests from the Netherlands, Belgium, Great Britain and the USA in counties and associations. The share of all guests in %.:Guests from the United Kingdom (share of all guests in %) in Rhineland-Palatinate is proven at the association community level.
UK Territorial Sea LimitThe limits and boundaries of the UK, UK Overseas Territories and UK Crown Dependencies are available from this website in accordance with Articles 16, 74 and 84 of the United Nations Convention on the Law of the Sea. Limits are calculated from the normal baseline (the low water line on the largest UKHO charts) and limits are maintained by UKHO. Please note that these limits will only be updated annually.From https://www.admiralty.co.uk/ukho/About-Us
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
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Depicted on this map is the extent of New France at its territorial height circa 1740 prior to its great territorial losses to British North America. Also shown on the map are the territorial claims, administrative divisions, and the distribution of population and settlement (including fur trading posts) circa 1740. This map along with British North America circa 1823 shows the settlement and population in Canada for two important periods in Canadian history prior to Confederation.
Environmental Sensitivity Index (ESI) maps are an integral component in oil-spill contingency planning and assessment. They serve as a source of information in the event of an oil spill incident. ESI maps contain three types of information: shoreline habitats (classified according to their sensitivity to oiling), sensitive biological resources, and human-use resources. Most often, this information is plotted on 7.5 minute USGS quadrangles, although in the Alaska ESI maps, USGS topographic maps at scales of 1:63,360 and 1:250,000 are used, and in other ESI maps, NOAA charts have been used as the base map. Collections of these maps, grouped by state or a logical geographic area, are published as ESI atlases. Digital data have been published for most of the U.S. shoreline, including Alaska, Hawaii, and Puerto Rico.
Layer-Group type layer: all
Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information
Portrayal in which units are categorized according to the representative lithology from the CGI SimpleLithology vocabulary as specified by the representativeLithology_URI property in the underlying dataset. The data in this layer are portrayed based on lithology using the color scheme encoded in http://schemas.usgin.org/schemas/slds/LithologyCGI201001URI.sld. Lithology for polygons was assigned by intersecting polygon from Reed at al, 2005 with polygons in the state geologic map compilation for the lower 48 states by the USGS Mineral Resources division. In that map compilation, lithology was generalized to a major and minor rock type using the scheme documented in Ludington et al. 2007 (also known as LithClass 6). The LithClass6 categories were mapped into the CGI Simple Lithology vocabulary (see https://www.seegrid.csiro.au/wiki/CGIModel/ConceptDefinitionsTG) for OneGeology data integration by SM Richard. Lithology for polygons in Alaska, marine areas, Puerto Rico and Virgin Islands is based on mapping of lithogenetic categories from Reed et al, 2005 into the CGI simple Lithology vocabulary (see https://www.seegrid.csiro.au/wiki/CGIModel/ConceptDefinitionsTG) for OneGeology data integration by SM Richard. Lithology for polygons in Alaska, marine areas, Puerto Rico and Virgin Islands is based on mapping of lithogenetic categories from Reed et al, 2005 into the CGI simple Lithology vocabulary (see https://www.seegrid.csiro.au/wiki/CGIModel/ConceptDefinitionsTG) for OneGeology data integration by SM Richard.
Street Noise-Level — Statistically Interpolated + Processed Measurements
Connect with our experts for the world’s most comprehensive Street Noise-Level Dataset. Access hyper-local and global average noise levels (dBA) from public streets across over 200 countries. This dataset, built using over 35 billion datapoints and developed in collaboration with leading acoustics professionals, provides unparalleled insight into real-world urban soundscapes. Unlike conventional noise models, which rely solely on simulations, our dataset combines real measurements with AI-powered interpolation to deliver statistically robust, highly accurate, and spatially complete noise-level data.
Power Your AI & Urban Analytics with Real-World Noise Insights
What makes this dataset unique? Silencio’s processed and interpolated Street Noise-Level Dataset is the largest and most precise global collection of acoustic data available. It integrates real user-collected measurements with AI-driven modeling, ensuring unmatched ground truth for AI training, urban intelligence, and noise-impact assessments.
Optimized for AI, Urban Planning & Research:
Empower your AI models and spatial analyses with rich, diverse, and realistic noise data. Ideal for sound recognition, smart cities, mobility modeling, noise mapping, real estate analysis, and sustainable urban planning.
Trusted & Compliant:
All data is collected via our mobile app, strictly anonymized, fully consented, and 100% GDPR-compliant — ensuring privacy and ethical integrity.
Historical & Up-to-Date:
Leverage both historical and continuously updated noise data to uncover trends, detect change, and power predictive models.
Hyper-Local & Global Coverage:
With coverage of over 200 countries and high spatial granularity, the dataset provides insights from the city level down to street segments.
Seamless Integration:
Delivered via CSV exports or S3 bucket delivery (APIs coming soon) for easy integration into AI training pipelines, geospatial tools, or analytics platforms.
World Elevation layers are compiled from many authoritative data providers, and are updated quarterly. This map shows the extent of the various datasets comprising the World Elevation dynamic (Terrain, TopoBathy) and tiled (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) services.The tiled services (Terrain 3D, TopoBathy 3D, World Hillshade, World Hillshade (Dark)) also include an additional data source from Maxar's Precision3D covering parts of the globe.Topography sources listed in the table below are part of Terrain, TopoBathy, Terrain 3D, TopoBathy 3D, World Hillshade and World Hillshade (Dark), while bathymetry sources are part of TopoBathy and TopoBathy 3D only. Data Source Native Pixel Size Approximate Pixel Size (meters) Coverage Primary Source Country/Region
Topography
Australia 1m 1 meter 1 Partial areas of Australia Geoscience Australia Australia
Moreton Bay, Australia 1m 1 meter 1 Moreton Bay region, Australia Moreton Bay Regional Council Australia
New South Wales, Australia 5m 5 meters 5 New South Wales State, Australia DFSI Australia
SRTM 1 arc second DEM-S 0.0002777777777779 degrees 31 Australia Geoscience Australia Australia
Burgenland 50cm 0.5 meters 0.5 Burgenland State, Austria Land Burgenland Austria
Upper Austria 50cm 0.5 meters 0.5 Upper Austria State, Austria Land Oberosterreich Austria
Austria 1m 1 meter 1 Austria BEV Austria
Austria 10m 10 meters 10 Austria BEV Austria
Wallonie 50cm 0.5 meters 0.5 Wallonie state, Belgium Service public de Wallonie (SPW) Belgium
Vlaanderen 1m 1 meter 1 Vlaanderen state, Belgium agentschap Digitaal Vlaanderen Belgium
Canada HRDEM 1m 1 meter 1 Partial areas of Canada Natural Resources Canada Canada
Canada HRDEM 2m 2 meter 2 Partial areas of the southern part of Canada Natural Resources Canada Canada
Denmark 40cm 0.4 meters 0.4 Denmark KDS Denmark
Denmark 10m 10 meters 10 Denmark KDS Denmark
England 1m 1 meter 1 England Environment Agency England
Estonia 1m 1 meter 1 Estonia Estonian Land Board Estonia
Estonia 5m 5 meters 5 Estonia Estonian Land Board Estonia
Estonia 10m 10 meters 10 Estonia Estonian Land Board Estonia
Finland 2m 2 meters 2 Finland NLS Finland
Finland 10m 10 meters 10 Finland NLS Finland
France 1m 1 meter 1 France IGN-F France
Bavaria 1m 1 meter 1 Bavaria State, Germany Bayerische Vermessungsverwaltung Germany
Berlin 1m 1 meter 1 Berlin State, Germany Geoportal Berlin Germany
Brandenburg 1m 1 meter 1 Brandenburg State, Germany GeoBasis-DE/LGB Germany
Hamburg 1m 1 meter 1 Hamburg State, Germany LGV Hamburg Germany
Hesse 1m 1 meter 1 Hesse State, Germany HVBG Germany
Nordrhein-Westfalen 1m 1 meter 1 Nordrhein-Westfalen State, Germany Land NRW Germany
Saxony 1m 1 meter 1 Saxony State, Germany Landesamt für Geobasisinformation Sachsen (GeoSN) Germany
Sachsen-Anhalt 2m 2 meters 2 Sachsen-Anhalt State, Germany LVermGeo LSA Germany
Hong Kong 50cm 0.5 meters 0.5 Hong Kong CEDD Hong Kong SAR
Italy TINITALY 10m 10 meters 10 Italy INGV Italy
Japan DEM5A *, DEM5B * 0.000055555555 degrees 5 Partial areas of Japan GSI Japan
Japan DEM10B * 0.00011111111 degrees 10 Japan GSI Japan
Latvia 1m 1 meters 1 Latvia Latvian Geospatial Information Agency Latvia
Latvia 10m 10 meters 10 Latvia Latvian Geospatial Information Agency Latvia
Latvia 20m 20 meters 20 Latvia Latvian Geospatial Information Agency Latvia
Lithuania 1m 1 meters 1 Lithuania NZT Lithuania
Lithuania 10m 10 meters 10 Lithuania NZT Lithuania
Netherlands (AHN3/AHN4) 50cm 0.5 meters 0.5 Netherlands AHN Netherlands
Netherlands (AHN3/AHN4) 10m 10 meters 10 Netherlands AHN Netherlands
New Zealand 1m 1 meters 1 Partial areas of New Zealand Land Information New Zealand (Sourced from LINZ. CC BY 4.0) New Zealand
Northern Ireland 10m 10 meters 10 Northern Ireland OSNI Northern Ireland
Norway 10m 10 meters 10 Norway NMA Norway
Poland 1m 1 meter 1 Partial areas of Poland GUGIK Poland
Poland 5m 5 meters 5 Partial areas of Poland GUGIK Poland
Scotland 1m 1 meter 1 Partial areas of Scotland Scottish Government et.al Scotland
Slovakia 1m 1 meter 1 Slovakia ÚGKK SR Slovakia
Slovakia 10m 10 meters 10 Slovakia GKÚ Slovakia
Slovenia 1m 1 meter 1 Slovenia ARSO Slovenia
Madrid City 1m 1 meter 1 Madrid city, Spain Ayuntamiento de Madrid Spain
Spain 2m (MDT02 2019 CC-BY 4.0 scne.es) 2 meters 2 Partial areas of Spain IGN Spain
Spain 5m 5 meters 5 Spain IGN Spain
Spain 10m 10 meters 10 Spain IGN Spain
Varnamo 50cm 0.5 meters 0.5 Varnamo municipality, Sweden Värnamo Kommun Sweden
Canton of Basel-Landschaft 25cm 0.25 meters 0.25 Canton of Basel-Landschaft, Switzerland Geoinformation Kanton Basel-Landschaft Switzerland
Grand Geneva 50cm 0.5 meters 0.5 Grand Geneva metropolitan, France/Switzerland SITG Switzerland and France
Switzerland swissALTI3D 50cm 0.5 meters 0.5 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein
Switzerland swissALTI3D 10m 10 meters 10 Switzerland and Liechtenstein swisstopo Switzerland and Liechtenstein
OS Terrain 50 50 meters 50 United Kingdom Ordnance Survey United Kingdom
Douglas County 1ft 1 foot 0.3048 Douglas County, Nebraska, USA Douglas County NE United States
Lancaster County 1ft 1 foot 0.3048 Lancaster County, Nebraska, USA Lancaster County NE United States
Sarpy County 1ft 1 foot 0.3048 Sarpy County, Nebraska, USA Sarpy County NE United States
Cook County 1.5 ft 1.5 foot 0.46 Cook County, Illinois, USA ISGS United States
3DEP 1m 1 meter 1 Partial areas of the conterminous United States, Puerto Rico USGS United States
NRCS 1m 1 meter 1 Partial areas of the conterminous United States NRCS USDA United States
San Mateo County 1m 1 meter 1 San Mateo County, California, USA San Mateo County CA United States
FEMA LiDAR DTM 3 meters 3 Partial areas of the conterminous United States FEMA United States
NED 1/9 arc second 0.000030864197530866 degrees 3 Partial areas of the conterminous United States USGS United States
3DEP 5m 5 meter 5 Alaska, United States USGS United States
NED 1/3 arc second 0.000092592592593 degrees 10 conterminous United States, Hawaii, Alaska, Puerto Rico, and Territorial Islands of the United States USGS United States
NED 1 arc second 0.0002777777777779 degrees 31 conterminous United States, Hawaii, Alaska, Puerto Rico, Territorial Islands of the United States; Canada and Mexico USGS United States
NED 2 arc second 0.000555555555556 degrees 62 Alaska, United States USGS United States
Wales 1m 1 meter 1 Wales Welsh Government Wales
WorldDEM4Ortho 0.00022222222 degrees 24 Global (excluding the countries of Azerbaijan, DR Congo and Ukraine) Airbus Defense and Space GmbH World
SRTM 1 arc second 0.0002777777777779 degrees 31 all land areas between 60 degrees north and 56 degrees south except Australia NASA World
EarthEnv-DEM90 0.00083333333333333 degrees 93 Global N Robinson,NCEAS World
SRTM v4.1 0.00083333333333333 degrees 93 all land areas between 60 degrees north and 56 degrees south except Australia CGIAR-CSI World
GMTED2010 7.5 arc second 0.00208333333333333 degrees 232 Global USGS World
GMTED2010 15 arc second 0.00416666666666666 degrees 464 Global USGS World
GMTED2010 30 arc second 0.0083333333333333 degrees 928 Global USGS World
Bathymetry
Bass Strait 30m 2022 0.0003 degrees 30 area of seabed between the coastlines of Victoria and northern Tasmania, extending approximately 460 km from west of King Island to east of Flinders Island. Geoscience Australia Australia
AusBathyTopo 2024 0.0025 degrees 250 Australian continent and Tasmania, and surrounding Macquarie Island and the Australian Territories of Norfolk Island, Christmas Island, and Cocos (Keeling) Islands. Geoscience Australia Australia
Canada west coast 10 meters 10 Canada west coast Natural Resources Canada Canada
Gulf of Mexico 40 feet 12 Northern Gulf of Mexico BOEM Gulf of Mexico
MH370 150 meters 150 MH370 flight search area (Phase 1) of Indian Ocean Geoscience Australia Indian Ocean
Switzerland swissBATHY3D 1 - 3 meters 1, 2, 3 Lakes of Switzerland swisstopo Switzerland
NCEI 1/9 arc second 0.000030864197530866 degrees 3 Puerto Rico, U.S Virgin Islands and partial areas of eastern and western United States coast NOAA NCEI United States
NCEI 1/3 arc second 0.000092592592593 degrees 10 Partial areas of eastern and western United States
Overview
Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.
Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.
The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.
Use cases for the Global Boundaries Database (GIS data, Geospatial data)
In-depth spatial analysis
Clustering
Geofencing
Reverse Geocoding
Reporting and Business Intelligence (BI)
Product Features
Coherence and precision at every level
Edge-matched polygons
High-precision shapes for spatial analysis
Fast-loading polygons for reporting and BI
Multi-language support
For additional insights, you can combine the GIS data with:
Population data: Historical and future trends
UNLOCODE and IATA codes
Time zones and Daylight Saving Time (DST)
Data export methodology
Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson
All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.
Why companies choose our map data
Precision at every level
Coverage of difficult geographies
No gaps, nor overlaps
Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.
At Driver Technologies, we specialize in collecting high-quality, highly-anonymized, driving data crowdsourced using our dash cam app. Our Traffic Light Map Video Data is built from the millions of miles of driving data captured and is optimized to be trained for whatever computer vision models you need and enhancing various applications in transportation and safety.
What Makes Our Data Unique? What sets our Traffic Light Map Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes road signs, pedestrians, vehicles, traffic signs, and road conditions, resulting in rich, annotated datasets that can be used for a range of applications.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.
Primary Use-Cases and Verticals The Traffic Light Map Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:
Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.
Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic light placement. Our data can also aid in making sure municipalities have an accurate count of signs in their area.
Integration with Our Broader Data Offering The Traffic Light Map Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and computer vision models.
In summary, Driver Technologies' Traffic Light Map Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Traffic Light Map Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.