This data provides the integrated cadastral framework for Canada Lands. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), registration plans (RS) and location sketches (LS) archived in the Canada Lands Survey Records.

The 2020 North American Land Cover 30-meter dataset was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between Natural Resources Canada, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad), and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country's specific requirements. This 30-meter dataset of North American Land Cover reflects land cover information for 2020 from Mexico and Canada, 2019 over the conterminous United States and 2021 over Alaska. Each country developed its own classification method to identify Land Cover classes and then provided an input layer to produce a continental Land Cover map across North America. Canada, Mexico, and the United States developed their own 30-meter land cover products; see specific sections on data generation below. The main inputs for image classification were 30-meter Landsat 8 Collection 2 Level 1 data in the three countries (Canada, the United States and Mexico). Image selection processes and reduction to specific spectral bands varied among the countries due to study-site-specific requirements. While Canada selected most images from the year 2020 with a few from 2019 and 2021, the Conterminous United States employed mainly images from 2019, while Alaska land cover maps are mainly based on the use of images from 2021. The land cover map for Mexico was based on land cover change detection between 2015 and 2020 Mexico Landsat 8 mosaics. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by CONABIO, INEGI, and CONAFOR; and for the United States by the USGS. Each country chose their own approaches, ancillary data, and land cover mapping methodologies to create national datasets. This North America dataset was produced by combining the national land cover datasets. The integration of the three national products merged four Land Cover map sections, Alaska, Canada, the conterminous United States and Mexico. See also: Natural Resources Canada has North American Land Cover information available online at https://open.canada.ca/data/en/dataset/ee1580ab-a23d-4f86-a09b-79763677eb47 The National Commission for the Knowledge and Use of Biodiversity has North American Land Cover information available online at https://www.biodiversidad.gob.mx/monitoreo/cobertura-suelo The U.S. Geological Survey has North American Land Cover information available online at www.mrlc.gov

Multi-temporal Local Climate Zone maps for seven functional urban areas (Calgary, Edmonton, Halifax, Montreal, Toronto, Vancouver, Winnipeg), and seven census years (1986, 1991, 1996, 2001, 2006, 2011, 2016). Regions of interest are defined by each cities' functional urban area, and the LCZ maps are available per city and census year, on a 100 m spatial resolution.

Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology was applied using optical (Landsat-5, AWiFS, DMC) and radar (Radarsat-2) based satellite images. Beginning with the 2011 growing season, this activity has been extended to other provinces in support of a national crop inventory. To date this approach can consistently deliver a crop inventory that meets the overall target accuracy of at least 85% at a final spatial resolution of 30m (56m in 2009 and 2010).

The 2020 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

The AAFC Land Use Time Series is a culmination and curated meta-analysis of several high-quality spatial datasets produced between 2000 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

The Government of Canada acquired a national image coverage from the Systeme Pour l'Observation de la Terre (SPOT 4 - 5) satellites that includes four multispectral bands in the visible to shortwave infrared region at 20m spatial resolution. Five years from 2005 - 2010 were necessary to image all of Canada under clear-sky conditions, while acquisition anniversary dates were less important provided the data were imaged during the snow-free period. These data were downloaded from the GeoBase Orthoimage 2005 - 2010 dataset (http://www.geobase.ca/geobase/en/data/imagery/imr/description.html) and used to map 2005 - 2010 land cover south of treeline. Northern Canada has not currently been remapped since circa 2000 due to technical challenges associated with land cover variability and image acquisition dates relative to short summers. This land cover product includes 16 generic classes based on plant functional and a minimum mapping unit of 20m. Radiometric normalization was applied to balance images acquired near mid-summer during the 'peak-of-season' temporal window. The combined Enhancement and Classification by Progressive Generalization methods were used to classify large-area balanced mosaics over twenty mapping zones. Image interpretation was guided using high resolution imagery and other content in Google Earth. Knowledge of land cover spectral signatures, field experience and published reports were also used to assist interpretation in many regions. Remaining images acquired outside the peak-of-season window in early spring or late fall were subsequently classified using decision trees trained on data from overlapping classified peak-of-season images. Accuracy was assessed using ground truth data acquired during several field campaigns conducted with other government departments such as Parks Canada and the Geological Survey. This sample was enhanced using points interpreted in Google Earth as described above to provide a more even spatial coverage of Canada. Overall accuracy assessed at 71% using 1566 reference points, more than half of which were acquired in the field. When assessed using only land cover that was homogeneous within 3 by 3 pixels to account for potential geolocation errors, accuracy increased to 85% for 349 points that were biased towards easily classified classes such as water.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The 2005 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

Approximate boundaries for all land parcels in New Brunswick. The boundaries are structured as Polygons. The Property Identifier number or PID is included for each parcel.

The Canada Geospatial Imagery Analytics market is experiencing robust growth, driven by increasing government investments in infrastructure development, precision agriculture adoption, and a heightened focus on environmental monitoring and urban planning. The market's Compound Annual Growth Rate (CAGR) of 21.76% from 2019 to 2024 suggests a significant expansion, projected to continue over the forecast period (2025-2033). Key market drivers include the rising availability of high-resolution satellite imagery, advancements in AI-powered image processing and analysis techniques, and the growing demand for real-time geospatial intelligence across various sectors. The cloud-based deployment model is gaining traction due to its scalability and cost-effectiveness, while large enterprises are the primary consumers, owing to their greater resources and need for sophisticated analytics. Significant growth is observed in verticals such as agriculture, where imagery analytics optimizes crop management and yield prediction, and in the defense and security sectors, which leverage it for surveillance and strategic planning. While data limitations prevent precise market sizing for Canada, extrapolating from the global market and considering Canada's robust technology sector, a reasonable estimate for the 2025 market size could be in the range of $150-200 million USD, with continued growth expected throughout the forecast period. The market segmentation reveals crucial insights into growth dynamics. The imagery analytics segment (including satellite, aerial and drone imagery) is likely to dominate due to its versatility. Within deployment modes, the cloud segment shows high potential, facilitated by the increasing affordability and accessibility of cloud computing resources. Large enterprises' greater financial capacity drives adoption of advanced analytics solutions, contributing significantly to market size. The insurance sector benefits from risk assessment and claims management using geospatial data, while the environmental monitoring sector utilizes it for resource management and impact assessment. Competition is expected to intensify among established players like Satellite Imaging Corporation, BAE Systems, and Google LLC, alongside emerging technology firms, spurring innovation and driving down costs. Potential restraints include data privacy concerns, the high initial investment costs associated with advanced analytics systems, and the need for skilled professionals to interpret the complex data generated. Recent developments include: October 2023: Canada will invest CAD 1.01 billion (USD 740.90 million) in satellite technology over the next 15 years to boost the Earth observation data it uses to track wildfires and other environmental crises. The new initiative called Radarsat+ will gather information about Earth's oceans, land, climate, and populated areas. Data collected from earth observation technologies allows scientists to see how the planet changes and make decisions for emergencies like wildfires or longer-term issues like climate change., December 2022: Carl Data Solutions, a player in predictive analytics for environmental monitoring as a service (EMaaS) and smart city applications driven by compliance, signed a strategic partnership agreement with K2 Geospatial, a Montreal-based company. Over 350 cities and municipalities, seaports, airports, road authorities, and utilities across North America and Europe are among the 500 organizations using K2 Geospatial. JMap, a spatial analysis mapping integration platform made to connect silos in a fully integrated IT ecosystem, is used by K2 users., In September 2022, CAPE Analytics, a provider of AI-powered geospatial property intelligence, partnered with weather technology firm Canopy Weather to launch a product for storm-related damage. Canopy Weather applies an application-first approach to deliver weather data products with specific, real-world business applications. Powered by machine learning, the rating indicates the likelihood of post-storm damage to roofs after severe weather. It can predict the subset of claims eligible for automated, straight-through processing with over 96% accuracy.. Key drivers for this market are: Increasing Demand for Location-based Services, Technological Innovations in Geospatial Imagery Services. Potential restraints include: Lack of Awareness About Benefits of Geospatial Imagery Services. Notable trends are: Increasing Adoption of 5G in Canada is Boosting the Market Growth.

Snapshot img

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.
Request Free Sample

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

The framework of the Cordilleran orogen of northwestern North America is commonly depicted as a 'collage' of terranes - crustal blocks containing records of a variety of geodynamic environments including continental fragments, pieces of island arc crust and oceanic crust. The series of maps available here are derived from a GIS compilation of terranes based on the map first published by Colpron et al. (2007) and more recently revised by Nelson et al. (2013). These maps are presented here in digital formats including ArcGIS file geodatabase (.gdb), shapefiles (.shp and related files), Google Earth (.kmz), as well as graphic files (.pdf). The GIS data includes terrane polygons and selected major Late Cretaceous and Tertiary strike-slip faults. Graphic PDF files derived from the GIS compilation were prepared for the Northern Cordillera (Alaska, Yukon and BC), the Canadian Cordillera (BC and Yukon), Yukon, and British Columbia. These maps are intended for page-size display (~1:5,000,000 and smaller). Polygons are accurate to ~1 km for Yukon and BC, ~5 km for Alaska. More detailed geological data are available from both BCGC, USGS and YGS websites. Descriptions of the terranes, their tectonic evolution and metallogeny can be found in Colpron et al. (2007), Nelson and Colpron (2007), Colpron and Nelson (2009), Nelson et al. (2013) and references therein. The terrane map project is a collaborative effort of the BC Geological Survey and the Yukon Geological Survey. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca

The Maine Geoparcel Viewer Application allows users to search and view available digital parcel data for Organized Townships and Unorganized Territories in the State of Maine. The Maine GeoLibrary and the Maine Office of GIS do not maintain parcel data for communities, cannot verify parcel ownership, and are not responsible for individual parcel data verification or updating emergency records concerning parcel addresses. If you have questions about a specific parcel, please contact the appropriate Town Office or County Registry of Deeds for the most up-to-date information.Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. The "Maine Parcels Organized Towns Feature" layer and "Maine Parcels Organized Towns ADB" table are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data; some data are more than ten years old. Please contact the appropriate Town Office or the County Registry of Deeds for more up-to-date parcel information. Organized Town data should very closely match registry information, except in the case of in-process property conveyance transactions.In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), Maine Revenue Services is the authoritative source for parcel data. The "Maine Parcels Unorganized Territory" layer is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services to compile up-to-date parcel ownership information.

The 2000 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

ParcelMap BC is the current, complete and trusted mapped representation of titled and Crown land parcels across British Columbia, considered to be the point of truth for the graphical representation of property boundaries. It is not the authoritative source for the legal property boundary or related records attributes; this will always be the plan of survey or the related registry information. This particular dataset is a subset of the complete ParcelMap BC data and is comprised of the parcel fabric and attributes for over two million parcels published under the Open Government Licence - British Columbia. Notes: 1. Parcel title information is sourced from the BC Land Title Register. Title questions should be directed to a local Land Title Office. 2. This dataset replaces the Integrated Cadastral Fabric.

The framework of the Cordilleran orogen of northwestern North America is commonly depicted as a 'collage' of terranes - crustal blocks containing records of a variety of geodynamic environments including continental fragments, pieces of island arc crust and oceanic crust. The series of maps available here are derived from a GIS compilation of terranes based on the map first published by Colpron et al. (2007) and more recently revised by Nelson et al. (2013). These maps are presented here in digital formats including ArcGIS file geodatabase (.gdb), shapefiles (.shp and related files), Google Earth (.kmz), as well as graphic files (.pdf). The GIS data includes terrane polygons and selected major Late Cretaceous and Tertiary strike-slip faults. Graphic PDF files derived from the GIS compilation were prepared for the Northern Cordillera (Alaska, Yukon and BC), the Canadian Cordillera (BC and Yukon), Yukon, and British Columbia. These maps are intended for page-size display (~1:5,000,000 and smaller). Polygons are accurate to ~1 km for Yukon and BC, ~5 km for Alaska. More detailed geological data are available from both BCGC, USGS and YGS websites. Descriptions of the terranes, their tectonic evolution and metallogeny can be found in Colpron et al. (2007), Nelson and Colpron (2007), Colpron and Nelson (2009), Nelson et al. (2013) and references therein. The terrane map project is a collaborative effort of the BC Geological Survey and the Yukon Geological Survey. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca