Maps and Apps Gallery is a configurable group app template that can be used for displaying a collection of maps, applications, documents, and layers. Gallery contents are searchable and can be filtered using item tags. Private gallery content can be accessed by signing in to the app using your ArcGIS credentials.Use Casesbuilding a common operational picture organizing a series of maps & apps for a community eventConfigurable OptionsConfigure Maps and Apps Gallery to present content from any group in your organization and personalize the app by modifying the following options: Display a custom title and logo in the application headerUse a custom color schemeChoose between grid- and list-style layoutsEnable or disable the tag cloud which can be used to filter the items displayed in the galleryChoose to open maps and layers in ArcGIS Online, or to preview them in the app's viewerSupported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsMaps and Apps Gallery will display all item types supported by ArcGIS Online and Portal, although sharing maps is preferable to sharing stand-alone layers.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a group and choose to create a web appOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.Learn MoreFor release notes and more information on configuring this app, see the Maps and Apps Gallery documentation.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

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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 applications,

This application is intended for informational purposes only and is not an operational product. The tool provides the capability to access, view and interact with satellite imagery, and shows the latest view of Earth as it appears from space.For additional imagery from NOAA's GOES East and GOES West satellites, please visit our Imagery and Data page or our cooperative institute partners at CIRA and CIMSS.This website should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. In addition, we do not provide weather forecasts on this site — that is the mission of the National Weather Service. Please contact them for any forecast questions or issues. Using the Maps​What does the Layering Options icon mean?The Layering Options widget provides a list of operational layers and their symbols, and allows you to turn individual layers on and off. The order in which layers appear in this widget corresponds to the layer order in the map. The top layer ‘checked’ will indicate what you are viewing in the map, and you may be unable to view the layers below.Layers with expansion arrows indicate that they contain sublayers or subtypes.What does the Time Slider icon do?The Time Slider widget enables you to view temporal layers in a map, and play the animation to see how the data changes over time. Using this widget, you can control the animation of the data with buttons to play and pause, go to the previous time period, and go to the next time period.Do these maps work on mobile devices and different browsers?Yes!Why are there black stripes / missing data on the map?NOAA Satellite Maps is for informational purposes only and is not an operational product; there are times when data is not available.Why does the imagery load slowly?This map viewer does not load pre-generated web-ready graphics and animations like many satellite imagery apps you may be used to seeing. Instead, it downloads geospatial data from our data servers through a Map Service, and the app in your browser renders the imagery in real-time. Each pixel needs to be rendered and geolocated on the web map for it to load.How can I get the raw data and download the GIS World File for the images I choose?The geospatial data Map Service for the NOAA Satellite Maps GOES satellite imagery is located on our Satellite Maps ArcGIS REST Web Service ( available here ).We support open information sharing and integration through this RESTful Service, which can be used by a multitude of GIS software packages and web map applications (both open and licensed).Data is for display purposes only, and should not be used operationally.Are there any restrictions on using this imagery?NOAA supports an open data policy and we encourage publication of imagery from NOAA Satellite Maps; when doing so, please cite it as "NOAA" and also consider including a permalink (such as this one) to allow others to explore the imagery.For acknowledgment in scientific journals, please use:We acknowledge the use of imagery from the NOAA Satellite Maps application: LINKThis imagery is not copyrighted. You may use this material for educational or informational purposes, including photo collections, textbooks, public exhibits, computer graphical simulations and internet web pages. This general permission extends to personal web pages. About this satellite imageryWhat am I looking at in these maps?In this map you are seeing the past 24 hours (updated approximately every 10 minutes) of the Western Hemisphere and Pacific Ocean, as seen by the NOAA GOES East (GOES-16) and GOES West (GOES-18) satellites. In this map you can also view four different ‘layers’. The views show ‘GeoColor’, ‘infrared’, and ‘water vapor’.This maps shows the coverage area of the GOES East and GOES West satellites. GOES East, which orbits the Earth from 75.2 degrees west longitude, provides a continuous view of the Western Hemisphere, from the West Coast of Africa to North and South America. GOES West, which orbits the Earth at 137.2 degrees west longitude, sees western North and South America and the central and eastern Pacific Ocean all the way to New Zealand.What does the GOES GeoColor imagery show?The 'Merged GeoColor’ map shows the coverage area of the GOES East and GOES West satellites and includes the entire Western Hemisphere and most of the Pacific Ocean. This imagery uses a combination of visible and infrared channels and is updated approximately every 15 minutes in real time. GeoColor imagery approximates how the human eye would see Earth from space during daylight hours, and is created by combining several of the spectral channels from the Advanced Baseline Imager (ABI) – the primary instrument on the GOES satellites. The wavelengths of reflected sunlight from the red and blue portions of the spectrum are merged with a simulated green wavelength component, creating RGB (red-green-blue) imagery. At night, infrared imagery shows high clouds as white and low clouds and fog as light blue. The static city lights background basemap is derived from a single composite image from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band. For example, temporary power outages will not be visible. Learn more.What does the GOES infrared map show?The 'GOES infrared' map displays heat radiating off of clouds and the surface of the Earth and is updated every 15 minutes in near real time. Higher clouds colorized in orange often correspond to more active weather systems. This infrared band is one of 12 channels on the Advanced Baseline Imager, the primary instrument on both the GOES East and West satellites. on the GOES the multiple GOES East ABI sensor’s infrared bands, and is updated every 15 minutes in real time. Infrared satellite imagery can be "colorized" or "color-enhanced" to bring out details in cloud patterns. These color enhancements are useful to meteorologists because they signify “brightness temperatures,” which are approximately the temperature of the radiating body, whether it be a cloud or the Earth’s surface. In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are usually “clear sky,” while pale white areas typically indicate low-level clouds. During a hurricane, cloud top temperatures will be higher (and colder), and therefore appear dark red. This imagery is derived from band #13 on the GOES East and GOES West Advanced Baseline Imager.How does infrared satellite imagery work?The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.What do the colors on the infrared map represent?In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are clear sky, while pale white areas indicate low-level clouds, or potentially frozen surfaces. Learn more about this weather imagery.What does the GOES water vapor map layer show?The GOES ‘water vapor’ map displays the concentration and location of clouds and water vapor in the atmosphere and shows data from both the GOES East and GOES West satellites. Imagery is updated approximately every 15 minutes in real time. Water vapor imagery, which is useful for determining locations of moisture and atmospheric circulations, is created using a wavelength of energy sensitive to the content of water vapor in the atmosphere. In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate little or no moisture present. This imagery is derived from band #10 on the GOES East and GOES West Advanced Baseline Imager.What do the colors on the water vapor map represent?In this imagery, green-blue and white areas indicate the presence of high water vapor or moisture content, whereas dark orange and brown areas indicate less moisture present. Learn more about this water vapor imagery.About the satellitesWhat are the GOES satellites?NOAA’s most sophisticated Geostationary Operational Environmental Satellites (GOES), known as the GOES-R Series, provide advanced imagery and atmospheric measurements of Earth’s Western Hemisphere, real-time mapping of lightning activity, and improved monitoring of solar activity and space weather.The first satellite in the series, GOES-R, now known as GOES-16, was launched in 2016 and is currently operational as NOAA’s GOES East satellite. In 2018, NOAA launched another satellite in the series, GOES-T, which joined GOES-16 in orbit as GOES-18. GOES-17 became operational as GOES West in January 2023.Together, GOES East and GOES West provide coverage of the Western Hemisphere and most of the Pacific Ocean, from the west coast of Africa all the way to New Zealand. Each satellite orbits the Earth from about 22,200 miles away.

The Download Tool is available through CT ECO, a partnership between UConn CLEAR and CT DEEP. The tool provides easy download access to aerial imagery and lidar elevation collected during multiple flights.

Overview

3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

Our corresponding paper (published at ITSC 2022) is available here.
Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

• Python tools to read, generate, and visualize the dataset,
• 3DHDNet deep learning pipeline (training, inference, evaluation) for
  map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@INPROCEEDINGS{9921866,
  author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and 
  Fingscheidt, Tim},
  booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, 
  title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, 
  year={2022},
  pages={627-634}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

• Benjamin Sertolli: Major contributions to our DevKit during his master thesis
• Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json"
with open(json_path) as jf:
  data = json.load(jf)
print(data)

2. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

• traffic signs,
• traffic lights,
• pole-like objects,
• construction site locations,
• construction site obstacles (point-like such as cones, and line-like such as fences),
• line-shaped markings (solid, dashed, etc.),
• polygon-shaped markings (arrows, stop lines, symbols, etc.),
• lanes (ordinary and temporary),
• relations between elements (only for construction sites, e.g., sign to lane association).

3. HD_Map_MetaData

Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

4. HD_PointCloud_Tiles

The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

• x-coordinates: 4 byte integer
• y-coordinates: 4 byte integer
• z-coordinates: 4 byte integer
• intensity of reflected beams: 2 byte unsigned integer
• ground classification flag: 1 byte unsigned integer

After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

import numpy as np
import pptk

file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin"
pc_dict = {}
key_list = ['x', 'y', 'z', 'intensity', 'is_ground']
type_list = ['

Retirement Notice: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map Viewer To show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021 By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this: 4. Click the styles button.5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off. Showing just one pair of years in ArcGIS Pro To show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well. How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022 What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

HD Maps Market size was valued at USD 3.65 Billion in 2024 and is projected to reach USD 10.5 Billion by 2032, growing at a CAGR of 14.2 % during the forecast period 2026 to 2032. The rising adoption of autonomous and semi-autonomous vehicles is a major factor driving the HD Maps market. These maps provide the precise, centimeter-level accuracy required for self-driving systems to understand road environments and make safe navigation decisions. Increasing investments from automotive OEMs and technology firms in autonomous driving solutions are likely to accelerate HD map development and integration. For example, major automakers such as BMW, Toyota, and Mercedes-Benz are partnering with mapping providers like HERE and TomTom to enhance their ADAS and automated driving systems.

Retirement Notice: This item is in mature support as of May 2025 and will retire in December 2025. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item. This map features land cover data represent a descriptive thematic surface for characteristics of the land's surface such as densities or types of developed areas, agricultural lands, and natural vegetation regimes. Land cover data are the result of a model, so a good way to think of the values in each cell are as the predominating value rather than the only characteristic in that cell.Land use and land cover data are critical and fundamental for environmental monitoring, planning, and assessment. This web map uses Terrain with Labels vector layers as its basemap. Dataset Summary BaseVue 2013 is a commercial global, land use / land cover (LULC) product developed by MDA. BaseVue covers the Earth’s entire land area, excluding Antarctica. BaseVue is independently derived from roughly 9,200 Landsat 8 images and is the highest spatial resolution (30m), most current LULC product available. The capture dates for the Landsat 8 imagery range from April 11, 2013 to June 29, 2014. The following 16 classes of land use / land cover are listed by their cell value in this layer: Deciduous Forest: Trees > 3 meters in height, canopy closure >35% (<25% inter-mixture with evergreen species) that seasonally lose their leaves, except Larch. Evergreen Forest: Trees >3 meters in height, canopy closure >35% (<25% inter-mixture with deciduous species), of species that do not lose leaves. (will include coniferous Larch regardless of deciduous nature). Shrub/Scrub: Woody vegetation <3 meters in height, > 10% ground cover. Only collect >30% ground cover. Grassland: Herbaceous grasses, > 10% cover, including pasture lands. Only collect >30% cover. Barren or Minimal Vegetation: Land with minimal vegetation (<10%) including rock, sand, clay, beaches, quarries, strip mines, and gravel pits. Salt flats, playas, and non-tidal mud flats are also included when not inundated with water. Not Used (in other MDA products 6 represents urban areas or built up areas, which have been split here in into values 20 and 21). Agriculture, General: Cultivated crop lands Agriculture, Paddy: Crop lands characterized by inundation for a substantial portion of the growing season Wetland: Areas where the water table is at or near the surface for a substantial portion of the growing season, including herbaceous and woody species (except mangrove species) Mangrove: Coastal (tropical wetlands) dominated by Mangrove species Water: All water bodies greater than 0.08 hectares (1 LS pixel) including oceans, lakes, ponds, rivers, and streams Ice / Snow: Land areas covered permanently or nearly permanent with ice or snow Clouds: Areas where no land cover interpretation is possible due to obstruction from clouds, cloud shadows, smoke, haze, or satellite malfunction Woody Wetlands: Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate periodically is saturated with, or covered by water. Only used within the continental U.S. Mixed Forest: Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. Only used within the continental U.S. Not Used Not Used Not Used Not Used High Density Urban: Areas with over 70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation where constructed materials account for >60%. Commercial, industrial, and transportation i.e., Train stations, airports, etc. Medium-Low Density Urban: Areas with 30%-70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation, where constructed materials account for greater than 40%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.

This map service includes geology, center points of oil and gas fields, geologic provinces, and political boundaries in the Former Soviet Union. This compilation is part of an interim product of the U.S. Geological Survey's World Energy Project (WEP) and part of a series on CD-ROM. The data sets were designed originally in ESRI's ARC/INFO ver. 7.0.4 and 7.1.2 system.

Outdoor Navigation App Market Outlook

According to our latest research, the global Outdoor Navigation App market size reached USD 2.8 billion in 2024. The market is experiencing robust expansion with a recorded CAGR of 12.4% from 2025 to 2033. By the end of the forecast period, the market is projected to attain a value of USD 8.1 billion by 2033. This significant growth is primarily driven by the increasing adoption of smartphones, the rising popularity of outdoor recreational activities, and continuous advancements in GPS and mapping technologies.

One of the core growth drivers for the Outdoor Navigation App market is the surge in outdoor activities such as hiking, cycling, running, and boating. The growing awareness about health and wellness, particularly after the global pandemic, has led individuals to seek outdoor adventures, thereby increasing the demand for reliable navigation solutions. The integration of advanced features such as real-time weather updates, offline maps, and route optimization has further enhanced the user experience, making these apps indispensable tools for outdoor enthusiasts. The proliferation of affordable and high-performance smartphones equipped with GPS capabilities has made outdoor navigation accessible to a broader population, significantly contributing to market growth.

Another pivotal factor fueling the expansion of the Outdoor Navigation App market is the continuous technological evolution in mapping and geolocation services. Innovations such as augmented reality (AR) overlays, AI-driven route suggestions, and enhanced user interfaces have transformed the way users interact with navigation apps. The adoption of cloud-based solutions and seamless synchronization across multiple devices have also improved the usability and reliability of these apps, catering to both casual users and professionals. Furthermore, partnerships between app developers and outdoor gear manufacturers are resulting in integrated ecosystems that offer comprehensive solutions for navigation, safety, and performance tracking.

The commercial and governmental sectors are also playing a vital role in propelling the Outdoor Navigation App market. Commercial enterprises, such as tour operators, logistics companies, and outdoor sports event organizers, are increasingly leveraging navigation apps to enhance operational efficiency and customer experience. Meanwhile, government and defense agencies are utilizing these applications for search and rescue operations, disaster management, and public safety initiatives. The adoption of navigation apps in these sectors is supported by the need for real-time data, precise geolocation, and the ability to operate in remote or challenging environments, thereby expanding the market’s reach beyond individual consumers.

From a regional perspective, North America continues to dominate the Outdoor Navigation App market, followed closely by Europe and Asia Pacific. The high disposable income, widespread digital literacy, and a strong culture of outdoor recreation in North America have established the region as a primary market for navigation apps. Europe, with its diverse landscapes and active outdoor communities, is witnessing a steady increase in adoption rates. Meanwhile, the Asia Pacific region is emerging as a high-growth market due to rapid urbanization, increasing smartphone penetration, and government initiatives to promote outdoor tourism and adventure sports. Each region presents unique opportunities and challenges, shaping the overall dynamics of the global market.

Platform Analysis

The Platform segment of the Outdoor Navigation App market is a critical determinant of user adoption and engagement. Android currently holds the largest share due to the sheer volume of Android devices in circulation globally, especially in emerging markets. The open-source nature of Android allows developers to innovate rapidly and customize app functionalities to suit diverse user needs. The flexibility of the Android ecosystem, coupled with affordable device options, has made it the platform of choice for many outdoor enthusiasts. The extensive reach of the Google Play Store and integration with Google Maps further bolster the dominance of Android-based navigation apps.

iOS, on the other hand, commands a significant share of the premium segment. Apple’s devices are renowned for their robus

The real-time transit app market is experiencing robust growth, driven by increasing smartphone penetration, rising urbanization, and a growing demand for convenient and efficient public transportation. The market's expansion is fueled by several key trends, including the integration of advanced features like real-time tracking, multimodal journey planning (incorporating various transit modes), and personalized travel recommendations. Furthermore, the increasing adoption of cloud-based solutions offers scalability and cost-effectiveness for transit agencies and app developers. While data limitations prevent precise quantification, a reasonable estimate for the 2025 market size, considering similar technology markets, could be around $2 billion USD, with a Compound Annual Growth Rate (CAGR) of approximately 15% projected through 2033. This growth is expected across all segments, particularly in applications like urban transportation and school bus tracking where efficiency and safety are paramount. The competitive landscape is dynamic, featuring established players like Google Maps and Moovit alongside niche providers focusing on specific regions or transit systems. Challenges include maintaining data accuracy in real-time, integrating with diverse transit systems, and ensuring data privacy and security. The market segmentation reveals strong potential across both application (urban transportation, school buses, airport shuttles, etc.) and deployment (cloud-based and on-premises) types. The North American and European markets currently dominate, driven by high adoption rates and sophisticated infrastructure. However, significant opportunities exist in developing regions in Asia and Africa as smartphone penetration and urbanization continue to rise. The market's future depends on continued innovation in user experience, data integration, and the development of personalized and sustainable transportation solutions. Strategic partnerships between app developers, transit authorities, and technology providers will be crucial in driving further market expansion and addressing the challenges inherent in delivering accurate and reliable real-time transit information.

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GIS In Telecom Sector Market Size 2025-2029

The GIS in telecom sector market size is valued to increase USD 2.35 billion, at a CAGR of 15.7% from 2024 to 2029. Increased use of GIS for capacity planning will drive the GIS in telecom sector market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 28% growth during the forecast period.
By Product - Software segment was valued at USD 470.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 256.91 million
Market Future Opportunities: USD 2350.30 million
CAGR from 2024 to 2029: 15.7%

Market Summary

The market is experiencing significant growth as communication companies increasingly adopt Geographic Information Systems (GIS) for network planning and optimization. Core technologies, such as satellite imagery and location-based services, are driving this trend, enabling telecom providers to improve network performance and customer experience. One major application of GIS in the telecom sector is capacity planning, which allows companies to optimize their network infrastructure based on real-time data.
However, the integration of GIS with big data and other advanced technologies presents a communication gap between developers and end-users, requiring a focus on user-friendly interfaces and training programs. Additionally, regulatory compliance and data security remain significant challenges for the market. Despite these hurdles, the opportunities for innovation and improved operational efficiency make the market an exciting and evolving space.

What will be the Size of the GIS In Telecom Sector Market during the forecast period?

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How is the GIS In Telecom Sector Market Segmented ?

The GIS in telecom sector 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.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Application

  Mapping
  Telematics and navigation
  Surveying
  Location based services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

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

The global telecom sector's reliance on Geographic Information Systems (GIS) continues to expand, with the market for GIS in telecoms projected to grow significantly. According to recent industry reports, the market for GIS data visualization and spatial data infrastructure in telecoms has experienced a notable increase of 18.7% in the past year. Furthermore, the demand for advanced spatial analysis tools, such as building penetration analysis, geospatial asset management, and work order management systems, has risen by 21.3%. Telecom companies utilize GIS for network performance monitoring, data integration platforms, and network planning. For instance, GIS enables network design, radio frequency interference analysis, route optimization software, mobile network optimization, signal propagation modeling, and service area mapping.

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The Software segment was valued at USD 470.60 billion in 2019 and showed a gradual increase during the forecast period.

Additionally, it plays a crucial role in infrastructure management, location-based services, emergency response planning, maintenance scheduling, and telecom network design. Moreover, the adoption of 3D GIS modeling, LIDAR data processing, and customer location mapping has gained traction, contributing to the market's expansion. The future outlook is promising, with industry experts anticipating a 25.6% increase in the use of GIS for telecom network capacity planning and telecom outage prediction. These trends underscore the continuous evolution of the market and its applications across various sectors.

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Regional Analysis

APAC is estimated to contribute 28% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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In China, the construction of smart cities in Qingdao, Hangzhou, and Xiamen, among others, is driving the demand for Geographic Information Systems (GIS) in various sectors. By 2025, China aims to build more smart cities, leading to significant growth opportunities for GIS companies. Esri Global Inc., a leading player

Customer Journey Mapping Software Market size was valued to be USD 10.8 Billion in the year 2024 and it is expected to reach USD 53.6 Billion in 2032, at a CAGR of 13.8% over the forecast period of 2026 to 2032.

Customer Journey Mapping (CJM) software is a specialist application that helps organizations see and analyze the various stages of a customer's interaction with a firm. This program delivers a full and comprehensive perspective of the customer experience from the first contact to the final encounter allowing businesses to optimize their operations and increase overall customer satisfaction

. The essence of CJM software is its capacity to record and map out the customer journey in a visual manner such as a flowchart or diagram which aids in identifying pain points, understanding customer wants, and aligning business goals appropriately.

The primary application of customer journey mapping software is to improve customer experience (CX). Understanding the various stages and touchpoints of the customer journey allows firms to discover pain points and areas for improvement. For example, if a customer journey map shows that consumers regularly abandon their shopping carts at the payment stage, the company can investigate and fix the problem whether by streamlining the checkout process, providing clearer instructions, or offering more payment options.

CJMS will use advanced analytics to deliver more detailed insights into client behavior and preferences. The integration of big data and predictive analytics will enable organizations to anticipate client wants and identify possible problems before they arise. This proactive strategy will allow businesses to modify their services and interactions in real-time resulting in a smooth and rewarding consumer experience. Businesses will obtain a holistic picture of the consumer journey by analyzing massive volumes of data from multiple touchpoints revealing patterns and trends that can be used to guide strategic choices and optimize marketing efforts.

Enhanced Attention to Customer Experience (CX): The importance of delivering superior customer experiences for sustaining brand loyalty and boosting revenue is increasingly acknowledged by businesses. The ability of customer journey mapping software to enable businesses to pinpoint and refine customer interaction points throughout their journey is leading to enhanced CX and competitive differentiation.

Embracing Omnichannel Marketing: The engagement of modern consumers with brands through diverse platforms (including websites, social media, and mobile apps) is noted. The tracking of these multi-channel interactions and the understanding of customer behavior facilitated by customer journey mapping software assist in tailoring marketing efforts for better engagement.

The Requirement for Insights Based on Data: The necessity for insights driven by data in comprehending customer behavior and preferences is recognized by businesses. Through the aggregation and examination of customer information from various sources, customer journey mapping software offers critical insights for augmenting customer engagement and loyalty.

Regulatory Compliance Demands: Certain sectors are governed by regulations that enforce data privacy and security standards. Tools for meticulous tracking and management of customer information are provided by customer journey mapping software aiding businesses in meeting these regulatory requirements.

Increased Utilization Among SMBs: The adoption of customer journey mapping software previously more common among larger corporations, is now expanding to Small and Medium Businesses (SMBs). The appeal of this technology to a broader business spectrum is being enhanced by cloud-based solutions and subscription models.

Product sample point cloud web scene for LidarBC.Related layers, maps, and apps:Point Cloud Scene Layer (Natural Colour) - 0718ec455aa248a088e0aa21e7c8b2d4Point Cloud Scene Layer (Elevation) - 70921c1b32b34db0a41b7dec1fba7ae4Web Scene - ac1c4d84eea548afbb3dfa230991f4f3Experience Builder - fc381c8a322f4360886c6a2e5cde0e64

FROM PUNCH CARDS TO MOBILE APPS: A GEOLOGIST'S 40 YEAR ADVENTURE IN COMPUTING

ALLMENDINGER, Richard W., Department of Earth and Atmospheric Sciences, Cornell University, Snee Hall, Ithaca, NY 14853-1504

Few things have changed more than computing over the last 40 years: from slide rulers and expensive calculators (early 70s), punch cards (late 70s and early 80s), desktop computers with graphical user interfaces (mid-1980s to 1990s) laptop computers of the (1990s to mid-2000s), to the current explosion of mobile devices/apps along with the Internet/Cloud. I started developing apps in the mid-1980s and today, my desktop and mobile apps touch about 50,000 people per year. I will highlight two of my 12 major apps: Stereonet and GMDE (Geologic Map Data Extractor). Stereonet was first written and distributed in the 1980s for the Mac. Today it is available for the Mac, Windows, and Linux and, although it remains single-user focused, it has been expanded to include visualization of observations in a Google satellite view, export 3D symbols for plotting in Google Earth, and upload of data directly to the StraboSpot website/database, tagged with StraboSpot-specific nomenclature. Stereonet also made the jump to iOS where the user can, not only see and plot their data on their iPhone or iPad, but can also use device orientation to make basic measurements in the field. GMDE is also available for all three desktop platforms but not (yet) for mobile devices. In short, GMDE facilitates the task of extracting quantitative data from geologic maps and satellite imagery. A georeferenced basemap with realtime access to elevation at any point from internet elevation services makes it easy to leverage all of the information hidden in a century of high quality geologic mapping. GMDE specializes in structural calculations: 3-point and piercing point problems, rapid digitization of existing orientation symbols, topographic sections, and down-plunge projections as well as an integrated Google satellite view. The digitized data from a static, raster map can be analyzed quantitatively and shared over the Internet to enable new scientific studies. In the future, the algorithms in GMDE can be adapted to enable better geologic mapping itself by allowing the geologist to make realtime calculations in the field that can be interrogated immediately for their significance. After all, technology should not just make our lives easier but enable genuinely new science to be done. http://www.geo.cornell.edu/geology/faculty/RWA/programs/.