Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

The global map application market is experiencing robust growth, driven by the increasing penetration of smartphones, rising demand for location-based services, and the integration of advanced features like augmented reality and real-time traffic updates. Let's assume a 2025 market size of $15 billion, considering the significant investment and expansion in this sector. With a Compound Annual Growth Rate (CAGR) of 12% projected for the period 2025-2033, the market is poised to reach approximately $45 billion by 2033. This growth is fueled by several key trends: the development of more sophisticated navigation systems incorporating AI, the surge in the popularity of ride-sharing services heavily reliant on map apps, and the expanding use of maps in various industries such as logistics and delivery services. While factors like data privacy concerns and the competitive landscape pose some restraints, the overall outlook remains positive, driven by continuous innovation and increasing user adoption across both general and enterprise segments. The market is segmented by operating system (Android, iOS, Others) and user type (General, Enterprise), reflecting the diverse applications and user needs catered to by these apps. Geographic expansion is another significant factor, with North America and Europe currently leading the market, but substantial growth potential in Asia Pacific and other emerging regions. The competitive landscape is highly dynamic, with established players like Google Maps and Waze vying for market share alongside specialized players like OsmAnd and Citymapper catering to niche needs. The ongoing development of offline map functionality, improved accuracy, and enhanced user interfaces are key factors in maintaining user engagement and attracting new users. Further growth will depend on the ability of companies to leverage emerging technologies such as 5G and edge computing to deliver faster and more reliable location services. The integration of map apps with other services, creating seamless user experiences across various platforms and applications, presents a key area of future development. The continuous expansion of the market reflects a fundamental human need for navigation and location-based information which is amplified by the ever-increasing interconnected world.

It comprises end-user discussions on topics related to the Google Maps application on the Reddit Forum. A small dataset comprising user discussion about Google Maps application used for validating argumentation-based research approaches. A Python script for extracting end-user feedback from the Reddit forum by keeping the argumentative order of discussions (comment-reply).

Supporting information for: REMAP: An online remote sensing application for land cover classification and monitoringcsv and json files for implementing land cover classifications using the remap, the remote ecosystem assessment and monitoring pipeline (https://remap-app.org/)Nearmap aerial photograph courtesy of Nearmap Pty Ltd.For further information see:Murray, N.J., Keith, D.A., Simpson, D., Wilshire, J.H., Lucas, R.M. (accepted) REMAP: A cloud-based remote sensing application for generalized ecosystem classifications. Methods in Ecology and Evolution.

The aridity index also known as the dryness index is the ratio of potential evapotranspiration to precipitation. The aridity index indicates water deficiency. The aridity index is used to classify locations as humid or dry. The evaporation ratio (evaporation index) on the other hand indicates the availability of water in watersheds. The evaporation index is inversely proportional to water availability. For long periods renewable water resources availability is residual precipitation after evaporation loss is deducted. These two ratios provide very useful information about water availability. Understating the powerful potential of the aridity index and evaporation ratio, this app is developed on the Google Earth Engine using NLDAS-2 and MODIS products to map temporal variability of the Aridity Index and Evaporation ratio over CONUS. The app can be found at https://cartoscience.users.earthengine.app/view/aridity-index.

This archive contains native resolution and super resolution (SR) Landsat imagery, derivative lake shorelines, and previously-published lake shorelines derived airborne remote sensing, used here for comparison. Landsat images are from 1985 (Landsat 5) and 2017 (Landsat 8) and are cropped to study areas used in the corresponding paper and converted to 8-bit format. SR images were created using the model of Lezine et al (2021a, 2021b), which outputs imagery at 10x-finer resolution, and they have the same extent and bit depth as the native resolution scenes included. Reference shoreline datasets are from Kyzivat et al. (2019a and 2019b) for the year 2017 and Walter Anthony et al. (2021a, 2021b) for Fairbanks, AK, USA in 1985. All derived and comparison shoreline datasets are cropped to the same extent, filtered to a common minimum lake size (40 m2 for 2017; 13 m2 for 1985), and smoothed via 10 m morphological closing. The SR-derived lakes were determined to have F-1 scores of 0.75 (2017 data) and 0.60 (1985 data) as compared to reference lakes for lakes larger than 500 m2, and accuracy is worse for smaller lakes. More details are in the forthcoming accompanying publication.

All raster images are in cloud-optimized geotiff (COG) format (.tif) with file naming shown in Table 1. Vector shoreline datasets are in ESRI shapefile format (.shp, .dbf, etc.), and file names use the abbreviations LR for low resolution, SR for high resolution, and GT for “ground truth” comparison airborne-derived datasets.

Landsat-5 and Landsat-8 images courtesy of the U.S. Geological Survey

For an interactive map demo of these datasets via Google Earth Engine Apps, visit: https://ekyzivat.users.earthengine.app/view/super-resolution-demo

Table 1: File naming scheme based on region, with some regions requiring two-scene mosaics.

Region

Landsat ID

Mosaic name

Yukon Flats Basin

LC08_L2SP_068014_20170708_20200903_02_T1

LC08_20170708_yflats_cog.tif

“

LC08_L2SP_068013_20170708_20201015_02_T1

“

Old Crow Flats

LC08_L2SP_067012_20170903_20200903_02_T1

-

Mackenzie River Delta

LC08_L2SP_064011_20170728_20200903_02_T1

LC08_20170728_inuvik_cog.tif

“

LC08_L2SP_064012_20170728_20200903_02_T1

“

Canadian Shield Margin

LC08_L2SP_050015_20170811_20200903_02_T1

LC08_20170811_cshield-margin_cog.tif

“

LC08_L2SP_048016_20170829_20200903_02_T1

“

Canadian Shield near Baker Creek

LC08_L2SP_046016_20170831_20200903_02_T1

-

Canadian Shield near Daring Lake

LC08_L2SP_045015_20170723_20201015_02_T1

-

Peace-Athabasca Delta

LC08_L2SP_043019_20170810_20200903_02_T1

-

Prairie Potholes North 1

LC08_L2SP_041021_20170812_20200903_02_T1

LC08_20170812_potholes-north1_cog.tif

“

LC08_L2SP_041022_20170812_20200903_02_T1

“

Prairie Potholes North 2

LC08_L2SP_038023_20170823_20200903_02_T1

-

Prairie Potholes South

LC08_L2SP_031027_20170907_20200903_02_T1

-

Fairbanks

LT05_L2SP_070014_19850831_20200918_02_T1

-

References:

Kyzivat, E. D., Smith, L. C., Pitcher, L. H., Fayne, J. V., Cooley, S. W., Cooper, M. G., Topp, S. N., Langhorst, T., Harlan, M. E., Horvat, C., Gleason, C. J., & Pavelsky, T. M. (2019b). A high-resolution airborne color-infrared camera water mask for the NASA ABoVE campaign. Remote Sensing, 11(18), 2163. https://doi.org/10.3390/rs11182163

Kyzivat, E.D., L.C. Smith, L.H. Pitcher, J.V. Fayne, S.W. Cooley, M.G. Cooper, S. Topp, T. Langhorst, M.E. Harlan, C.J. Gleason, and T.M. Pavelsky. 2019a. ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1707

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021a). Super-resolution surface water mapping on the Canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Ekaterina M. D. Lezine, Kyzivat, E. D., & Smith, L. C. (2021b). Super-resolution surface water mapping on the canadian shield using planet CubeSat images and a generative adversarial network. Canadian Journal of Remote Sensing, 47(2), 261–275. https://doi.org/10.1080/07038992.2021.1924646

Walter Anthony, K.., Lindgren, P., Hanke, P., Engram, M., Anthony, P., Daanen, R. P., Bondurant, A., Liljedahl, A. K., Lenz, J., Grosse, G., Jones, B. M., Brosius, L., James, S. R., Minsley, B. J., Pastick, N. J., Munk, J., Chanton, J. P., Miller, C. E., & Meyer, F. J. (2021a). Decadal-scale hotspot methane ebullition within lakes following abrupt permafrost thaw. Environ. Res. Lett, 16, 35010. https://doi.org/10.1088/1748-9326/abc848

Walter Anthony, K., and P. Lindgren. 2021b. ABoVE: Historical Lake Shorelines and Areas near Fairbanks, Alaska, 1949-2009. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1859

What is SEPAL?

SEPAL (https://sepal.io/) is a free and open source cloud computing platform for geo-spatial data access and processing. It empowers users to quickly process large amounts of data on their computer or mobile device. Users can create custom analysis ready data using freely available satellite imagery, generate and improve land use maps, analyze time series, run change detection and perform accuracy assessment and area estimation, among many other functionalities in the platform. Data can be created and analyzed for any place on Earth using SEPAL.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/63a3efa0-08ab-4ad6-9d4a-96af7b6a99ec/download/cambodia_mosaic_2020.png" alt="alt text" title="Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia">

Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia

SEPAL reaches over 5000 users in 180 countries for the creation of custom data products from freely available satellite data. SEPAL was developed as a part of the Open Foris suite, a set of free and open source software platforms and tools that facilitate flexible and efficient data collection, analysis and reporting. SEPAL combines and integrates modern geospatial data infrastructures and supercomputing power available through Google Earth Engine and Amazon Web Services with powerful open-source data processing software, such as R, ORFEO, GDAL, Python and Jupiter Notebooks. Users can easily access the archive of satellite imagery from NASA, the European Space Agency (ESA) as well as high spatial and temporal resolution data from Planet Labs and turn such images into data that can be used for reporting and better decision making.

National Forest Monitoring Systems in many countries have been strengthened by SEPAL, which provides technical government staff with computing resources and cutting edge technology to accurately map and monitor their forests. The platform was originally developed for monitoring forest carbon stock and stock changes for reducing emissions from deforestation and forest degradation (REDD+). The application of the tools on the platform now reach far beyond forest monitoring by providing different stakeholders access to cloud based image processing tools, remote sensing and machine learning for any application. Presently, users work on SEPAL for various applications related to land monitoring, land cover/use, land productivity, ecological zoning, ecosystem restoration monitoring, forest monitoring, near real time alerts for forest disturbances and fire, flood mapping, mapping impact of disasters, peatland rewetting status, and many others.

The Hand-in-Hand initiative enables countries that generate data through SEPAL to disseminate their data widely through the platform and to combine their data with the numerous other datasets available through Hand-in-Hand.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/868e59da-47b9-4736-93a9-f8d83f5731aa/download/probability_classification_over_zambia.png" alt="alt text" title="Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia">

Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia

The digital map market is experiencing robust growth, projected to reach a market size of $9.05 billion in 2025 and expanding at a compound annual growth rate (CAGR) of 26.06%. This significant expansion is driven by several key factors. The increasing adoption of location-based services (LBS) across various sectors, including automotive, logistics, and e-commerce, fuels demand for accurate and comprehensive digital maps. Advancements in technologies like AI and machine learning are enhancing map accuracy, functionality, and personalization, further stimulating market growth. Furthermore, the rising penetration of smartphones and connected devices provides a readily available platform for digital map usage. The integration of digital maps into autonomous vehicle technology is another major driver, promising substantial future growth. Competition is fierce, with established players like Google, TomTom, and HERE Technologies vying for market share alongside emerging innovative companies offering specialized solutions. Market segmentation reveals a strong emphasis on navigation applications, reflecting the pervasive use of digital maps for route planning and guidance. Geocoding services, which convert addresses into geographical coordinates, also constitute a substantial market segment. While North America currently holds a significant market share due to early adoption and technological advancements, the Asia-Pacific region is expected to witness the fastest growth, propelled by rapid urbanization and increasing smartphone penetration in countries like India and China. However, challenges remain, including data privacy concerns, the need for continuous map updates to maintain accuracy, and the high cost of data acquisition and processing. Despite these restraints, the long-term outlook for the digital map market remains positive, with continued technological innovation and expanding applications promising sustained growth throughout the forecast period (2025-2033).

The global GPS navigation software market is experiencing robust growth, driven by the increasing adoption of smartphones, the proliferation of connected cars, and the rising demand for real-time location-based services. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $40 billion by 2033. Key drivers include advancements in mapping technology (e.g., high-definition maps, 3D mapping), the integration of AI and machine learning for improved route optimization and traffic prediction, and the growing need for efficient logistics and fleet management solutions. The market is segmented by software type (e.g., in-dash navigation, mobile apps, web-based), application (e.g., personal navigation, commercial fleet management), and region. Competition is intense, with established players like Garmin, TomTom, and Google competing with emerging tech companies and regional players. Growth is particularly strong in developing economies with expanding middle classes and increasing smartphone penetration. However, challenges remain, including data privacy concerns, the need for continuous map updates, and the potential disruption from autonomous driving technologies. The market's future trajectory depends heavily on the successful integration of new technologies, such as augmented reality navigation and improved user interfaces. Strategic partnerships and mergers & acquisitions are likely to shape the competitive landscape, particularly as companies strive to deliver more personalized and comprehensive location-based services. The increasing reliance on cloud-based services and the adoption of subscription models also represent significant market trends.

This dataset represents a high resolution urban land cover classification map across the southern California Air Basin (SoCAB) with a spatial resolution of 60 cm in urban regions and 10 m in non-urban regions. This map was developed to support NASA JPL-based urban biospheric CO2 modeling in Los Angeles, CA. Land cover classification was derived from a novel fusion of Sentinel-2 (10-60 m x 10-60 m) and 2016 NAIP (60 cm x 60 cm) imagery and provides identification of impervious surface, non-photosynthetic vegetation, shrub, tree, grass, pools and lakes.

Land Cover Classes in .tif file: 0: Impervious surface 1: Tree (mixed evergreen/deciduous) 2: Grass (assumed irrigated) 3: Shrub 4: Non-photosynthetic vegetation 5: Water (masked using MNDWI/NDWI)

Google Earth Engine interactive app displaying this map: https://wcoleman.users.earthengine.app/view/socab-irrigated-classification

A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Support from the Earth Science Division OCO-2 program is acknowledged. Copyright 2020. All rights reserved.

The global road safety app market, currently valued at $239 million in 2025, is projected to experience robust growth, driven by increasing smartphone penetration, rising concerns about road accidents, and the growing adoption of telematics and connected car technologies. The market's Compound Annual Growth Rate (CAGR) of 8.4% from 2025 to 2033 indicates a significant expansion, with the market expected to surpass $400 million by 2030. Key growth drivers include the increasing demand for features like driver behavior monitoring, emergency assistance functionalities (e.g., SOS buttons), and integrated navigation with real-time hazard alerts. Furthermore, government initiatives promoting road safety and the integration of these apps with insurance programs are contributing to market expansion. The market is segmented by application (enterprise and personal) and operating system (iOS and Android), with the personal segment currently dominating due to widespread consumer adoption. Regional growth will be driven by increasing adoption in emerging markets like Asia-Pacific and Middle East & Africa, although North America and Europe will retain substantial market share due to early adoption and mature technology infrastructure. While challenges remain, such as data privacy concerns and the need for consistent app quality and functionality, the overall market outlook is positive, anticipating continued growth fueled by technological advancements and heightened awareness of road safety. The competitive landscape is characterized by a mix of established players like Google Maps and Waze, alongside specialized road safety app developers such as SafetyCulture and Life360. Differentiation is achieved through unique features, partnerships with insurance providers, and targeted marketing strategies. The future of the market will likely witness increased integration with vehicle systems, further enhancing functionalities and promoting proactive road safety measures. The development of AI-powered features such as driver fatigue detection and advanced hazard prediction will also play a significant role in driving market expansion and improving road safety globally. Strategic collaborations between app developers, telematics providers, and automotive manufacturers will further shape the market's trajectory, leading to more comprehensive and effective road safety solutions.

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.
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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

An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Iraq Heritage Program" data publication.

This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8

A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz, G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065. https://doi.org/10.5194/nhess-19-2053-2019 Cohen, S., G. R. Brakenridge, A. Kettner, B. Bates, J. Nelson, R. McDonald, Y. Huang, D. Munasinghe, and J. Zhang (2018), Estimating Floodwater Depths from Flood Inundation Maps and Topography, Journal of the American Water Resources Association, 54 (4), 847–858. https://doi.org/10.1111/1752-1688.12609 Sample products and data availability: https://sdml.ua.edu/models/fwdet/ https://sdml.ua.edu/michigan-flood-may-2020/ https://cartoscience.users.earthengine.app/view/fwdet-gee-mi https://alabama.app.box.com/s/31p8pdh6ngwqnbcgzlhyk2gkbsd2elq0 GEE implementation output: fwdet_gee_brazos.tif ArcMap implementation output (see Cohen et al. 2019): fwdet_v2_brazos.tif iRIC validation layer (see Nelson et al. 2010): iric_brazos_hydraulic_model_validation.tif Brazos River inundation polygon access in GEE: var brazos = ee.FeatureCollection('users/cartoscience/FwDET-GEE-Public/Brazos_River_Inundation_2016') Nelson, J.M., Shimizu, Y., Takebayashi, H. and McDonald, R.R., 2010. The international river interface cooperative: public domain software for river modeling. In 2nd Joint Federal Interagency Conference, Las Vegas, June (Vol. 27). Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # FwDET-GEE calculates floodwater depth from a floodwater extent layer and a DEM Authors: Brad G. Peter, Sagy Cohen, Ronan Lucey, Dinuke Munasinghe, Austin Raney Emails: bpeter@ua.edu, sagy.cohen@ua.edu, ronan.m.lucey@nasa.gov, dsmunasinghe@crimson.ua.edu, aaraney@crimson.ua.edu Organizations: BP, SC, DM, AR - University of Alabama; RL - University of Alabama in Huntsville Last Modified: 10/08/2020 To cite this code use: Peter, Brad; Cohen, Sagy; Lucey, Ronan; Munasinghe, Dinuke; Raney, Austin, 2020, "A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET-GEE)", https://doi.org/10.7910/DVN/JQ4BCN, Harvard Dataverse, V2 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDETv2.0) [1] developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet ------------------------------------------------------------------------------------------------------------------------- How to run this code with your flood extent GEE asset: User of this script will need to update path to flood extent (line 32 or 33) and select from the processing options. Available DEM options (1) are USGS/NED (U.S.) and USGS/SRTMGL1_003 (global). Other options include (2) running the elevation outlier filtering algorithm, (3) adding water body data to the inundation extent, (4) add a water body data layer uploaded by the user rather than using the JRC global surface water data, (5) masking out regular water body data, (6) masking out 0 m depths, (7) choosing whether or not to export, (8) exporting additional data layers, and (9) setting an export file name. The simpleVis option (10) bypasses the time consuming processes and is meant for visualization only; set this option to false to complete the entire process and enable exporting. ------------------------------------------------------------------------------------------------------------------------- ••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• Load flood extent layer | Flood extent layer must be uploaded to GEE first as an asset. If the flood extent is a shapefile, upload as a FeatureCollection; otherwise, if the flood extent layer is a raster, upload it as an image. A raster layer may be required if the flood extent is a highly complex geometry -------------------------------------- */ var flood = ee.FeatureCollection('users/username/folder/flood_extent') // comment out this line if using an Image // var flood = ee.Image('users/username/folder/flood_extent') // comment out this line if using a FeatureCollection var waterExtent = ee.FeatureCollection('users/username/folder/water_extent') // OPTIONAL comment out this line if using an Image // var waterExtent = ee.Image('users/username/folder/water_extent') // OPTIONAL comment out this line if using a FeatureCollection // Processing options - refer to the directions above /*1*/ var demSource = 'USGS/NED' // 'USGS/NED' or 'USGS/SRTMGL1_003' /*2*/ var outlierTest = 'TRUE' // 'TRUE' (default) or 'FALSE' /*3*/ var addWater = 'TRUE' // 'TRUE' (default) or 'FALSE' /*4*/ var userWater = 'FALSE' // 'TRUE' or 'FALSE' (default) /*5*/ var maskWater = 'FALSE' // 'TRUE' or 'FALSE' (default) /*6*/ var maskZero = 'FALSE' // 'TRUE' or 'FALSE' (default) /*7*/ var exportLayer = 'TRUE' // 'TRUE' (default) or 'FALSE' /*8*/ var exportAll = 'FALSE' // 'TRUE' or 'FALSE' (default) /*9*/ var outputName = 'FwDET_GEE' // text string for naming export file /*10*/ var simpleVis = 'FALSE' // 'TRUE' or 'FALSE' (default) // ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT •••••••••••••••••••••••••••••••••••••••••••••••••••• // Create buffer around flood area to use for clipping other layers var area = flood.geometry().bounds().buffer(1000).bounds() // Load DEM and grab projection info var dem = ee.Image(demSource).select('elevation').clip(area) // [2,3] var projection = dem.projection() var resolution = projection.nominalScale().getInfo() // Load global surface water layer var jrc = ee.Image('JRC/GSW1_1/GlobalSurfaceWater').select('occurrence').clip(area) // [4] var water_image = jrc // User uploaded flood extent layer // Identify if a raster or vector layer is being used and proceed with appropriate process if ( flood.name() == 'FeatureCollection' ) { var addProperty = function(feature) { return feature.set('val',0); }; var flood_image = flood.map(addProperty).reduceToImage(['val'],ee.Reducer.first()) .rename('flood') } else { var flood_image = flood.multiply(0) } // Optional user uploaded water extent layer if ( userWater == 'TRUE' ) { // Identify if a raster or vector layer is being used and proceed with appropriate process if ( waterExtent.name() == 'FeatureCollection' ) { var addProperty = function(feature) { return feature.set('val',0); }; var water_image = waterExtent.map(addProperty).reduceToImage(['val'],ee.Reducer.first()) .rename('flood') } else { var water_image = waterExtent.multiply(0) } } // Add water bodies to flood extent if 'TRUE' is selected if ( addWater == 'TRUE' ) { var w = water_image.reproject(projection) var waterFill = flood_image.mask().where(w.gt(0),1) flood_image = waterFill.updateMask(waterFill.eq(1)).multiply(0) } // Change processing options if 'TRUE' is selected if ( simpleVis == 'FALSE' ) { flood_image = flood_image.reproject(projection) } else { outlierTest = 'FALSE' exportLayer = 'FALSE' } // Run the outlier filtering process if 'TRUE' is selected if ( outlierTest == 'TRUE' ) { // Outlier detection and filling on complete DEM using the modified z-score and a median filter [5] var kernel = ee.Kernel.fixed(3,3,[[1,1,1],[1,1,1],[1,1,1]]) var kernel_weighted = ee.Kernel.fixed(3,3,[[1,1,1],[1,0,1],[1,1,1]]) var median = dem.focal_median({kernel:kernel}).reproject(projection) var median_weighted = dem.focal_median({kernel:kernel_weighted}).reproject(projection) var diff = dem.subtract(median) var mzscore = diff.multiply(0.6745).divide(diff.abs().focal_median({kernel:kernel}).reproject(projection)) var fillDEM = dem.where(mzscore.gt(3.5),median_weighted) // Outlier detection and filling on the flood extent border pixels var expand = flood_image.focal_max({kernel: ee.Kernel.square({ radius: projection.nominalScale(), units: 'meters' })}).reproject(projection) var demMask = fillDEM.updateMask(flood_image.mask().eq(0)) var boundary = demMask.add(expand) var medianBoundary = boundary.focal_median({kernel:kernel}).reproject(projection) var medianWeightedBoundary = boundary.focal_median({kernel:kernel_weighted}).reproject(projection) var diffBoundary = boundary.subtract(medianBoundary) var mzscoreBoundary = diffBoundary.multiply(0.6745).divide(diffBoundary.abs().focal_median({kernel:kernel}).reproject(projection)) var fill =

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/.

The road safety app market, currently valued at $239 million in 2025, is projected to experience robust growth, driven by increasing smartphone penetration, rising concerns about road accidents, and the growing adoption of connected car technologies. The 8.4% CAGR indicates a significant expansion over the forecast period (2025-2033). Key drivers include the increasing demand for features like real-time accident reporting, driver behavior monitoring (through features such as speed detection and distraction alerts), and integration with emergency services. Furthermore, the market is witnessing a surge in the adoption of advanced features such as AI-powered driver assistance, proactive safety warnings based on location data, and community-based reporting of hazardous road conditions. The competitive landscape is characterized by a mix of established players like Google Maps and emerging startups focusing on niche functionalities. This competitive environment is fostering innovation and driving the development of more sophisticated and user-friendly apps. Market restraints include data privacy concerns, the potential for app fatigue due to the proliferation of road safety apps, and the need for continuous improvements in user interface and functionality to maintain high adoption rates. The market is segmented by various features (e.g., GPS navigation, driver monitoring, emergency assistance), operating systems, and geographic regions. Future growth will likely be influenced by technological advancements, regulatory changes related to road safety, and public awareness campaigns promoting the usage of such applications. The integration of these apps with autonomous vehicle technology is also a significant emerging trend that will likely significantly impact market growth in the later years of the forecast period. The rising demand for enhanced safety features in developing economies will also contribute to market expansion.

The Geospatial Analytics Market size was valued at USD 98.93 billion in 2023 and is projected to reach USD 227.04 billion by 2032, exhibiting a CAGR of 12.6 % during the forecasts period. The Geospatial Analytics Market describes an application of technologies and approaches processing geographic and spatial data for intelligence and decision-making purposes. This market comprises of mapping tools and software, spatial data and geographic information systems (GIS) used in various fields including urban planning, environmental, transport and defence. Use varies from inventory tracking and control to route optimization and assessment of changes in environment. Other trends are the growth of big data and machine learning to improve the predictive methods, the improved real-time data processing the use of geographic data in combination with other technologies, for example, IoT and cloud. Some of the factors that are fuelling the need to find a marketplace for GIS solutions include; Increasing importance of place-specific information Increasing possibilities for data collection The need to properly manage spatial information in a high stand environment. Recent developments include: In May 2023, Google launched Google Geospatial Creator, a powerful tool that allows users to create immersive AR experiences that are both accurate and visually stunning. It is powered by Photorealistic 3D Tiles and ARCore from Google Maps Platform and can be used with Unity or Adobe Aero. Geospatial Creator provides a 3D view of the world, allowing users to place their digital content in the real world, similar to Google Earth and Google Street View. , In April 2023, Hexagon AB launched the HxGN AgrOn Control Room. It is a mobile app that allows managers and directors of agricultural companies to monitor all field operations in real time. It helps managers identify and address problems quickly, saving time and money. Additionally, the app can help to improve safety by providing managers with a way to monitor the location and status of field workers. , In December 2022, ESRI India announced the availability of Indo ArcGIS offerings on Indian public clouds and services to provide better management, collecting, forecasting, and analyzing location-based data. , In May 2022, Trimble announced the launch of the Trimble R12i GNSS receiver, which has a powerful tilt adjustment feature. It enables land surveyors to concentrate on the task and finish it more quickly and precisely. , In May 2021, Foursquare purchased Unfolded, a US-based provider of location-based services. This US-based firm provides location-based services and goods, including data enrichment analytics and geographic data visualization. With this acquisition, Foursquare aims to provide its users access to various first and third-party data sets and integrate them with the geographical characteristics. , In January 2021, ESRI, a U.S.-based geospatial image analytics solutions provider, introduced the ArcGIS platform. ArcGIS Platform by ESRI operates on a cloud consumption paradigm. App developers generally use this technology to figure out how to include location capabilities in their apps, business operations, and goods. It aids in making geospatial technologies accessible. .