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.

MODIS product version comparison application for Google Earth Engine This is associated an article published by IEEE in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing on 20 March 2019, available online at doi.org/10.1109/JSTARS.2019.2901404. Reference: Peter, B.G. and Messina, J.P., 2019. Errors in Time-Series Remote Sensing and an Open Access Application for Detecting and Visualizing Spatial Data Outliers Using Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(4), pp.1165-1174. Link to manuscript https://ieeexplore.ieee.org/abstract/document/8672086 Interactive Google Earth Engine Application https://cartoscience.users.earthengine.app/view/versions Google Earth Engine Code // Version 1.1 Map.setCenter(30, 20, 2.5).setOptions('HYBRID').style().set('cursor', 'crosshair'); var countryList = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017'); var stats = function(year) { Map.layers().reset(); var countrySelected = app.country.countrySelect.getValue(); var region = countryList.filterMetadata('Country', 'equals', countrySelected).geometry(); var versionOne = app.inputBox.productBox.getValue(); var versionTwo = app.inputBox.productBoxTwo.getValue(); var band = app.inputBox.bandBox.getValue(); var bandTwo = app.inputBox.bandBoxTwo.getValue(); if (app.inputBox.customCheckbox.getValue() === true) { var latCoord = ee.Number.parse(app.inputBox.latCoordBox.getValue()).getInfo(); var lonCoord = ee.Number.parse(app.inputBox.lonCoordBox.getValue()).getInfo(); var distBuffer = ee.Number.parse(app.inputBox.distBox.getValue()).getInfo(); var distNum = distBuffer*1000; region = ee.Geometry.Point([lonCoord,latCoord]).buffer(distNum).bounds(); } var modisCollectionOne = ee.ImageCollection(versionOne).select(band); var modisCollectionTwo = ee.ImageCollection(versionTwo).select(bandTwo); var imageOne = modisCollectionOne.filter(ee.Filter.calendarRange(year,year,'year')).mean(); var imageTwo = modisCollectionTwo.filter(ee.Filter.calendarRange(year,year,'year')).mean(); var abs = imageOne.select(band).subtract(imageTwo.select(bandTwo)).abs().rename("difference"); var percentilesOne = imageOne.reduceRegion({ reducer: ee.Reducer.percentile([10,90]), geometry: region, scale: 250, maxPixels: 1e13 }); var percentilesTwo = imageTwo.reduceRegion({ reducer: ee.Reducer.percentile([10,90]), geometry: region, scale: 250, maxPixels: 1e13 }); var percentilesAbs = abs.reduceRegion({ reducer: ee.Reducer.percentile([10,90]), geometry: region, scale: 250, maxPixels: 1e13 }); var minOne = ee.Number(percentilesOne.get(band+'_p10')).getInfo(); var maxOne = ee.Number(percentilesOne.get(band+'_p90')).getInfo(); var minTwo = ee.Number(percentilesTwo.get(bandTwo+'_p10')).getInfo(); var maxTwo = ee.Number(percentilesTwo.get(bandTwo+'_p90')).getInfo(); var minBoth = Math.min(minOne,minTwo); var maxBoth = Math.max(maxOne,maxTwo); var minAbs = ee.Number(percentilesAbs.get('difference_p10')).getInfo(); var maxAbs = ee.Number(percentilesAbs.get('difference_p90')).getInfo(); var grayscale = ['f7f7f7', 'cccccc', '969696', '525252','141414']; Map.addLayer(imageOne.select(band).rename(band+'_'+versionOne).clip(region),{min: minBoth, max: maxBoth, palette: grayscale},band+' • '+versionOne, false); Map.addLayer(imageTwo.select(bandTwo).rename(bandTwo+'_'+versionTwo).clip(region),{min: minBoth, max: maxBoth, palette: grayscale},band+' • '+versionTwo, false); Map.addLayer(abs.clip(region),{min: minAbs, max: maxAbs, palette: grayscale},"Difference"); var options = { title: year+' Histogram', fontSize: 11, legend: {position: 'none'}, series: {0: {color: '7100AA'}} }; var histogram = ui.Chart.image.histogram(imageOne, region, 10000).setOptions(options); var optionsTwo = { title: year+' Histogram', fontSize: 11, legend: {position: 'none'}, series: {0: {color: '0071AA'}} }; var histogramTwo = ui.Chart.image.histogram(imageTwo, region, 10000).setOptions(optionsTwo); var clickLabel = ui.Label('Click map to get pixel time-series', {fontWeight: '300', fontSize: '13px', margin: '10px 10px 15px 30px'}); var clickLabelTwo = ui.Label('Click map to get pixel time-series', {fontWeight: '300', fontSize: '13px', margin: '10px 10px 15px 30px'}); app.rootPanels.panelOne.widgets().set(1, ui.Label('temp')); app.rootPanels.panelTwo.widgets().set(1, ui.Label('temp')); app.rootPanels.panelOne.widgets().set(1, histogram); app.rootPanels.panelOne.widgets().set(2, clickLabel); app.rootPanels.panelTwo.widgets().set(1, histogramTwo); app.rootPanels.panelTwo.widgets().set(2, clickLabelTwo); Map.centerObject(region); Map.setOptions('HYBRID'); Map.onClick(function(coords) { var point = ee.Geometry.Point(coords.lon, coords.lat); var dot = ui.Map.Layer(point, {color: 'AA0000'}, "Inspector"); Map.layers().set(3, dot); var clickChart = ui.Chart.image.series(modisCollectionOne, point, ee.Reducer.mean(), 10000); clickChart.setOptions({ title: 'Pixel | X: ' + coords.lon.toFixed(2)+', '+'Y: ' + coords.lat.toFixed(2),...

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

Adults in the United States were surveyed about the travel apps and websites that they viewed most positively as of the first quarter of 2021. Google Maps ranked first on the list with a popularity score amounting to 79 percent. Meanwhile, Google Earth and Expedia ranked second and third, respectively, in terms of popularity.

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/.

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 Regional Geological Section has prepared a geotourism route accompanied by general and geological information with photos and brief descriptions, following the route of the former Spoleto-Norcia railway. Two regional geologists have identified the different geothematic characteristics along the entire route using some Apps to create a product in Google Earth environment and, in particular, different themes have been treated, not only of a geological or geomorphological nature but also landscape, vegetation and history relatively, in the latter case, to the evidence still present of the railway route. The route, with a total length of 33 kilometers, has been highlighted with colors depending on the type of practicability:  green - pedestrian and cycling.  yellow - driveway  orange - currently with restricted access  red - road link with the state road purple - not passable The geo-tourism itinerary, through a broad-spectrum reading, highlights how landscape and geology influence human activities and how it is necessary to approach the reality in which we live with curiosity, respect and awareness. The file in KMZ and KML format for Google Earth with the geo-tourist route is available for download from the documents section of the URL.

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. Please see the associated publications: 1. Peter, B.G., Cohen, S., Lucey, R., Munasinghe, D., Raney, A. and Brakenridge, G.R., 2020. Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for rapid and large scale flood analysis. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5. https://ieeexplore.ieee.org/abstract/document/9242297 2. Cohen, S., Peter, B.G., Haag, A., Munasinghe, D., Moragoda, N., Narayanan, A. and May, S., 2022. Sensitivity of remote sensing floodwater depth calculation to boundary filtering and digital elevation model selections. Remote Sensing, 14(21), p.5313. https://github.com/csdms-contrib/fwdet 3. 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 4. 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....

As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

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).

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.

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

Learn how to use the interactive map here, https://drive.google.com/file/d/1EE0oZBXTSrk1ekvasgdzibnpL-8IUvup/view.Read more about the Growing Together Project at https://engage.dpsnc.net in English (https://engage.dpsnc.net/growing-together-regions-programs-and-boundaries) and Spanish (https://engage.dpsnc.net/growing-together-regions-programs-and-boundaries-espanol).

In 2023, Google Maps was the most popular free navigation mobile app by downloads in Poland, amounting to nearly two million on iPhone and iPad App Store, and Google Play. Komoot followed with approximately 610 thousand downloads.

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.

Road Trip Planner App Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period from 2024 to 2031.

Global Road Trip Planner App Market Drivers

The Road Trip Planner App market is influenced by several key market drivers, including:

Increasing Travel Enthusiasm: Rising interest in road trips and outdoor adventures is driving demand for planning tools. Many people are seeking personalized and unique travel experiences.
Mobile Device Penetration: The widespread use of smartphones and tablets facilitates the development and adoption of mobile apps for road trip planning, making it easier for users to access road trip tools on-the-go.

Global Road Trip Planner App Market Restraints

The market for road trip planner apps can be influenced by several restraints that may hinder growth or the adoption of such applications. Here are some key market restraints:

Intense Competition: The market is saturated with numerous travel and navigation apps, including popular mapping services like Google Maps and other specialized travel planners. This can make it difficult for new entrants to gain market traction.
User Dependency on Other Services: Many users already rely on established apps that provide comprehensive services, including travel planning, navigation, and real-time traffic updates. The convenience of all-in-one apps can reduce the perceived need for specialized road trip planners.

Digital Maps Market Size And Forecast

Digital Maps Market size was valued at USD 25.95 Billion in 2024 and is projected to reach USD 100.9 Billion by 2031, growing at a CAGR of 18.50% from 2024 to 2031.

Global Digital Maps Market Drivers

Increasing smartphone penetration: The growing number of smartphone users and the widespread availability of internet connectivity have made digital maps easily accessible.
Advancements in mapping technology: The development of more accurate and detailed digital maps, incorporating real-time traffic updates and navigation features, has increased their appeal to users.
Growth of the ride-sharing and delivery services industry: These industries rely heavily on accurate and up-to-date digital maps for navigation and route optimization.

Global Digital Maps Market Restraints

Data privacy concerns: The collection and use of location data raise privacy concerns, which can hinder the adoption of digital maps.
Map inaccuracies: Despite advancements in mapping technology, inaccuracies and errors can still occur, leading to user dissatisfaction.
Competition from free mapping services: The availability of free mapping services from tech giants like Google and Apple can limit the market for premium digital mapping solutions.

A Google Earth Engine App developed to delineate water bodies around the globe from 1984 until present and to provide 16 day estimates of surface area of water bodies as well as shapefiles to the user. The app uses a novel framework to filters only those images that cloud is on top of the water body and allows users to choose from a list of spectral water indices to map water bodies. The app also allows users to select the choice of threshold (i.e., a fixed zero threshold or dynamic threshold to separate water form non-water background).

In May 2024, Google Sites were ranked first among the most popular multiplatform web properties in the United States with over 278 million visitors from mobile and desktop connections – popular Google online properties include not only Google Search but also online video platform YouTube, communication services such as Gmail and Hangouts, as well as assorted online services such as Apps and Maps as well as digital app distribution platform Google Play.

Alphabet Inc. provides various products and platforms in the United States, Europe, the Middle East, Africa, the Asia-Pacific, Canada, and Latin America. It operates through Google Services, Google Cloud, and Other Bets segments. The Google Services segment offers products and services, including ads, Android, Chrome, hardware, Gmail, Google Drive, Google Maps, Google Photos, Google Play, Search, and YouTube. It is also involved in the sale of apps and in-app purchases and digital content in the Google Play store; and Fitbit wearable devices, Google Nest home products, Pixel phones, and other devices, as well as in the provision of YouTube non-advertising services. The Google Cloud segment offers infrastructure, platform, and other services; Google Workspace that include cloud-based collaboration tools for enterprises, such as Gmail, Docs, Drive, Calendar, and Meet; and other services for enterprise customers. The Other Bets segment sells health technology and internet services. The company was founded in 1998 and is headquartered in Mountain View, California.