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.

This statistic shows the results of a survey on the usage of the internet for route planning, maps and road maps (e.g. Google Maps) in Germany from 2013 to 2016. In 2016, there were about ***** million people among the German-speaking population aged 14 years and older, who frequently used the internet to plan routes or to access maps and road maps.

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.

It starts with a simple habit: you open your browser and type a question. A few keystrokes later, Google gives you answers, videos, maps, and suggestions before you even finish your thought. For billions of people around the world, this daily interaction is second nature. But behind that blinking cursor...

Google My Business (GMB) is a platform designed to help you share detailed information about your business when it appears in search results. In addition to a URL and description, you can include photos, videos, contact numbers, operating hours, delivery zones, and links to booking services. Google My Business enables you to create eye-catching listings that enhance visibility when customers search online. It allows your in-store products to be displayed directly on your Google Business Profile. A cover photo, along with previews from Google Maps and Google Street View, gives potential customers a clear idea of what to expect when they visit. However, keep in mind that users can suggest changes to your profile, so it’s important to review it frequently to ensure accuracy.

Google My Business also highlights key factors to consider for verifying your business presence and enhancing your local search visibility through optimization.

Data Dictionary

Notice on Dataset Usage and Attribution

This dataset, created by Agung Pambudi, is entirely original and has not been shared previously. It is distributed under the CC BY 4.0 license, which permits unrestricted use, provided the author is appropriately credited. A DOI is included to ensure accurate citation. Please be aware that duplicating this work on Kaggle is prohibited.

The Navigation and Mapping Solutions market is experiencing robust growth, driven by the increasing adoption of location-based services (LBS) across various sectors. The market's expansion is fueled by several key factors, including the proliferation of smartphones equipped with advanced GPS technology, the rising demand for real-time traffic updates and navigation assistance, and the increasing integration of mapping solutions into automotive systems. Furthermore, the development of sophisticated mapping technologies, such as 3D mapping and augmented reality (AR) overlays, is enhancing user experience and driving market penetration. The expanding use of these solutions in logistics and transportation, coupled with the growth of e-commerce and the demand for efficient delivery services, contributes significantly to the market's upward trajectory. We estimate the market size in 2025 to be around $15 billion, projecting a Compound Annual Growth Rate (CAGR) of 12% through 2033. Despite the promising outlook, market growth faces certain challenges. High initial investment costs associated with developing and maintaining advanced mapping systems may limit entry for smaller players. Data privacy concerns and regulatory restrictions regarding data collection and usage pose significant hurdles. The accuracy and reliability of mapping data remain critical factors affecting market adoption, particularly in remote or rapidly changing areas. Competition among established players like Google, TomTom, and Garmin is intense, demanding continuous innovation and strategic partnerships to maintain a competitive edge. Despite these restraints, the long-term prospects for the navigation and mapping solutions market remain positive, driven by ongoing technological advancements and expanding applications across diverse industries.

Dataset Abstract:Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments. The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.

USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.

Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.

Dataset: 5 bandsb1: Number of tall monthsb2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcaneb3: ESA crop mask: 0 = non-cropland, 1 = croplandb4: ESRI crop mask: 0 = non-cropland, 1 = croplandb5: GLAD crop mask: 0 = non-cropland, 1 = cropland

The dataset can be accessed on Google Earth Engine (GEE) at https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLADExample GEE script for visualizing and masking the sugarcane maps by country available at:https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD

explain the relationship between DMA, FIPS and county

ChatGPT said: Here's a breakdown of the relationship between DMA, FIPS, and county:

🗺️ 1. County A county is a geographic and administrative subdivision of a U.S. state.

For example: Cook County in Illinois or Los Angeles County in California.

The U.S. has over 3,000 counties or county-equivalents.

🔢 2. FIPS Code FIPS = Federal Information Processing Standards code.

Used to uniquely identify geographic areas like states and counties.

It consists of:

State FIPS (2 digits) — e.g., California = 06

County FIPS (3 digits) — e.g., Los Angeles County = 037

Together: 06037 (California + Los Angeles County)

FIPS codes are used in datasets, GIS, and federal data standards to track data consistently across agencies.

📺 3. DMA (Designated Market Area) DMA is a geographic region defined by Nielsen where the population receives the same (or similar) TV and radio content.

Used in advertising, media, and Google Trends to segment the market by media consumption patterns.

There are 210 DMAs in the U.S., each labeled by Nielsen with a DMA code or name (e.g., "New York", "Chicago", "Palm Springs").

📌 How They Relate Concept Level Code Type Example County Local FIPS Los Angeles County (06037) FIPS Code System Numeric Encodes state + county DMA Media Region Name/Index Los Angeles DMA

One DMA contains multiple counties.

Example: The Los Angeles DMA includes Los Angeles County, Orange County, Ventura County, etc.

Each county has a unique FIPS code, but a DMA might span counties in multiple states.

When mapping or analyzing data (e.g., in GIS or Google Trends), you often merge county-level data (FIPS) into DMAs for media or market analysis.

🧭 Example State County FIPS DMA Name DMA Code California Los Angeles 06037 Los Angeles 803 California Orange 06059 Los Angeles 803 California Riverside 06065 Palm Springs 804

👉 DMA for FIPS 02013?

Alaska is unique: it’s sparsely populated and has limited Nielsen DMA coverage.

Most of rural Alaska does not belong to a defined DMA. Instead, large parts of Alaska are treated as “non-DMA” or are grouped into a single large DMA (often labeled Anchorage DMA if included).

Maps are crucial for conveying the character and atmosphere of places. While maps that evoke a sense of place are increasingly recognized as important, existing methods often rely on subjective design choices, which can be inefficient and lack scalability. This paper proposes a method for generating place-aware colored maps using crowdsourced images to realize mapping with a sense of place. Feature colors of place extracted from these images are used to create colored maps that balance legibility, harmony, and imageability. To evaluate the method’s effectiveness, we conducted two comparative experiments: one comparing place-aware colored maps with Google Maps, and another with an aesthetically rich baseline—the “Hopper” style from Snazzy Maps. Evaluation was based on participants’ performance in map-reading tasks and their perceived sense of place. Results show that our colored maps perform similarly to Google Maps in map-reading tasks but significantly outperform both baselines in evoking a stronger sense of place. This indicates that place-aware colored maps effectively evoke a sense of place while maintaining basic map functionality. This research introduces a framework for mapping with a sense of place, offering a new approach to color generation that enhances user engagement through intuitive connections to place characteristics.

We assessed park visitation at our selection of candidate parks using publicly available, geotagged social media data from Instagram. To gather data on park usage, we wrote a Python script to collect data from publicly accessible Instagram photos. We first matched candidate parks to their corresponding Instagram location tags, of which there were often several for a park, and visually verified that all location tags corresponded to the correct parks using Google maps. After we had created this list of accurate location tags for the parks that we identified in the NJ OSPRI dataset, we ran our Python script to collect the metadata of all photos associated with each tag; we collected the user handle, date, location tag, photo caption, and hashtags for each photo.

Motivation

The data in this dataset is a spatial inventory of urban agriculture (UA) carried out in the city of Rome (Italy) (Grande Raccordo Anulare (GRA)). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.

License

Creative Commons CC-BY

Disclaimer

Despite our best efforts to validate the data, some information may be incorrect.

Description of the dataset

Typologies of UA

• Residential garden: Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.
• Community garden: A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.
• Urban farm: Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus exclusively on horticulture, vineyard, olive groves and orchard.
• Institutional garden: Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).
• Illegal garden: Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.

Land use typologies

• Horticulture: annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).
• Vineyard: grape vines grown in order to produce wine or table grape.
• Olive groves: olive trees grown in order to produce olive oil or table olives.
• Orchards: mixed trees such as orange, stone fruit, pome fruit, olive trees.
• Mixed crops: an area grown with a mix of horticulture crops and fruit trees, not divisible.

Credit

Pulighe G., Lupia F. (2016) Mapping spatial patterns of urban agriculture in Rome (Italy) using Google Earth and web-mapping services. Land Use Policy 59(2016) 49-58.

www.sciencedirect.com/science/article/pii/S0264837716300059

Dataset Abstract:

Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL) has played an important role in improving production forecasts and enabling large-scale study of agricultural inputs and outcomes. Although CDL offers crop type maps across the conterminous US from 2008 onward, such maps are missing in many Midwestern states or are uneven in quality before 2008. To fill these data gaps, we used the now-public Landsat archive and cloud computing services to map corn and soybean at 30m resolution across the US Midwest from 1999-2018. Our training data were CDL from 2008-2018, and we validated the predictions on CDL 1999-2007 where available, county-level crop acreage statistics, and state-level crop rotation statistics. The corn-soybean maps, which we call the Corn-Soy Data Layer (CSDL), are publicly hosted on Google Earth Engine and also available for download on Zenodo.

Summary of Methods:

Using Google Earth Engine, we trained a random forest classifier to classify each pixel of the study area into corn, soybean, and an aggregated "other crops" class. CDL 2008-2018 data were used as labels. The features input to the model were harmonic regression coefficients fit to the NIR, SWIR1, SWIR2, and GCVI bands/indices of time series from Landsat 5, 7, and 8 Surface Reflectance observations. Cloudy pixels were masked out using the pixel_qa band provided with Landsat Surface Reflectance products.

Map Legend:

0 = outside study area

1 = corn

5 = soy

9 = other crop

255 = non-crop (masked by NLCD)

Values were chosen to be consistent with CDL values when possible.

Usage Notes:

We recommend that users consider metrics such as (1) user's and producer's accuracy with CDL and (2) R2 with NASS statistics across space and time to determine in which states/counties and years CSDL is of high quality. This can be done with the CSV file of user's and producer's accuracies included in this Zenodo, and annual county-level statistics and example code we have included in our repo at https://github.com/LobellLab/csdl.

Updates:

March 1, 2021: Fixed an issue where 255 (non-crop) values were represented as NAs instead. CSDL now contains the 255 values representing non-crop.

October 20, 2020: Fixed projection issues in the previous version. The CSDL projection now matches that of CDL.

July 13, 2020: We revised how we used NLCD to mask out non-crop pixels from our maps. Instead of using one cropland mask (the union of cropland across all NLCD maps) for all years of CSDL, we used a different cropland mask (the last available NLCD) for each year of CSDL. We also reprojected the CSDL maps to the same projection as CDL to make it easier for users to transition between or combine the two datasets.

This is a mutli-modal dataset for restaurants from Google Local (Google Maps). Data includes images and reviews posted by users, as well as metadata for each restaurant.

Bali Tourist Attractions Dataset from Google Maps

Dataset Description

This dataset contains information about tourist attractions in Bali collected through automated scraping from Google Maps. It covers 761 tourist spots spread across 9 regencies/cities in Bali Island. The dataset aims to provide a comprehensive overview of the locations, categories, and popularity of Bali’s tourist destinations.

Data Source

• Data collected via automated scraping from Google Maps.
• Rating and links are obtained directly from each attraction’s Google Maps page.
• Dataset includes various popular tourist categories in Bali, ranging from natural parks, beaches, cultural sites, to general tourist attractions.

Column Descriptors

Dataset Purpose

• Support research and analysis related to Bali tourism.
• Facilitate development of map-based and recommendation tourism applications using real data.
• Assist in mapping and promoting tourist destinations more effectively.
• Provide comprehensive data for local governments and tourism industry stakeholders.

Sample Data

How to Use the Dataset

• The dataset can be imported and used in various data analysis tools such as Python (pandas), R, or GIS software.
• The latitude and longitude columns can be used to visualize tourist spots on a map.
• The rating column can be used for popularity and quality analysis of tourist destinations.
• The kategori and preferensi columns can assist in segmenting tourism types.

License

This dataset is provided for research and application development purposes. Use of this dataset must comply with Google Maps’ data usage policies and respect intellectual property rights.

Contact

For questions or further discussion regarding this dataset, please contact:

• Name: Bertnardo Mario Uskono
• Email: bertnardouskono@gmail.com

DataForSEO will land you with accurate data for a SERP monitoring solution. In particular, our SERP API provides data from:

• Google Organic search, Maps, News, and Images tabs in vertical search
• Bing Organic and Local Pack search
• Yahoo, Yandex, Baidu, and Naver search

For each of the search engines, we support all possible locations. You can set any keyword, location, and language, as well as define additional parameters, e.g. time frame, category, number of results.

You can set the device and the OS that you want to obtain SERP results for. We support Android/iOS for mobile and Windows/macOS for desktop.

We can supply you with all organic, paid, and extra Google SERP elements, including featured snippet, answer box, knowledge graph, local pack, map, people also ask, people also search, and more.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

Rarely discussed about the Israel–Palestinian conflict is the antagonism that exists between the Palestinian leadership and the refugees. With the advent of the Oslo “peace process” in the 1990s, the antagonism began to escalate, for the process's key assumption became that the leadership would relinquish the refugees' right to return home so that Israel would be preserved as a majority Jewish state in exchange for the Palestinian leadership's sovereignty over the West Bank and Gaza Strip. Because the refugees’ return home would upset the demographic balance of a Jewish-majority state, they have become impossible figures for both Israel and for the Palestinian leadership's political frame, an “impossibility” that is taken for granted in dominant maps of Palestine/Israel. This article highlights some ways the refugees have refused this erasure by mapping onto the land their historical presence. Taking their use of Google Earth as a case study, it begins by providing background on Google Earth, situating the software's prehistory within Cold War battles for surveillance and control. It then points to some “cracks” Google Earth's introduction has presented the post–Cold War political scene with: namely, that nation-states are today stumbling to control with whom maps are shared, who can make them, and what they will look like. It then moves on to show how the refugees have taken advantage of the State of Israel's (as well as the Palestinian leadership's) inability to control the map, in the process rendering the geoweb a new battlefield in the conflict. I conclude with an analysis of how cartographically placing Israel's founding and perpetual violence at the fore, as the Palestinian refugees' counter-cartography does, can help to move forward the refugees' demands for justice. Key Words: counter-cartography, geoweb, Google, Palestine, qualitative GIS, social movements.

Crowdsourced Speed Limit Data Market Outlook

According to our latest research, the global Crowdsourced Speed Limit Data market size stands at USD 1.32 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.8% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 6.51 billion. This impressive growth is primarily driven by the increasing adoption of connected vehicles, advancements in real-time navigation systems, and the rising demand for accurate road and traffic data across various sectors.

One of the primary growth factors fueling the expansion of the Crowdsourced Speed Limit Data market is the proliferation of mobile devices and navigation applications. The widespread usage of smartphones equipped with GPS and location-based services has made it easier than ever to collect and share speed limit data in real time. This democratization of data collection not only enhances the accuracy of mapping platforms but also supports a dynamic ecosystem where users contribute to and benefit from up-to-date road information. Furthermore, the integration of crowdsourced data into popular navigation apps such as Google Maps and Waze has set new standards for user expectations, pushing other industry players to adopt similar approaches and fueling further market growth.

Another significant driver is the rapid development of autonomous and connected vehicles. For autonomous vehicles to operate safely and efficiently, they require access to highly accurate and current speed limit information. Crowdsourced speed limit data, constantly updated by millions of users and vehicles, offers a scalable solution that traditional mapping methods cannot match. Automotive OEMs are increasingly integrating this data into their advanced driver-assistance systems (ADAS) and infotainment platforms, enhancing both safety and user experience. The synergy between automotive innovation and crowdsourced data is expected to remain a key catalyst for market expansion through the forecast period.

In addition, the growing emphasis on traffic management and road safety initiatives by government agencies worldwide is propelling the Crowdsourced Speed Limit Data market. Authorities are leveraging crowdsourced data to enhance their traffic monitoring capabilities, optimize traffic flow, and reduce accident rates. The ability to gather granular, real-time speed limit information from a diverse pool of contributors enables more responsive and data-driven policy decisions. As governments increasingly collaborate with technology providers and automotive OEMs, the adoption of crowdsourced speed limit data is anticipated to accelerate, further strengthening the market’s growth trajectory.

From a regional perspective, North America currently leads the market, closely followed by Europe and the Asia Pacific. The presence of major technology companies, high smartphone penetration, and advanced transportation infrastructure have positioned North America at the forefront of this market. Meanwhile, Europe’s strict regulatory environment and focus on road safety have driven significant adoption across the continent. The Asia Pacific region is emerging as a high-growth market due to rapid urbanization, increasing vehicle ownership, and government investments in smart transportation systems. As these regions continue to innovate and expand their digital ecosystems, their contributions to the global crowdsourced speed limit data market will become even more pronounced.

Data Source Analysis

The Data Source segment is a cornerstone of the Crowdsourced Speed Limit Data market, encompassing mobile applications, navigation devices, automotive OEMs, government platforms, and other sources. Mobile applications represent the largest and fastest-growing sub-segment, thanks to the ubiquity of smartphones and the widespread adoption of GPS-enabled apps. These applications allow users to report and validate speed limits, feeding real-time information into

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas 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.2TreesAny significant clustering of tall (~15 feet 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).4Flooded 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.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built 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.8Bare 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.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen 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.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five 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 L2A 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 for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. 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.

The global road safety app market, 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. A compound annual growth rate (CAGR) of 8.4% from 2025 to 2033 indicates a significant expansion of this market. Key drivers include the increasing demand for features like speed monitoring, driver behavior analysis, and emergency assistance functionalities. The integration of these apps with connected car systems further enhances their appeal and utility, leading to wider adoption across various demographics. The market is segmented by application (enterprise and personal use) and operating system (iOS and Android), reflecting the diverse needs and preferences of users. While the enterprise segment may see growth from fleet management solutions, the personal segment is expected to dominate due to increasing individual awareness of road safety and the desire for enhanced protection during commutes and travels. Geographic distribution reveals a strong presence in North America and Europe, largely attributable to higher technological adoption and established safety regulations. However, emerging markets in Asia-Pacific and Middle East & Africa are expected to show significant growth as infrastructure improves and smartphone usage increases. Competitive rivalry is intense, with established players like Google Maps and Waze competing against specialized road safety apps offering unique functionalities. This competitive landscape is pushing innovation and improvement in app features and user experience. The market's restraints primarily involve data privacy concerns and the need for robust regulatory frameworks to ensure accurate and reliable data collection and use. Furthermore, challenges related to app integration with different car models and operating systems continue to hinder widespread adoption. To overcome these challenges, app developers are focusing on improved user interfaces, enhanced data security features, and stronger partnerships with automotive manufacturers and insurance providers. The future trajectory suggests a considerable expansion of the road safety app market, with increasing focus on preventative measures, advanced driver-assistance systems integration, and sophisticated analytics to understand and mitigate road accident risks. This is fueled by a growing awareness of road safety as a crucial public health concern. The market will likely see further segmentation based on user-specific needs and preferences, such as tailored safety apps for specific demographics (e.g., elderly drivers, teenagers).

Google is not only popular in its home country, but is also the dominant internet search provider in many major online markets, frequently generating between ** and ** percent of desktop search traffic. The search engine giant has a market share of over ** percent in India and accounted for the majority of the global search engine market, way ahead of other competitors such as Yahoo, Bing, Yandex, and Baidu. Google’s online dominance All roads lead to Rome, or if you are browsing the internet, all roads lead to Google. It is hard to imagine an online experience without the online behemoth, as the company offers a wide range of online products and services that all seamlessly integrate with each other. Google search and advertising are the core products of the company, accounting for the vast majority of the company revenues. When adding this up with the Chrome browser, Gmail, Google Maps, YouTube, Google’s ownership of the Android mobile operating system, and various other consumer and enterprise services, Google is basically a one-stop shop for online needs. Google anti-trust rulings However, Google’s dominance of the search market is not always welcome and is keenly watched by authorities and industry watchdogs – since 2017, the EU commission has fined Google over ***** billion euros in antitrust fines for abusing its monopoly in online advertising. In March 2019, European Commission found that Google violated antitrust regulations by imposing contractual restrictions on third-party websites in order to make them less competitive and fined the company *** billion euros.