Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

GEE-TED: A tsetse ecological distribution model for Google Earth Engine Please refer to the associated publication: Fox, L., Peter, B.G., Frake, A.N. and Messina, J.P., 2023. A Bayesian maximum entropy model for predicting tsetse ecological distributions. International Journal of Health Geographics, 22(1), p.31. https://link.springer.com/article/10.1186/s12942-023-00349-0 Description GEE-TED is a Google Earth Engine (GEE; Gorelick et al. 2017) adaptation of a tsetse ecological distribution (TED) model developed by DeVisser et al. (2010), which was designed for use in ESRI's ArcGIS. TED uses time-series climate and land-use/land-cover (LULC) data to predict the probability of tsetse presence across space based on species habitat preferences (in this case Glossina Morsitans). Model parameterization includes (1) day and night temperatures (MODIS Land Surface Temperature; MOD11A2), (2) available moisture/humidity using a vegetation index as a proxry (MODIS NDVI; MOD13Q1), (3) LULC (MODIS Land Cover Type 1; MCD12Q1), (4) year selections, and (5) fly movement rate (meters/16-days). TED has also been used as a basis for the development of an agent-based model by Lin et al. (2015) and in a cost-benefit analysis of tsetse control in Tanzania by Yang et al. (2017). Parameterization in Fox et al. (2023): Suitable LULC types and climate thresholds used here are specific to Glossina Morsitans in Kenya and are based on the parameterization selections in DeVisser et al. (2010) and DeVisser and Messina (2009). Suitable temperatures range from 17–40°C during the day and 10–40°C at night and available moisture is characterized as NDVI > 0.39. Suitable LULC comprises predominantly woody vegetation; a complete list of suitable categories is available in DeVisser and Messina (2009). In the Fox et al. (Forthcoming) publication, two versions of MCD12Q1 were used to assess suitable LULC types: Versions 051 and 006. The GeoTIFF supplied in this dataset entry (GEE-TED_Kenya_2016-2017.tif) uses the aforementioned parameters to show the probable tsetse distribution across Kenya for the years 2016-2017. A static graphic of this GEE-TED output is shown below and an interactive version can be viewed at: https://cartoscience.users.earthengine.app/view/gee-ted. Figure associated with Fox et al. (2023) GEE code The code supplied below is generalizable across geographies and species; however, it is highly recommended that parameterization is given considerable attention to produce reliable results. Note that output visualization on-the-fly will take some time and it is recommended that results be exported as an asset within GEE or exported as a GeoTIFF. Note: Since completing the Fox et al. (2023) manuscript, GEE has removed Version 051 per NASA's deprecation of the product. The current release of GEE-TED now uses only MCD12Q1 Version 006; however, alternative LULC data selections can be used with minimal modification to the code. // Input options var tempMin = 10 // Temperature thresholds in degrees Celsius var tempMax = 40 var ndviMin = 0.39 // NDVI thresholds; proxy for available moisture/humidity var ndviMax = 1 var movement = 500 // Fly movement rate in meters/16-days var startYear = 2008 // The first 2 years will be used for model initialization var endYear = 2019 // Computed probability is based on startYear+2 to endYear var country = 'KE' // Country codes - https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var crs = 'EPSG:32737' // See https://epsg.io/ for appropriate country UTM zone var rescale = 250 // Output spatial resolution var labelSuffix = '02052020' // For file export labeling only //[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] MODIS/006/MCD12Q1 var lulcOptions006 = [1,1,1,1,1,1,1,1,1, 0, 1, 0, 0, 0, 0, 0, 0] // 1 = suitable 0 = unsuitable // No more input required ------------------------------ // var region = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") .filterMetadata('country_co', 'equals', country) // Input parameter modifications var tempMinMod = (tempMin+273.15)/0.02 var tempMaxMod = (tempMax+273.15)/0.02 var ndviMinMod = ndviMin*10000 var ndviMaxMod = ndviMax*10000 var ndviResolution = 250 var movementRate = movement+(ndviResolution/2) // Loading image collections var lst = ee.ImageCollection('MODIS/006/MOD11A2').select('LST_Day_1km', 'LST_Night_1km') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select('NDVI') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var lulc006 = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1') // Lulc mode and boolean reclassification var lulcMask = lulc006.mode().remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],lulcOptions006) .eq(1).rename('remapped').clip(region) // Merge NDVI and LST image collections var combined = ndvi.combine(lst, true) var combinedList = combined.toList(10000) // Boolean reclassifications (suitable/unsuitable) for day/night temperatures and ndvi var con =...

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GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live Map Data as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live Map Data include:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live Map Data include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.

Find alternative fueling stations near an address or ZIP code or along a route in the United States. Enter a state to see a station count. ## Data Collection Methods ## The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities coordinators, an Add a Station form on the Alternative Fuels Data Center (AFDC) website, and through collaborating with infrastructure equipment and fuel providers. NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies and inclusion criteria may result in slight differences between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to maintain the data. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, and propane industry groups to establish best practices for identifying new stations in the most-timely manner possible and to develop a more rigorous network for the future. ## Station Update Schedule ## Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and dispensing the fuel specified. Based on an established data collection schedule, the database is updated once a month with the exception of electric vehicle supply equipment (EVSE) data, which are updated twice a month. Stations that are no longer operational or no longer provide alternative fuel are removed from the database on a monthly basis or as they are identified. ## Mapping and Counting Methods ## Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or charging outlets at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool like Google Earth, Google Maps, or Google StreetView. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application. ## Notes about Specific Station Types ## ### Private Stations ### Stations with an Access of "Private - Fleet customers only" may allow other entities to fuel through a business-to-business arrangement. For more information, fleet customers should refer to the information listed in the details section for that station to contact the station directly. ### Biodiesel Stations ### The Alternative Fueling Station Locator only includes stations offering biodiesel blends of 20% (B20) and above. ### Electric Vehicle Supply Equipment (EVSE) ### An electric charging station, or EVSE, appears as one point on the map, regardless of the number of charging outlets at that location. The number and type of charging outlets available are displayed as additional details when the station location is selected. Each point on the map is counted as one station in the station count. To see a total count of EVSE for all outlets available, go to the Alternative Fueling Station Counts by State table. Residential EVSE locations are not included in the Alternative Fueling Station Locator. ## Liquefied Petroleum Gas (Propane) Stations ### Because many propane stations serve customers other than drivers and fleets, NREL collaborated with the industry to effectively represent the differences. Each propane station is designated as a 'primary' or 'secondary' service type. Both types are able to fuel vehicles. However, locations with a 'primary' designation offer vehicle services and fuel priced specifically for use in vehicles. The details page for each station lists its service designation.

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 professional map services market is experiencing robust growth, projected to reach $625.6 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 7.0% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of location-based services across diverse sectors like utilities, construction, transportation, and government is a primary driver. Advanced mapping technologies, including AI-powered mapping and real-time data integration, are enhancing the accuracy and functionality of map services, leading to increased demand. Furthermore, the growing need for precise mapping data for infrastructure planning, urban development, and disaster management is significantly contributing to market growth. The market segmentation reveals a strong reliance on consulting and advisory services, alongside significant demand for deployment and integration, and ongoing support and maintenance. Competition is fierce, with established players like Google, TomTom, and Esri vying for market share alongside emerging innovative companies specializing in niche applications. Geographic expansion is also a key aspect, with North America and Europe currently holding significant market share, but Asia-Pacific exhibiting rapid growth potential driven by infrastructure development and increasing technological adoption. The market's future trajectory appears bright, anticipating continued growth driven by technological advancements and expanding application areas. The integration of Internet of Things (IoT) data into mapping solutions presents a substantial opportunity for market expansion. The increasing reliance on autonomous vehicles and drone technology will further fuel demand for highly accurate and detailed mapping data. However, challenges remain, including data security concerns and the need for robust data management infrastructure. The competitive landscape necessitates continuous innovation and strategic partnerships to secure market share and capitalize on emerging opportunities. The ongoing development of standardized mapping data formats and protocols will play a crucial role in facilitating market growth and interoperability.

Abstract: We present a detailed geomorphological map (1:5000-scale) of a middle mountainous area in Jena, Germany. To overcome limitations associated with traditional field-based approaches and to extend the possibility of manually digital mapping in a structural way, we propose an approach using geographic information systems (GIS) and high-resolution digital data. The geomorphological map features were extracted by manually interpreting and analyzing the combination of different data sources using light detection and ranging (LiDAR) data. A combination of topographic and geological maps, digital orthophotos (DOPs), Google Earth images, field investigations, and derivatives from digital terrain models (DTMs) revealed that it is possible to generate and present the geomorphologic features involved in classical mapping approaches. We found that LiDAR-DTM and land surface parameters (LSPs) can provide better results when incorporating the visual interpretation of multidirectional hillshade and LSP composite maps. The genesis of landforms can be readily identified, and findings enabled us to systematically delineate landforms and geomorphological process domains. Although our approach provides a cost effective, objective, and reproducible alternative for the classical approach, we suggest that further use of digital data should be undertaken to support analysis and applications.

The Digital Earth Platform market is experiencing robust growth, driven by increasing government initiatives for infrastructure development, rising demand for precise location-based services, and the expanding adoption of advanced technologies like AI and machine learning for data analysis. The market's compound annual growth rate (CAGR) is estimated to be around 15% between 2025 and 2033, indicating a significant expansion opportunity. Key segments driving this growth include government departments and enterprise applications, which leverage the platform's capabilities for efficient resource management, urban planning, and environmental monitoring. The use of GPS and GIS technologies is central to the platform's functionality, enabling accurate mapping, spatial analysis, and real-time data visualization. North America and Europe currently hold the largest market share due to high technological adoption and robust government investments. However, the Asia-Pacific region is projected to witness significant growth in the coming years, fuelled by rapid urbanization and infrastructure development in countries like China and India. Competition in the market is intense, with major players including Microsoft, Google, and ESRI vying for market dominance through technological innovation and strategic partnerships. Market restraints include the high initial investment costs associated with implementing and maintaining Digital Earth Platforms, along with concerns surrounding data security and privacy. Despite these challenges, the long-term outlook for the Digital Earth Platform market remains exceptionally positive, driven by the increasing need for data-driven decision-making across various sectors. The market segmentation reveals diverse application scenarios. Government departments utilize the platform for comprehensive urban planning, disaster response, and environmental monitoring. Enterprises leverage its capabilities for optimizing logistics, supply chain management, and market analysis. Public services benefit from improved infrastructure management and resource allocation. Technological advancements are constantly shaping the market, with emerging trends focusing on cloud-based solutions, improved data analytics, and integration with Internet of Things (IoT) devices. This convergence of technologies promises enhanced data processing speeds, more comprehensive analysis, and the capacity to manage increasingly large and complex datasets efficiently. The continuous refinement of GPS and GIS technologies also contributes to the platform’s growing precision and usefulness across various applications. The market’s success hinges on the continuous development and deployment of user-friendly interfaces that allow seamless data access and visualization across a variety of devices and applications.

The digital map market is estimated to capture a valuation of US$ 18.3 billion in 2023 and is projected to reach US$ 73.1 billion by 2033. The market is estimated to secure a CAGR of 14.8% from 2023 to 2033.

How are the Various Regions Affecting the Growth of Digital Map in the Market?

Scope of Report

Global annual maps of livestock distribution of cattle, goats, sheep and horses at 1-km spatial resolution convering the period of 2000–2022. The maps were produced using harmonized and used as reference data (52,883 census polygons and 678,266 individual data points), random forest predictive models using and a large stack of multi-source harmonized gridded/raster spatial layers (307 individual raster spatial layers harmonized at 1 km spatial resolution).

Pixels values represent number of livestock animals (headcounts) calibrated according to FAOSTAT database. The adjustment was applied individually to each country, ensuring that the adjusted pixel values accurately reflected the national totals. For applications relying on headcount data from FAOSTAT the provided maps serve as a fully compatible alternative.

In line with a request from our funders, livestock maps will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine.

Related resources

• Maps of livestock headcount for 2000—2022 (FAOSTAT-adjusted):
  All animals
• Maps of livestock density for 2000—2022 (including prediction interval):
  Cattle Goat Sheep Horse
• Livestock reference census data (2000–2022):
  CSV and GeoPackage files
• Maps potential land for livestock production for 2000—2022 (including production systems)
  Raster files
• Data catalogues:
  OpenLandMap STAC Google Earth Engine

Components: Hardware: Includes mobile mapping systems, sensors, and other equipment Software: Includes software for data collection, processing, and visualization Services: Includes data collection, processing, and analysis servicesSolutions: Location-based: Provides location-based information and services Indoor mapping: Creates maps of indoor spaces Asset management: Helps manage assets and track their location 3D mapping: Creates 3D models of buildings and infrastructureApplications: Land surveys: Used for surveying land and creating maps Aerial surveys: Used for surveying areas from the air Real estate & construction: Used for planning and designing buildings and infrastructure IT & telecom: Used for network planning and management Recent developments include: One of the pioneers in wearable mobile mapping technology, NavVis, revealed the NavVis VLX 3, their newest generation of wearable technology. As the name suggests, this is the third version of their wearable VLX system; the NavVis VLX 2 was released in July of 2021, which is over two years ago. In their news release, NavVis emphasises the NavVis VLX 3's improved accuracy in point clouds by highlighting the two brand-new, 32-layer lidars that have been "meticulously designed and crafted" to minimise noise and drift in point clouds while delivering "high detail at range.", According to the North American Mach9 Software Platform, mobile Lidar will produce 2D and 3D maps 30 times faster than current systems by 2023., Even though this is Mach9's first product launch, the business has already begun laying the groundwork for future expansion by updating its website, adding important engineering and sales professionals, relocating to new headquarters in Pittsburgh's Bloomfield area, and forging ties in Silicon Valley., In order to make search more accessible to more users in more useful ways, Google has unveiled a tonne of new search capabilities for 2022 spanning Google Search, Google Lens, Shopping, and Maps. These enhancements apply to Google Maps, Google Shopping, Google Leons, and Multisearch., A multi-year partnership to supply Velodyne Lidar, Inc.'s lidar sensors to GreenValley International for handheld, mobile, and unmanned aerial vehicle (UAV) 3D mapping solutions, especially in GPS-denied situations, was announced in 2022. GreenValley is already receiving sensors from Velodyne., The acquisition of UK-based GeoSLAM, a leading provider of mobile scanning solutions with exclusive high-productivity simultaneous localization and mapping (SLAM) programmes to create 3D models for use in Digital Twin applications, is expected to close in 2022 and be completed by FARO® Technologies, Inc., a global leader in 4D digital reality solutions., November 2022: Topcon donated to TU Dublin as part of their investment in the future of construction. Students learning experiences will be improved by instruction in the most cutting-edge digital building techniques at Ireland's first technical university., October 2022: Javad GNSS Inc has released numerous cutting-edge GNSS solutions for geospatial applications. The TRIUMPH-1M Plus and T3-NR smart antennas, which employ upgraded Wi-Fi, Bluetooth, UHF, and power management modules and integrate the most recent satellite tracking technology into the geospatial portfolio, are two examples of important items.. Key drivers for this market are: Improvements in GPS, LiDAR, and camera technologies have significantly enhanced the accuracy and efficiency of mobile mapping systems. Potential restraints include: The initial investment required for mobile mapping equipment, including sensors and software, can be a barrier for small and medium-sized businesses.. Notable trends are: Mobile mapping systems are increasingly integrated with cloud platforms and AI technologies to process and analyze large datasets, enabling more intelligent mapping and predictive analytics.

Google is not only popular in its home country but is also the dominant internet search provider in many major online markets, frequently generating almost 80 percent of desktop search traffic. The search engine giant has a market share of over 90 percent in India and accounted for the majority of the global search engine market, 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 8 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 1.7 billion euros.

JavaScript code to be implemented in Google Earth Engine(c). The multi-scale relief model (MSRM) is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high- and low-resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM-X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software. The algorithm is tested in the Sutlej-Yamuna interfluve, which is a very large low-relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM.

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1Rvsb53lfYA00A2PJU22lSR44EBgtAThdeDrdhl_NGJk/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for corporate intelligence, market research, and lead generation.

Canaria’s LinkedIn Company Profile Data offers comprehensive information on over 25 million global companies, refreshed quarterly or biannually, with more frequent updates available for select subsets. This dataset captures essential company attributes, enabling actionable insights for data-driven business strategies.

Each company profile includes AI-validated attributes to ensure consistency and accuracy across the dataset: - Core Company Information: Access details on company name, industry, website, and unique identifiers, covering both headquarters and branch locations. - Leadership Details: Includes information on key executives such as the CEO, sourced from platform data and validated for precision. - Digital Presence: Links to company websites, profile URLs, and industry pages offer an expanded view of the company’s online footprint, supporting deeper analysis. - Company Metrics: Follower counts, ratings, and review metrics allow for assessment of a company’s public engagement and social influence. - Financial & Demographic Data: Company size, revenue range, employee counts, and founding year provide a complete view of each company’s market footprint.

Optional Google Maps Enrichment For enhanced geographic accuracy, Canaria’s optional Google Maps enrichment table includes branch-level insights like location coordinates, hours of operation, contact details, and business category. This additional data layer is ideal for location-based strategies that require precise branch data.

Core Industry Applications - Corporate Intelligence: Develop targeted insights on company growth, market positioning, and industry alignment for strategic planning. - Market Research: Leverage company size, revenue, and industry data for comprehensive views of U.S. and global market trends. - Lead Generation: Identify high-value leads using company size, industry, and location data, ideal for targeted outreach. - Account-Based Marketing (ABM): Tailor marketing efforts based on company demographics, enhancing the relevance and effectiveness of campaigns. Competitive Analysis: Assess competitor profiles, including digital presence and follower metrics, to inform competitive strategy.

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The Automotive 3D Map System market is experiencing robust growth, driven by the increasing adoption of Advanced Driver-Assistance Systems (ADAS) and autonomous driving technologies. The market size in 2025 is estimated at $10 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors: the escalating demand for enhanced navigation and location-based services, the proliferation of connected cars, and the imperative for improved safety and efficiency in autonomous vehicles. The integration of high-definition 3D maps into vehicle systems allows for precise localization, improved route planning, and the enablement of crucial safety features like lane keeping assist and automatic emergency braking. Furthermore, the continuous advancements in sensor technologies, such as LiDAR and radar, are contributing to the increased accuracy and detail of 3D maps, further accelerating market growth. Significant investments from both established automotive players and technology companies are fueling innovation and competition within the sector. Market segmentation reveals a dynamic landscape, with in-dash navigation systems holding a significant share currently, followed by portable navigation devices. However, the application in passenger vehicles dominates the market, albeit with substantial growth anticipated in light commercial vehicles, heavy-duty trucks, buses, and off-road vehicles. Geographic distribution shows strong market penetration in North America and Europe, driven by early adoption of autonomous vehicle technologies and stringent safety regulations. However, the Asia-Pacific region is projected to experience the fastest growth, fueled by increasing vehicle production and rising disposable incomes in developing economies. Restraints to growth include high initial investment costs for 3D mapping infrastructure and the need for robust cybersecurity measures to safeguard sensitive location data. Despite these challenges, the long-term outlook remains positive, with continued technological advancements and increasing government support paving the way for sustained expansion of the automotive 3D map system market.

The global Mobile Mapping Systems market is experiencing robust growth, projected to reach $20,740 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.0% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of autonomous vehicles and the need for highly accurate and detailed maps for navigation and advanced driver-assistance systems (ADAS) are significantly fueling market demand. Furthermore, the growth of smart cities initiatives, requiring comprehensive infrastructure mapping for efficient urban planning and management, is a major contributor. Government and public sector investments in infrastructure projects, coupled with rising demand for location-based services across various sectors like transportation and logistics, real estate, and video entertainment, are also boosting market growth. The shift towards cloud-based solutions and the integration of advanced technologies like LiDAR and GPS are further enhancing the capabilities and efficiency of mobile mapping systems, attracting broader adoption. The market is segmented by system type (Direct Mobile Mapping System and Backpack Mobile Mapping System) and application (Automobile, Transportation & Logistics, Government & Public Sector, Video Entertainment, Real Estate, Travel & Hospitality, and Other). While the Automobile sector currently holds a significant market share, the Government & Public Sector and Transportation & Logistics segments are expected to witness substantial growth due to increasing infrastructure development and the need for efficient logistics management. Competition in the market is intense, with major players including Ericsson, Microsoft, Apple, Google, and TomTom continuously innovating and expanding their product offerings to cater to the evolving demands of various industries. The market's geographical distribution is diverse, with North America and Europe currently leading in adoption, followed by the Asia-Pacific region, which is expected to demonstrate significant growth potential in the coming years driven by economic development and increasing urbanization. This comprehensive report analyzes the burgeoning Mobile Mapping Systems (MMS) market, projected to reach $15 billion by 2030. It delves into key trends, competitive landscapes, and growth drivers, providing invaluable insights for businesses and investors alike. The report leverages extensive market research and data analysis to provide actionable intelligence on this rapidly evolving technology. Keywords: Mobile Mapping, LiDAR, 3D Mapping, GIS, Location-Based Services, Autonomous Vehicles, Mapping Technology, Geospatial Data.

DISS is a georeferenced repository of tectonic, fault and paleoseismological information. The core objects of DISS are: - the individual seismogenic source, a simplified and three-dimensional representation of a fault plane. Individual seismogenic sources are assumed to exhibit "characteristic" behaviour with respect to rupture length/width and expected magnitude; - the composite seismogenic source, an elongated region containing an unspecified number of aligned seismogenic sources that cannot be singled out. Composite seismogenic sources are not associated with a specific set of earthquakes or earthquake distribution; - the debated seismogenic source, is an active fault that has been proposed in the literature as a potential seismogenic source but was not considered reliable enough to be included in the database. Individual and Composite seimogenic sources are two alternative seismic source models to choose from. They are tested against independent geophysical data to ensure the users about their level of reliability. A number of scientists have already used DISS in successful applications concerned with various aspects of seismic hazard. Each record in the Database is backed by a Commentary, a selection of Pictures, and a list of References, as well as fault scarp or fold axis data when available (usually structural features with documented Late Pleistocene - Holocene activity). The Database can be accessed through a web browser or displayed on Google Earth. Data e Risorse Questo dataset non ha dati terremoti

The Federal Motor Carrier Safety Administration (FMCSA) Hazardous Material Routes were developed using the 2004 First Edition TIGER/Line files. The routes are described in the National Hazardous Material Route Registry (NMHRR). The on-line NMHRR linkage is http://hazmat.fmcsa.dot.gov/nhmrr/index.asp With the exception of 13 features that were not identified with the Tiger/Lines, Hazmat routes were created by extracting the TIGER/Line segments that corresponded to each individual route. Hazmat routes in the NTAD, are organized into 3 database files, hazmat.shp, hmroutes.dbf, and hmstcnty.dbf. Each record in each database represents a unique Tiger/Line segment. These Tiger/Line segments are grouped into routes identified as character strings in the ROUTE_ID field in the hmroutes.dbf table. The route name appearing in the ROUTE_ID is assigned by FMCSA and is unique for each State [this sentence could be deleted - it doesn't add a lot to it]. The hmstcnty.dbf table allows the user to select routes by State and County. A single shapefile, called hazmat.shp, represents geometry for all routes in the United States.

© The Federal Motor Carrier Safety Administration (FMCSA) This layer is sourced from maps.bts.dot.gov.

The Federal Motor Carrier Safety Administration (FMCSA) Hazardous Material Routes (NTAD 2015) were developed using the 2004 First Edition TIGER/Line files. The routes are described in the National Hazardous Material Route Registry (NMHRR). The on-line NMHRR linkage is http://hazmat.fmcsa.dot.gov/nhmrr/index.asp With the exception of 13 features that were not identified with the Tiger/Lines, Hazmat routes were created by extracting the TIGER/Line segments that corresponded to each individual route. Hazmat routes in the NTAD, are organized into 3 database files, hazmat.shp, hmroutes.dbf, and hmstcnty.dbf. Each record in each database represents a unique Tiger/Line segment. These Tiger/Line segments are grouped into routes identified as character strings in the ROUTE_ID field in the hmroutes.dbf table. The route name appearing in the ROUTE_ID is assigned by FMCSA and is unique for each State [this sentence could be deleted - it doesn't add a lot to it]. The hmstcnty.dbf table allows the user to select routes by State and County. A single shapefile, called hazmat.shp, represents geometry for all routes in the United States.

© The Federal Motor Carrier Safety Administration (FMCSA)