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

Cloud-based image processing platforms like the Google Earth Engine (GEE) bring unprecedented possibilities for education, research, and outreach. This workshop will focus on an interactive exploration of GEE capabilities, the repository of all of publicly available aerial and satellite data, and user upload of imagery for analysis. The workshop will begin with a presentation of examples of GEE projects with a focus on education, undergraduate research, and outreach followed by hands-activities.

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Outscraper Google Maps Scraper is a tool built with advanced technology that lets you scrape a myriad of valuable information about businesses from Google's database. This information includes but is not limited to, business names, addresses, contact information, website URLs, reviews, ratings, and operational hours.

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As a result of the Google Maps scraping, your data file will contain the following details:

Query Name Site Type Subtypes Category Phone Full Address Borough Street City Postal Code State Us State Country Country Code Latitude Longitude Time Zone Plus Code Rating Reviews Reviews Link Reviews Per Scores Photos Count Photo Street View Working Hours Working Hours Old Format Popular Times Business Status About Range Posts Verified Owner ID Owner Title Owner Link Reservation Links Booking Appointment Link Menu Link Order Links Location Link Place ID Google ID Reviews ID

If you want to enrich your datasets with social media accounts and many more details you could combine Google Maps Scraper with Domain Contact Scraper.

Domain Contact Scraper can scrape these details:

Email Facebook Github Instagram Linkedin Phone Twitter Youtube

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

The Alabama Department of Transportation (ALDOT) and the U.S. Geological Survey (USGS) studied several sites in the northern East Gulf Coastal Plain of Alabama to investigate effects of newly installed box culverts on the natural conditions of the streams they are traversing (Pugh and Gill, 2021). Data collection for the study spanned approximately 10 years and included before-, during-, and after-construction phases of box culvert installations at selected stream sites. The objectives of the project were to (1) assess the degree and extent of changes in geomorphic conditions, suspended-sediment concentrations, turbidity, and benthic macroinvertebrate populations at selected small streams following box culvert installation and (2) identify any substantial relationships between observed changes in geomorphology and benthic macroinvertebrate populations. Aerial imagery for each study site, taken before, during and after culvert construction, was downloaded from Google Earth (https://earth.google.com/web/) and are presented as separate Portable Document Format (PDF) files labeled by site name and imagery date. Aerial imagery was examined to see if any natural or anthropogenic changes occurred in the areas surrounding the study sites. For example, examination of the High Log Creek imagery from 2013 and 2015 shows the forested area northwest of the study site was clear cut and the start of culvert construction occurred sometime between when the two images were taken.

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km². For each building in this dataset we include the polygon describing its footprint on the ground, a confidence score indicating how sure we are that this is a building, and a Plus Code corresponding to the center of the building. There is no information about the type of building, its street address, or any details other than its geometry. Building footprints are useful for a range of important applications: from population estimation, urban planning and humanitarian response to environmental and climate science. The project is based in Ghana, with an initial focus on the continent of Africa and new updates on South Asia, South-East Asia, Latin America and the Caribbean. Inference was carried out during May 2023. For more details see the official website of the Open Buildings dataset.

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of location-based services (LBS) across diverse sectors like automotive, logistics, and smart city initiatives is a primary catalyst. Furthermore, advancements in technologies such as AI, machine learning, and high-resolution satellite imagery are enabling the creation of more accurate, detailed, and feature-rich digital maps. The shift towards cloud-based deployment models offers scalability and cost-effectiveness, further accelerating market growth. While data privacy concerns and the high initial investment costs for sophisticated mapping technologies present some challenges, the overall market outlook remains overwhelmingly positive. The competitive landscape is dynamic, with established players like Google, TomTom, and ESRI vying for market share alongside innovative startups offering specialized solutions. The segmentation of the market by solution (software and services), deployment (on-premise and cloud), and industry reveals significant opportunities for growth in sectors like automotive navigation, autonomous vehicle development, and precision agriculture, where real-time, accurate mapping data is crucial. The Asia-Pacific region, driven by rapid urbanization and technological advancements in countries like China and India, is expected to witness particularly strong growth. The market's future hinges on continuous innovation. We anticipate a rise in the demand for 3D maps, real-time updates, and integration with other technologies like the Internet of Things (IoT) and augmented reality (AR). Companies are focusing on enhancing the accuracy and detail of their maps, incorporating real-time traffic data, and developing tailored solutions for specific industry needs. The increasing adoption of 5G technology promises to further boost the market by enabling faster data transmission and real-time updates crucial for applications like autonomous driving and drone delivery. The development of high-precision mapping solutions catering to specialized sectors like infrastructure management and disaster response will also fuel future growth. Ultimately, the digital map market is poised for continued expansion, driven by technological advancements and increased reliance on location-based services across a wide spectrum of industries. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Complexity in Integration of Traditional Maps with Modern GIS System. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

Google Earth Engine used to compute the NDVI statistics added to Globe-LFMC. The input of the program is a point shapefile (“samplePlotsShapefile”, extensions .cpg, .dbf, .prj, .shp, .shx) representing the location of each Globe-LFMC site. This shapefile is available as additional data in figshare (see Code Availability). To run this GEE code the shapefile needs to be uploaded into the GEE Assets and, then, imported into the Code Editor with the name “plots” (without quotation marks).Google Earth Engine codeChange Notice - GEE_script_for_GlobeLFMC_ndvi_stats_v2.jsThe following acknowledgements have been added at the beginning of the code: “Portions of the following code are modifications based on work created and shared by Google in Earth Engine Data Catalog and Earth Engine Guides under the Apache 2.0 License. https://www.apache.org/licenses/LICENSE-2.0”Change Notice - samplePlotsShapefile_v2The shapefile describing the database sites has been corrected and updated with the correct coordinates.

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.

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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.

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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.

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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.

GIS shapefiles of all buildings and disturbance detected across Antarctica, manually digitised from Google Earth images. The data set includes point locations for Automated Weather Stations (AWS), lighthouses, flight routes, maintained traverse routes, camp and hut sites, historic sites and monuments, and sites of current and former stations where mapping was not possible.

The following provides descriptions of the attributes within the GIS layers: 'STATION' refers to the name of the Research Station or Base

'NAME' refers to a named building within a station (e.g. 'Brookes Hut' which is part of 'DAVIS' within the 'STATION' attributes.

'Ice_free' refers to if a building is located on ice or in an ice-free environment '0' = a building on ice. '1' = on an ice-free environment.

'STATUS' refers to the use of the buildings: 1 = Closed site 2 = Lighthouse or camp 3 = Field hut or refuge 4 = Summer/seasonal only 5 = Year round operation.

These data were the output of: Brooks, S. T., Jabour, J., van den Hoff, J. and Bergstrom, D. M. Our footprint on Antarctica competes with nature for rare ice-free land. Nature Sustainability, doi:10.1038/s41893-019-0237-y (2019).

This dataset was last updated on the 30 October 2019 with six additional footprint locations added.

This dataset details all data used for a manuscript submission entitled "Spotting green tides over Brittany from space: three decades of monitoring with Landsat imagery". It presents data derived from Earth observation detection on the macroalgae surface on four studied sites in Brittany, France. These estimates were made using Landsat 5 and 8 satellite imagery, using the Google Earth Engine environment. Spectral signatures of natural features found on the study sites (sand, water and algae) are also presented. Additional datasets include 1) green macroalgae surface estimates made by an external source, CEVA (French Algae Technology and Innovation Center) and derived from aerial photography. This data was used for comparison with our results 2) nitrogen concentrations for four water stations close to the study sites. Nitrogen is considered the main physico-chemical factor controlling algae growth.

Clipping • How to clip a raster image to the extent of a vector polygon in order to speed up processing times as well to display only the imagery you want.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the …

Nighttime satellite imagery were accessed via Google Earth Engine). Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. In cases where two satellites were collecting data - two composites were produced. The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Several attributes are included - we used stable_lights which represents lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. The background noise was identified and replaced with values of zero.These data were provided to Google Earth Engine by teh National Centers for Environmental Information - National Oceanic and Atmospheric Administration of the United States (see Supporting Documentation).CANUE staff exported the annual data and extracted values of annual mean nighttime brightness for all postal codes in Canada for each year from 1992 to 2013 (DMTI Spatial, 2015).

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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Since the first nuclear bomb test in New Mexico in 1945, there have been over 2000 confirmed nuclear explosions around the globe. This dataset includes accurate to questionable locations of all 2000+. This data was found online at the Google Earth Community site. It was posted by the user Hill. Source: http://bbs.keyhole.com/ubb/showflat.php/Cat/0/Number/34290/an/0/page/0#34290 Accessed: 9.11.07

The Geologic Atlas of the United States is a set of 227 folios published by the U.S. Geological Survey between 1894 and 1945. Each folio includes both topographic and geologic maps for each quad represented in that folio, as well as description of the basic and economic geology of the area.

Includes a link to a Google Earth overlay which includes links to sites with raster information as well as a map on the webpage with the links present. A viewer can use the links displayed on page (inside numbers) to be led to sites with lists/catalogs of downloadable data. Includes JPEG, TIFF, and GIS data.

The Digital Geologic-GIS Map of Roosevelt-Vanderbilt National Historic Sites and Vicinity, New York is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (rova_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (rova_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (rova_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (rova_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (rova_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (rova_geology_metadata_faq.pdf). Please read the rova_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: New York Geological Survey and New York State Department of Transportation. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (rova_geology_metadata.txt or rova_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).