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

Are you looking to identify B2B leads to promote your business, product, or service? Outscraper Google Maps Scraper might just be the tool you've been searching for. This powerful software enables you to extract business data directly from Google's extensive database, which spans millions of businesses across countless industries worldwide.

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.

Whether you are a small business trying to make a mark or a large enterprise exploring new territories, the data obtained from the Outscraper Google Maps Scraper can be a treasure trove. This tool provides a cost-effective, efficient, and accurate method to generate leads and gather market insights.

By using Outscraper, you'll gain a significant competitive edge as it allows you to analyze your market and find potential B2B leads with precision. You can use this data to understand your competitors' landscape, discover new markets, or enhance your customer database. The tool offers the flexibility to extract data based on specific parameters like business category or geographic location, helping you to target the most relevant leads for your business.

In a world that's growing increasingly data-driven, utilizing a tool like Outscraper Google Maps Scraper could be instrumental to your business' success. If you're looking to get ahead in your market and find B2B leads in a more efficient and precise manner, Outscraper is worth considering. It streamlines the data collection process, allowing you to focus on what truly matters – using the data to grow your business.

https://outscraper.com/google-maps-scraper/

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

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.

Dynamic World is a 10m near-real-time (NRT) Land Use/Land Cover (LULC) dataset that includes class probabilities and label information for nine classes. Dynamic World predictions are available for the Sentinel-2 L1C collection from 2015-06-27 to present. The revisit frequency of Sentinel-2 is between 2-5 days depending on latitude. Dynamic World predictions are generated for Sentinel-2 L1C images with CLOUDY_PIXEL_PERCENTAGE <= 35%. Predictions are masked to remove clouds and cloud shadows using a combination of S2 Cloud Probability, Cloud Displacement Index, and Directional Distance Transform. Images in the Dynamic World collection have names matching the individual Sentinel-2 L1C asset names from which they were derived, e.g: ee.Image('COPERNICUS/S2/20160711T084022_20160711T084751_T35PKT') has a matching Dynamic World image named: ee.Image('GOOGLE/DYNAMICWORLD/V1/20160711T084022_20160711T084751_T35PKT'). All probability bands except the "label" band collectively sum to 1. To learn more about the Dynamic World dataset and see examples for generating composites, calculating regional statistics, and working with the time series, see the Introduction to Dynamic World tutorial series. Given Dynamic World class estimations are derived from single images using a spatial context from a small moving window, top-1 "probabilities" for predicted land covers that are in-part defined by cover over time, like crops, can be comparatively low in the absence of obvious distinguishing features. High-return surfaces in arid climates, sand, sunglint, etc may also exhibit this phenomenon. To select only pixels that confidently belong to a Dynamic World class, it is recommended to mask Dynamic World outputs by thresholding the estimated "probability" of the top-1 prediction.

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 factors. The increasing adoption of location-based services (LBS) across various sectors, including transportation, logistics, and e-commerce, is a primary driver. Furthermore, the proliferation of smartphones and connected devices, coupled with advancements in GPS technology and mapping software, continues to fuel market growth. The rising demand for high-resolution, real-time mapping data for autonomous vehicles and smart city initiatives also significantly contributes to market expansion. Competition among established players like Google, TomTom, and ESRI, alongside emerging innovative companies, is fostering continuous improvement in map accuracy, functionality, and data accessibility. This competitive landscape drives innovation and lowers costs, making digital maps increasingly accessible to a broader range of users and applications. However, market growth is not without its challenges. Data security and privacy concerns surrounding the collection and use of location data represent a significant restraint. Ensuring data accuracy and maintaining up-to-date map information in rapidly changing environments also pose operational hurdles. Regulatory compliance with differing data privacy laws across various jurisdictions adds another layer of complexity. Despite these challenges, the long-term outlook for the digital map market remains positive, driven by the relentless integration of location intelligence into nearly every facet of modern life, from personal navigation to complex enterprise logistics solutions. The market's segmentation (although not explicitly provided) likely includes various map types (e.g., road maps, satellite imagery, 3D maps), pricing models (subscriptions, one-time purchases), and industry verticals served. This diversified market structure further underscores its resilience and potential for sustained growth. 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: 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. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

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.

A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. Please see the associated publications: 1. Peter, B.G., Cohen, S., Lucey, R., Munasinghe, D., Raney, A. and Brakenridge, G.R., 2020. Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for rapid and large scale flood analysis. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5. https://ieeexplore.ieee.org/abstract/document/9242297 2. Cohen, S., Peter, B.G., Haag, A., Munasinghe, D., Moragoda, N., Narayanan, A. and May, S., 2022. Sensitivity of remote sensing floodwater depth calculation to boundary filtering and digital elevation model selections. Remote Sensing, 14(21), p.5313. https://github.com/csdms-contrib/fwdet 3. Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz, G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065. https://doi.org/10.5194/nhess-19-2053-2019 4. Cohen, S., G. R. Brakenridge, A. Kettner, B. Bates, J. Nelson, R. McDonald, Y. Huang, D. Munasinghe, and J. Zhang (2018), Estimating Floodwater Depths from Flood Inundation Maps and Topography, Journal of the American Water Resources Association, 54 (4), 847–858. https://doi.org/10.1111/1752-1688.12609 Sample products and data availability: https://sdml.ua.edu/models/fwdet/ https://sdml.ua.edu/michigan-flood-may-2020/ https://cartoscience.users.earthengine.app/view/fwdet-gee-mi https://alabama.app.box.com/s/31p8pdh6ngwqnbcgzlhyk2gkbsd2elq0 GEE implementation output: fwdet_gee_brazos.tif ArcMap implementation output (see Cohen et al. 2019): fwdet_v2_brazos.tif iRIC validation layer (see Nelson et al. 2010): iric_brazos_hydraulic_model_validation.tif Brazos River inundation polygon access in GEE: var brazos = ee.FeatureCollection('users/cartoscience/FwDET-GEE-Public/Brazos_River_Inundation_2016') Nelson, J.M., Shimizu, Y., Takebayashi, H. and McDonald, R.R., 2010. The international river interface cooperative: public domain software for river modeling. In 2nd Joint Federal Interagency Conference, Las Vegas, June (Vol. 27). Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # FwDET-GEE calculates floodwater depth from a floodwater extent layer and a DEM Authors: Brad G. Peter, Sagy Cohen, Ronan Lucey, Dinuke Munasinghe, Austin Raney Emails: bpeter@ua.edu, sagy.cohen@ua.edu, ronan.m.lucey@nasa.gov, dsmunasinghe@crimson.ua.edu, aaraney@crimson.ua.edu Organizations: BP, SC, DM, AR - University of Alabama; RL - University of Alabama in Huntsville Last Modified: 10/08/2020 To cite this code use: Peter, Brad; Cohen, Sagy; Lucey, Ronan; Munasinghe, Dinuke; Raney, Austin, 2020, "A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET-GEE)", https://doi.org/10.7910/DVN/JQ4BCN, Harvard Dataverse, V2 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDETv2.0) [1] developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data. GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet ------------------------------------------------------------------------------------------------------------------------- How to run this code with your flood extent GEE asset: User of this script will need to update path to flood extent (line 32 or 33) and select from the processing options. Available DEM options (1) are USGS/NED (U.S.) and USGS/SRTMGL1_003 (global). Other options include (2) running the elevation outlier filtering algorithm, (3) adding water body data to the inundation extent, (4) add a water body data layer uploaded by the user rather than using the JRC global surface water data, (5) masking out regular water body data, (6) masking out 0 m depths, (7) choosing whether or not to export, (8) exporting additional data layers, and (9) setting an export file name....

Elevate your B2B marketing strategy with B2B Email Databases' premier Google Maps Data Extraction Service. Our cutting-edge solution offers direct access to a wealth of business information from Google's extensive database, encompassing millions of businesses across a multitude of industries worldwide.

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Google Data for Market Intelligence, Business Validation & Lead Enrichment Google Data is one of the most valuable sources of location-based business intelligence available today. At Canaria, we’ve built a robust, scalable system for extracting, enriching, and delivering verified business data from Google Maps—turning raw location profiles into high-resolution, actionable insights.

Our Google Maps Company Profile Data includes structured metadata on businesses across the U.S., such as company names, standardized addresses, geographic coordinates, phone numbers, websites, business categories, open hours, diversity and ownership tags, star ratings, and detailed review distributions. Whether you're modeling a market, identifying leads, enriching a CRM, or evaluating risk, our Google Data gives your team an accurate, up-to-date view of business activity at the local level.

This dataset is updated daily and is fully customizable, allowing you to pull exactly what you need, whether you're targeting a specific geography, industry segment, review range, or open-hour window.

What Makes Canaria’s Google Data Unique? • Location Precision – Every business record is enriched with latitude/longitude, ZIP code, and Google Plus Code to ensure exact geolocation • Reputation Signals – Review tags, star ratings, and review counts are included to allow brand sentiment scoring and risk monitoring • Diversity & Ownership Tags – Capture public-facing declarations such as “women-owned” or “Asian-owned” for DEI, ESG, and compliance applications • Contact Readiness – Clean, standardized phone numbers and domains help teams route leads to sales, support, or customer success • Operational Visibility – Up-to-date open hours, categories, and branch information help validate which locations are active and when

Our data is built to be matched, integrated, and analyzed—and is trusted by clients in financial services, go-to-market strategy, HR tech, and analytics platforms.

What This Google Data Solves Canaria Google Data answers critical operational, market, and GTM questions like:

• Which businesses are actively operating in my target region or category? • Which leads are real, verified, and tied to an actual physical branch? • How can I detect underperforming companies based on review sentiment? • Where should I expand, prospect, or invest based on geographic presence? • How can I enhance my CRM, enrichment model, or targeting strategy using location-based data?

Key Use Cases for Google Maps Business Data Our clients leverage Google Data across a wide spectrum of industries and functions. Here are the top use cases:

Lead Scoring & Business Validation • Confirm the legitimacy and physical presence of potential customers, partners, or competitors using verified Google Data • Rank leads based on proximity, star ratings, review volume, or completeness of listing • Filter spammy or low-quality leads using negative review keywords and tag summaries • Validate ABM targets before outreach using enriched business details like phone, website, and hours

Location Intelligence & Market Mapping • Visualize company distributions across geographies using Google Maps coordinates and ZIPs • Understand market saturation, density, and white space across business categories • Identify underserved ZIP codes or local business deserts • Track presence and expansion across regional clusters and industry corridors

Company Risk & Brand Reputation Scoring • Monitor Google Maps reviews for sentiment signals such as “scam”, “spam”, “calls”, or service complaints • Detect risk-prone or underperforming locations using star rating distributions and review counts • Evaluate consistency of open hours, contact numbers, and categories for signs of listing accuracy or abandonment • Integrate risk flags into investment models, KYC/KYB platforms, or internal alerting systems

CRM & RevOps Enrichment • Enrich CRM or lead databases with phone numbers, web domains, physical addresses, and geolocation from Google Data • Use business category classification for segmentation and routing • Detect duplicates or outdated data by matching your records with the most current Google listing • Enable advanced workflows like field-based rep routing, localized campaign assignment, or automated ABM triggers

Business Intelligence & Strategic Planning • Build dashboards powered by Google Maps data, including business counts, category distributions, and review activity • Overlay business presence with population, workforce, or customer base for location planning • Benchmark performance across cities, regions, or market verticals • Track mobility and change by comparing past and current Google Maps metadata

DEI, ESG & Ownership Profiling • Identify minority-owned, women-owned, or other diversity-flagged companies using Google Data ownership attributes • Build datasets aligned with supplier diversity mandates or ESG investment strategies • Segment location insights by ownership type ...

L'outil Hakai Google Earth Engine Kelp (outil GEEK) a été développé dans le cadre d'une collaboration entre l'Institut Hakai, l'Université de Victoria et le ministère des Pêches et des Océans pour tirer parti des capacités de cloud computing pour analyser l'imagerie satellite Landsat (30 m) afin d'extraire l'étendue de la canopée et du varech. La méthodologie originale est décrite dans Nijland et al. 2019*.

Remarque : Ce jeu de données est conçu comme une « lecture seule », car nous continuons à améliorer les résultats. Il vise à démontrer l'utilité de l'archive Landsat pour cartographier le varech. Ces données sont visibles sur la carte Web GEEK disponible ici.

Ce package de données contient deux jeux de données :

Etendue annuelle maximale estivale du varech formant la canopée (1984 - 2019) en tant que rasters. Etendue maximale décennale du varech formant la canopée (1984 - 1990, 1991 - 2000, 2001 - 2010, 2011 - 2020)

Ce jeu de données a été généré à la suite de modifications apportées aux méthodologies GEEK originales. Les paramètres utilisés pour générer les rasters étaient des scènes d'images avec :

Plage de mois Imagescene = 1er mai - 30 septembre Clouds maximum dans la scène = 80% Marée maximale = 3,2 m (+0,5 MWL des marées de la côte centrale selon les méthodes KIM-1) Marée minimale = 0 m Tampon de rivage appliqué au masque de terrain = 1 pixel (30 m) NDVI* minimum (pour qu'un pixel individuel soit classé comme varech) = -0,05 Nombre minimum de fois qu'un pixel de varech individuel doit être détecté en tant que varech au cours d'une seule année = 30 % de toutes les détections d'une année donnée K moyenne minimale (moyenne du NDVI pour tous les pixels à un emplacement donné détecté comme varech) = -0,05 * NDVI = indice de végétation de différence normalisée.

Ces paramètres ont été choisis sur la base d'une évaluation de la précision à l'aide d'une étendue de varech dérivée d'images WorldView-2 (2 m) de juillet 2014 et août 2014. Ces données ont été rééchantillonnées à 30 m. Bien que de nombreuses itérations exécutées pour l'outil aient donné des résultats très similaires, des paramètres ont été sélectionnés qui ont maximisé la précision du varech pour la comparaison de 2014.

Les résultats de l'évaluation de la précision ont été les suivants : Erreur de commission de 50 % Erreur d'omission de 25 %

En termes simples, les méthodes actuelles conduisent à un niveau élevé de « faux positifs », mais elles capturent avec précision l'étendue du varech par rapport au jeu de données de validation. Cette erreur peut être attribuée à la sensibilité de l'utilisation d'un seul NDVI pour détecter le varech. Nous observons des variations des seuils NDVI à la fois au sein d'une seule scène et entre les scènes.

L'objectif du jeu de données de séries chronologiques est censé prendre en compte une partie de cette erreur, car les pixels détectés seulement un par décennie sont supprimés.

Ce jeu de données fait partie du programme de cartographie de l'habitat de Hakai. L'objectif principal du programme de cartographie de l'habitat de Hakai est de générer des inventaires spatiaux des habitats côtiers, d'étudier comment ces habitats évoluent au fil du temps et les moteurs de ce changement.

*Nijland, W., Reshitnyk, L. et Rubidge, E. (2019). Télédétection par satellite de varech formant une canopée sur un littoral complexe : une nouvelle procédure utilisant les archives d'images Landsat. Télédétection de l'environnement, 220, 41-50. doi:10.1016/j.rse.2018.10.032

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

The SEN12TP dataset (Sentinel-1 and -2 imagery, timely paired) contains 2319 scenes of Sentinel-1 radar and Sentinel-2 optical imagery together with elevation and land cover information of 1236 distinct ROIs taken between 28 March 2017 and 31 December 2020. Each scene has a size of 20km x 20km at 10m pixel spacing. The time difference between optical and radar images is at most 12h, but for almost all scenes it is around 6h since the orbits of Sentinel-1 and -2 are shifted like that. Next to the \(\sigma^\circ\) radar backscatter also the radiometric terrain corrected \(\gamma^\circ\) radar backscatter is calculated and included. \(\gamma^\circ\) values are calculated using the volumetric model presented by Vollrath et. al 2020.

The uncompressed dataset has a size of 222 GB and is split spatially into a train (~90%) and a test set (~10%). For easier download the train set is split into four separate zip archives.

Please cite the following paper when using the dataset, in which the design and creation is detailed:
T. Roßberg and M. Schmitt. A globally applicable method for NDVI estimation from Sentinel-1 SAR backscatter using a deep neural network and the SEN12TP dataset. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2023. https://doi.org/10.1007/s41064-023-00238-y.

The file sen12tp-metadata.json includes metadata of the selected scenes. It includes for each scene the geometry, an ID for the ROI and the scene, the climate and land cover information used when sampling the central point, the timestamps (in ms) when the Sentinel-1 and -2 image was taken, the month of the year, and the EPSG code of the local UTM Grid (e.g. EPSG:32643 - WGS 84 / UTM zone 43N).

Naming scheme: The images are contained in directories called {roi_id}_{scene_id}, as for some unique regions image pairs of multiple dates are included. In each directory are six files for the different modalities with the naming {scene_id}_{modality}.tif. Multiple modalities are included: radar backscatter and multispectral optical images, the elevation as DSM (digital surface model) and different land cover maps.

Data modalities

name	Modality	GEE collection
s1	Sentinel-1 radar backscatter	COPERNICUS/S1_GRD
s2	Sentinel-2 Level-2A (Bottom of atmosphere, BOA) multispectral optical data with added cloud probability band	COPERNICUS/S2_SR COPERNICUS/S2_CLOUD_PROBABILITY
dsm	30m digital surface model	JAXA/ALOS/AW3D30/V3_2
worldcover	land cover, 10m resolution	ESA/WorldCover/v100

The following bands are included in the tif files, for an further explanation see the documentation on GEE. All bands are resampled to 10m resolution and reprojected to the coordinate reference system of the Sentinel-2 image.

Modality Bands

Modality	Band count	Band names in tif file	Notes
s1	5	VV_sigma0, VH_sigma0, VV_gamma0flat, VH_gamma0flat, incAngle	VV/VH_sigma0 are the \(\sigma^\circ\) values, VV/VH_gamma0flat are the radiometric terrain corrected \(\gamma^\circ\) backscatter values incAngle is the incident angle
s2	13	B1, B2, B3, B4, B5, B7, B7, B8, B8A, B9, B11, B12, cloud_probability	multispectral optical bands and the probability that a pixel is cloudy, calculated with the sentinel2-cloud-detector library optical reflectances are bottom of atmosphere (BOA) reflectances calculated using sen2cor
dsm	1	DSM	Height above sea level. Signed 16 bits. Elevation (in meter) converted from the ellipsoidal height based on ITRF97 and GRS80, using EGM96†1 geoid model.
worldcover	1	Map	Landcover class

Checking the file integrity
After downloading and decompression the file integrity can be checked using the provided file of md5 checksum.
Under Linux: md5sum --check --quiet md5sums.txt

References:

Vollrath, Andreas, Adugna Mullissa, Johannes Reiche (2020). "Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine". In: Remote Sensing 12.1, Art no. 1867. https://doi.org/10.3390/rs12111867.

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

Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020) -

This dataset provides high-resolution, nationwide land use/land cover (LULC) and terrestrial carbon stock maps of Pakistan for four epochs: 1990, 2000, 2010, and 2020. Developed using multi-sensor satellite imagery and advanced classification techniques in Google Earth Engine (GEE), the dataset presents a comprehensive analysis of land cover changes driven by urbanization and their impacts on carbon storage capacity over 30 years.

The LULC data includes nine distinct classes, covering key land cover types such as forest cover, agriculture, rangeland, wetlands, barren lands, water bodies, built-up areas, and snow/ice. Classification was performed using a hybrid machine learning approach, and the accuracy of the land cover maps was validated using a stratified random sampling approach.

The carbon stock maps were derived using the InVEST model, which estimated carbon storage in four major carbon pools (above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter) based on the LULC maps. The results showed a significant decline in carbon storage due to rapid urban expansion, particularly in major cities like Karachi and Lahore, where substantial forest and agricultural lands were converted into urban areas. The study estimates that Pakistan lost approximately -5% of its carbon storage capacity over this period, with urban areas growing by over ~1040%.

This dataset is a valuable resource for researchers, policymakers, and environmental managers, providing crucial insights into the long-term impacts of urbanization on land cover and carbon sequestration. It is expected to support future land management strategies, urban planning, and climate change mitigation efforts. The high temporal and spatial resolution of the dataset makes it ideal for monitoring land cover dynamics and assessing ecosystem services over time.

This dataset is aslo available as Google Earth Engine application. For more details check:

> Github Project repository: https://github.com/waleedgeo/lulc_pk
> Paper DOI: https://doi.org/10.1016/j.eiar.2023.107396
> Paper PDF: https://waleedgeo.com/papers/waleed2024_paklulc.pdf

If you find this work useful, please consider citing it as

Waleed, M., Sajjad, M., & Shazil, M. S. (2024). Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020). Environmental Impact Assessment Review, 105, 107396.

Contributors:
Mirza Waleed (email) (Linkedin)
Muhammad Sajjad (email) (Linkedin)
Muhammad Shareef Shazil

To check other work, please check:
My Webpage & Google Scholar

Used within the Travellers Road Information Portal Interactive Map to convey transportation related information in both official languages. Contains information about major construction projects, including restrictions and delays. This data is best viewed using Google Earth or similar Keyhole Markup Language (KML) compatible software. For instructions on how to use Google Earth, read the Google Earth tutorial .

The High Definition (HD) Maps market is experiencing robust growth, driven by the escalating demand for autonomous vehicles and Advanced Driver-Assistance Systems (ADAS). The market size in 2025 is estimated at $15.49 billion, projecting a significant expansion over the forecast period (2025-2033). While the provided CAGR (Compound Annual Growth Rate) is missing, considering the rapid technological advancements and increasing adoption of autonomous driving technologies, a conservative estimate would place the CAGR between 15% and 20% for the forecast period. This growth is fueled by several key factors, including the increasing accuracy and detail offered by HD maps compared to traditional maps, enabling safer and more efficient navigation for autonomous vehicles. The market is segmented by type (centralized vs. crowdsourced mapping) and application (autonomous vehicles, ADAS, others), with autonomous vehicles currently dominating the market share due to their critical reliance on precise and up-to-date map data. Major players like TomTom, Google, HERE Technologies, and Baidu Apollo are heavily investing in research and development, fostering innovation and competition within the market. Regional growth is expected to be geographically diverse, with North America and Europe leading the initial adoption, followed by a rapid expansion in the Asia-Pacific region driven by significant investments in autonomous vehicle infrastructure and technological advancements. The competitive landscape is characterized by both established map providers and technology giants entering the market. This intense competition is pushing innovation forward, leading to more accurate, detailed, and frequently updated HD maps. Challenges include the high cost of creating and maintaining HD maps, the need for continuous data updates to reflect dynamic road conditions, and data privacy concerns surrounding the collection and use of location data. Despite these challenges, the long-term outlook for the HD Maps market remains incredibly positive, fueled by the continuous advancement of autonomous driving technology and the increasing demand for improved road safety and traffic management solutions. The market's growth trajectory suggests significant opportunities for both established players and emerging companies in the years to come. We project a substantial increase in market size by 2033, exceeding the 2025 figures by a considerable margin, based on the estimated CAGR.

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.

Surface mining for coal has taken place in the Central Appalachian region of the United States for well over a century, with a notable increase since the 1970s. Researchers have quantified the ecosystem and health impacts stemming from mining, relying in part on a geospatial dataset defining surface mining’s extent at a decadal interval. This dataset, however, does not deliver the temporal resolution necessary to support research that could establish causal links between mining activity and environmental or public health and safety outcomes, nor has it been updated since 2005. Here we use Google Earth Engine and Landsat imagery to map the yearly extent of surface coal mining in Central Appalachia from 1985 through 2015, making our processing models and output data publicly available. We find that 2,900 km2 of land has been newly mined over this 31-year period. Adding this more-recent mining to surface mines constructed prior to 1985, we calculate a cumulative mining footprint of 5,900 km2. Over the study period, correlating active mine area with historical surface mine coal production shows that each metric ton of coal is associated with 12 m2 of actively mined land. Our automated, open-source model can be regularly updated as new surface mining occurs in the region and can be refined to capture mining reclamation activity into the future. We freely and openly offer the data for use in a range of environmental, health, and economic studies; moreover, we demonstrate the capability of using tools like Earth Engine to analyze years of remotely sensed imagery over spatially large areas to quantify land use change.

To represent the location of public fishing stream parking areas in New York State and can be used with programs like Google Earth, Google Maps, and DEC's State Lands Interactive Mapper ( http://www.dec.ny.gov/outdoor/45478.html ).Service layer is updated as needed and was last updated on 3/2024.For more information, please visit https://www.dec.ny.gov/outdoor/122444.html.1. The NYS DEC asks to be credited in derived products. 2. Secondary distribution of the data is not allowed. 3. Any documents provided is an integral part o the data set. Failure to use the documentation in conjunction with the digital data constitutes misuse of the data. 4. Although every effort has been made to ensure the accuracy of information, errors may be reflected in the data supplied. The user must be aware of data conditions and bear responsibility for the appropriate use of the information with respect to possible errors, original map scale, collection methodology, currency of data, and other conditions.

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.