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!

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.

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

This dataset provides comprehensive local business and point of interest (POI) data from Google Maps in real-time. It includes detailed business information such as addresses, websites, phone numbers, emails, ratings, reviews, business hours, and over 40 additional data points. Perfect for applications requiring local business data (b2b lead generation, b2b marketing), store locators, and business directories. The dataset is delivered in a JSON format via REST API.

The Google Maps Platform (GMP) consulting services market is experiencing robust growth, driven by the increasing adoption of location-based services across various sectors. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for location intelligence and data-driven decision-making across large enterprises and SMEs is pushing companies to leverage GMP's capabilities. Secondly, the shift towards online services, facilitated by the increasing accessibility and affordability of high-speed internet, is bolstering the adoption of GMP consulting services for efficient mapping, navigation, and location-based marketing. Furthermore, advancements in augmented reality (AR) and virtual reality (VR) technologies integrated with GMP are creating new avenues for innovative applications, driving market growth. However, factors like the high cost of implementation and the need for specialized expertise can restrain market expansion. The market is segmented by application (large enterprises and SMEs) and service type (online and offline), with large enterprises currently dominating due to their greater resources and need for complex location-based solutions. Geographically, North America and Europe currently hold significant market shares, but the Asia-Pacific region is anticipated to exhibit the fastest growth rate due to rapid digitalization and increasing smartphone penetration. The competitive landscape is fragmented, with a mix of global consulting giants like Deloitte, Accenture, and WPP, alongside specialized GMP consulting firms such as MapsPeople and Applied Geographics. These companies are engaged in fierce competition, offering a range of services including integration, customization, application development, and ongoing support. The success of these firms is contingent on their ability to provide tailored solutions that cater to the unique needs of diverse industries and clients, and to continuously adapt to the ever-evolving features and functionalities of the GMP. A critical factor for future growth will be the ability to integrate GMP with other platforms and technologies to create holistic and effective solutions for clients, generating a compelling return on investment. This necessitates significant investment in R&D and upskilling of the workforce.

We are developing a set of NASA Extensions to the Google Maps API—and soon to other frameworks such as OpenLayers as well—that will make these platforms more useful to NASA scientists and our colleagues elsewhere.

Real-time API access to rich Business Listings Data of 200M+ local businesses & POI worldwide, in any category. The API serves business data points such as name, category, phone number, emails, website, reviews, photos, social links, and more.

Outscraper's Global Location Data service is an advanced solution for harnessing location-based data from Google Maps. Equipped with features such as worldwide coverage, precise filtering, and a plethora of data fields, Outscraper is your reliable source of fresh and accurate data.

Outscraper's Global Location Data Service leverages the extensive data accessible via Google Maps to deliver critical location data on a global scale. This service offers a robust solution for your global intelligence needs, utilizing cutting-edge technology to collect and analyze data from Google Maps and create accurate and relevant location datasets. The service is supported by a constant stream of reliable and current data, powered by Outscraper's advanced web scraping technology, guaranteeing that the data pulled from Google Maps is both fresh and accurate.

One of the key features of Outscraper's Global Location Data Service is its advanced filtering capabilities, allowing you to extract only the location data you need. This means you can specify particular categories, locations, and other criteria to obtain the most pertinent and valuable data for your business requirements, eliminating the need to sort through irrelevant records.

With Outscraper, you gain worldwide coverage for your location data needs. The service's advanced data scraping technology lets you collect data from any country and city without restrictions, making it an indispensable tool for businesses operating on a global scale or those looking to expand internationally. Outscraper provides a wealth of data, offering an unmatched number of fields to compile and enrich your location data. With over 40 data fields, you can generate comprehensive and detailed datasets that offer deep insights into your areas of interest.

The global reach of this service spans across Africa, Asia, and Europe, covering over 150 countries, including but not limited to Zimbabwe in Africa, Yemen in Asia, and Slovenia in Europe. This broad coverage ensures that no matter where your business operations or interests lie, you will have access to the location data you need.

Experience the Outscraper difference today and elevate your location data analysis to the next level.

The custom digital map service market is experiencing robust growth, driven by the increasing demand for location-based services across diverse sectors. The market, estimated at $8 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a compound annual growth rate (CAGR) of approximately 15%. This expansion is largely attributed to several key drivers. Firstly, the automotive industry's reliance on advanced navigation and driver-assistance systems is a major catalyst. Secondly, the burgeoning location services sector, encompassing ride-sharing, delivery services, and location-based advertising, fuels considerable demand for customized map solutions. Further propelling growth is the rise of business analytics, where customized maps provide invaluable insights into spatial data, optimizing logistics, resource management, and market analysis. Finally, the ongoing development of real-time map data technologies, offering dynamic updates and high accuracy, significantly enhances the value proposition of these services. While data security and privacy concerns pose some challenges, the overall market outlook remains positive. The market segmentation reveals a strong emphasis on custom map solutions, reflecting a growing need for tailored cartographic representations catering to specific business requirements. Real-time map data is another key segment, capitalizing on the demand for dynamic and up-to-date location information. Geographic distribution shows North America and Europe as leading markets, with significant growth potential in the Asia-Pacific region, particularly in rapidly developing economies like China and India. Key players in the market, including Google, TomTom, Mapbox, and others, are actively investing in research and development, pushing technological boundaries and expanding their service portfolios to maintain a competitive edge. The ongoing evolution of map technologies, including improvements in data accuracy, integration with AI/ML, and expanding functionalities like 3D mapping and augmented reality overlays, will further shape the market landscape in the coming years.

Content This dataset is composed of 10k images from Google Street Map.

The coords.csv file holds latitude and longitude information for all 10k images. The images themselves have a size of 640x640. All the coordinates come directly from google street map so they are 100% accurate.

Contribute The script to get those image is available as free software a https://github.com/paulchambaz/geotrouvetout.

License This dataset is licensed under the GPLv3 license, feel free to use it however you want.

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.

This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration, and management. The Census API is used to obtain population counts from the 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport the table to CSVThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the TIGER/Line shapefile and Census API downloads. The notebook can be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

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

National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...

Sample EV-to-RCS data obtained using Google Maps API.

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

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

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

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

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

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

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

The API-as-a-Service (AaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, microservices architecture, and the need for rapid application development. The market, estimated at $50 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 20% between 2025 and 2033, reaching approximately $200 billion by 2033. Key drivers include the rising demand for digital transformation initiatives across various industries, the simplification of application development through pre-built APIs, and the reduced infrastructure costs associated with AaaS solutions. Significant growth is observed across various application segments, including internal telecom developers, long-tail developers, and enterprises leveraging APIs for functionalities like identity management, payments, maps, and communication services (voice, SMS, MMS, and RCS). The market's geographic distribution is broad, with North America and Europe currently holding significant shares, but Asia-Pacific is projected to show the most rapid growth in the forecast period due to increasing digital adoption and infrastructure development. Competitive forces are strong, with established players like Google, Twilio, and MuleSoft vying for market share alongside specialized providers catering to specific API types. However, restraints on growth include security concerns surrounding API usage, the complexities of API integration, and the lack of standardized API formats across platforms. The AaaS market segmentation reveals significant opportunities for specialized vendors. The Identity Management API segment is particularly lucrative, driven by stringent regulatory compliance needs and the increasing importance of data privacy. Similarly, the Payment API segment benefits from the growth of e-commerce and digital transactions. The diverse range of companies involved, from tech giants to specialized API providers, indicates a healthy and competitive landscape. Successful players will likely be those offering robust security, ease of integration, comprehensive documentation, and strong customer support. Moreover, a shift towards serverless architectures and AI-powered API management platforms will shape future market trends. A focus on developer experience and the expansion into emerging markets, particularly in Asia-Pacific, will be critical factors in achieving sustained market growth.

http://www.openstreetmap.org/images/osm_logo.png" alt=""/> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

Documented March 19, 2023

!!NEW!!!

GeoDAR reservoirs were registered to the drainage network! Please see the auxiliary data "GeoDAR-TopoCat" at https://zenodo.org/records/7750736. "GeoDAR-TopoCat" contains the drainage topology (reaches and upstream/downstream relationships) and catchment boundary for each reservoir in GeoDAR, based on the algorithm used for Lake-TopoCat (doi:10.5194/essd-15-3483-2023).

Documented April 1, 2022

Citation

Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869–1899, 2022, https://doi.org/10.5194/essd-14-1869-2022.

Please cite the reference above (which was fully peer-reviewed), NOT the preprint version. Thank you.

Contact

Dr. Jida Wang, jidawang@ksu.edu, gdbruins@ucla.edu

Data description and components

Data folder “GeoDAR_v10_v11” (.zip) contains two consecutive, peer-reviewed versions (v1.0 and v1.1) of the Georeferenced global Dams And Reservoirs (GeoDAR) dataset:

• GeoDAR_v10_dams (in both shapefile format and the comma-separated values (csv) format): GeoDAR version 1.0, including 22,560 dam points georeferenced based on the World Register of Dams (WRD), the International Commission on Large Dams (ICOLD; https://www.icold-cigb.org; last access on March 13th, 2019).
• GeoDAR_v11_dams (in both shapefile and csv): GeoDAR version 1.1 dam points, including 24,783 dams which harmonized GeoDAR_v10_dams and the Global Reservoir and Dam Database (GRanD) v1.3 (Lehner et al., 2011).
• GeoDAR_v11_reservoirs (in shapefile): GeoDAR version 1.1 reservoirs, including 21,515 reservoir polygons retrieved by associating GeoDAR_v11_dams with GRanD v1.3 reservoirs, HydroLAKES v1.0 (Messager et al., 2016), and the UCLA Circa 2015 Lake Inventory (Sheng et al., 2016). The reservoir retrieval follows a one-to-one relationship between dams and reservoirs.

As by-products of GeoDAR harmonization, folder “GeoDAR_v10_v11” also contains:

• GRanD_v13_issues.csv: This file contains the original records of all 7,320 dam points in GRanD v1.3, with 94 of them marked by our identified issues and suggested corrections. These 94 records are placed at the beginning of this table. They include 89 records showing possible georeferencing and/or attribute errors, and another 5 records documented as subsumed or replaced. Our added fields start from column BG and include:
  • “Issue”: main issue(s) of this record
  • “Description”: more detailed explanation of the issue
  • “Lat_corrected”: suggested correction for latitude (if any) in decimal degree
  • “Lon_corrected”: suggested correction for longitude (if any) in decimal degree
  • “Correction_source”: correction source(s)
  • “Harmonized”: whether this GRanD dam was harmonized in GeoDAR v1.1 and the reason.
• Wada_et_al_2017_harmonized.csv: This csv file contains the original records of all 139 georeferenced large dams/reservoirs in Wada et al. (2017; doi:10.1007/s10712-016-9399-6), with our revised storage capacities and spatial coordinates for data harmonization. Our added fields start from column E and include:
  • Revised_capacity_km3: Our revised reservoir storage capacity in cubic kilometers used for harmonization
  • Revised_lat: Revised latitude in decimal degree
  • Revised_lon: Revised longitude in decimal degree
  • Verification_notes: Description of the issues, verification sources, and other information used for harmonization.

Attribute description

We first sampled the GSV points at intervals of 30 m from commercial road network data in Taipei City, Taiwan. We then applied the Google Map API with an authentication code to retrieve our sample points' latest panorama images from 2018-2022 (https://developers.google.com/maps/documentation/streetview/overview).We split the panoramic image into six separate images for each sample point with pitch angles of 0° and 45°. Thus, 12 images were obtained for further GVI computations for each point. The resolution of each image is 224×224 pixels. There were 86,637 sample points and 1,039,644 images included in this study.