Explore APISCRAPY, your AI-powered Google Map Data Scraper. Easily extract Business Location Data from Google Maps and other platforms. Seamlessly access and utilize publicly available map data for your business needs. Scrape All Publicly Available Data From Google Maps & Other Platforms.

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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

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training, validation, and evaluation data from the map georeferencing challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

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File generated from running the Extract Data solution.

The folder contains 3 shapefiles usable in GIS (geographic information system). These data result from the processing of the french map of devastated regions ("carte des régions dévastées"). The map was edited in 1920 by the geographic service of French army. The objective was to classify lands depending on the intensity of destruction, and to locate areas where substantial restoration work was necessary. The 47 map sheets of the collection at scale 1:50,000 have been scanned and can be obtained from the National Geographic Institute (IGN) in .jpg format. The map shows large red-colored zones representing heavily damaged front-line area by trenches and bombing according to the map legend. There are also red-hatched features locating destroyed cities, roads and destroyed or cut forests. The blue-colored symbols show new constructions, such as memorials and cemeteries. For the methodology of georeferencing, classification and vectorization, see Nelly Paradelle, Marianne Laslier, Guillaume DeCocq, "Automatic extraction of former WWI battlefields from ancient maps," Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 127270H (17 October 2023); https://doi.org/10.1117/12.2684009

-MANUAL_ENVELOPE.shp : This dataset contains the envelope bordering the local destructions from the dataset "RED POLYGONS", and drawn manually within QGIS.

-RED_POLYGONS.shp : This dataset contains only polygons of local destructions (cities, roads, buildings, destroyed or cut forests etc.) extracted from the map of devastated regions

-RED_ZONE.shp : This dataset contains only polygons of the large red-colored areas representing heavily damaged front-line area by trenches and bombing extracted from the map of devastated regions

Files with extension .qmd provide metadata.

A detailed terrain map or base map (1:500-1:2000) forms a base material for construction, planning and building formation activities, municipal engineering plans, various private maps, guide maps and theme maps.

Content: Base map items by target type. Area used to extract soil.

Published levels:

WFS interface:

• GIS:Kanta_Country_materials_output area

OGC API:

• Strain soil extraction area

Additional information:

• open spatial data interfaces

Web map used in Delaware County GIS Data Extract application that allows users to extract Delaware County, Ohio GIS data in various formats.

Analyses of minerals in chondrules and associated silica-rich igneous rims in CR chondrites. Data are from quantitative wavelength dispersive spectroscopy (WDS) X-ray maps obtained on the JEOL JXA-8530F electron microprobe at the University of Manchester. Powerpoint files show locations of areas extracted from WDS maps: each area is an individual analysis of a mineral grain. Excel files contain extracted data for each location.

Table of extracted data for the final 15 papers used for "A Systematic Mapping Study on the Debugging of Autonomous Driving Systems". Each paper title contains a link to the full paper whilst full citations for each can be found in the associated mapping study.

Input topographic maps, surface mine extents, and quad boundaries used in the following study:Maxwell, A.E., M.S. Bester, L.A. Guillen, C.A. Ramezan, D.J. Carpinello, Y. Fan, F.M. Hartley, S.M. Maynard, and J.L. Pyron, 2020. Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps, Remote Sensing, 12(24): 1-25. https://doi.org/10.3390/rs12244145.Associated code and descriptions of the data are provided on GitHub: https://github.com/maxwell-geospatial/topoDL. The surface mine extent data were obtained from the USGS prospect- and mine-related features from USGS topographic maps dataset: https://mrdata.usgs.gov/usmin/. Topographic maps were downloaded from TopoView/The National Map. We have simply prepared the data for easier ingestion into deep learning semantic segmentation workflows by aligning the vector polygon data with the associated topographic map and including topographic map boundaries to remove the collar information. Vector data can be rasterized and combined with the topographic maps to generate image chips and masks for semantic segmentation deep learning.The chip prep script on GitHub can be used to create chips and masks from these data. This compressed folder contains the following subfolders (ky_mines, ky_quads, ky_topos, oh_mines, oh_quads, oh_topos, va_mines, va_quads, va_topos). The mines folders contain the mine extents for each topographic map used in the study while the quads folders contain the quadrangle boundaries. All vector data are in shapefile format. The topos folders contain the topographic maps in TIFF format.

Step-wise reduction in candidate map pages.

This layer is a component of ENOW_Counties.

This map service presents spatial information about the Economics: National Ocean Watch (ENOW) data in the Web Mercator projection. The ENOW data provides time-series data on the ocean and Great Lakes economy, which includes six economic sectors dependent on the oceans and Great Lakes, and measures four economic indicators: Establishments, Employment, Wages, and Gross Domestic Product (GDP). The annual time-series data are available for about 400 coastal counties, 30 coastal states, 8 regions, and the nation. The service was developed by the National Oceanic and Atmospheric Administration (NOAA), but may contain data and information from a variety of data sources, including non-NOAA data. NOAA provides the information “as-is” and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. The NOAA Office for Coastal Management will make every effort to provide continual access to this service but it may need to be taken down during routine IT maintenance or in case of an emergency. If you plan to ingest this service into your own application and would like to be informed about planned and unplanned service outages or changes to existing services, please register for our Data Services Newsletter (http://coast.noaa.gov/digitalcoast/publications/subscribe). For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

© NOAA Office for Coastal Management

Map of the geothermal heat extraction capacity up to 100 m (in W/m) All boreholes in the Geological State Office that have a depth of at least 40 m have been evaluated with the values ​​of the geothermal extraction capacity (according to VDI 4640). Area maps of the possible heat extraction performance in Hamburg were developed from this point information.

Extraction Examples Dataset

This dataset contains 17 examples for testing extraction workflows.

  Dataset Structure

Each example includes:

PDF file: Original document map_info.json: Map extraction metadata direction.json: Direction information
GeoJSON files: Polygon geometries Area JSON files: Area definitions

  File Organization

files/ ├── example1/ │ ├── document.pdf │ ├── map_info.json │ ├── direction.json │ ├── polygon1.geojson │ └── area1.json… See the full description on the dataset page: https://huggingface.co/datasets/alexdzm/extraction-examples.

ADMMR map collection: Pay Roll Mine Dump Map; 1 in. to 20 feet; 36 x 26 in.

Piloted data extracted codebook for systematic map protocol in Moore, E., Howson, P., Grainger, M. et al. The role of participatory scenarios in ecological restoration: a systematic map protocol. Environ Evid 11, 23 (2022). https://doi.org/10.1186/s13750-022-00276-w

Export of data available in the Open Street map database from the weekly extraction by Geofabrik. This extraction can be imported into Osmium, Osmosis, Imposm, osm2pgsql, mkgmap, or ArcGis or Quantis GIS tools.

This is the extraction of the collaborative database produced by the OpenStreetMap project. This database is extremely rich and mainly focused on human activity: roads, streets, paths, extremely varied points of interest (town halls, churches, administrative services, tourist points, shops, etc.), etc. The OpenStreetMap project is somehow the wikipedia of mapping.

The Digital Geomorphic-GIS Map of Cape Lookout National Seashore, North Carolina (1:24,000 scale 2008 mapping) is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (calo_geomorphology.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 3.X map file (.mapx) file (calo_geomorphology.mapx) and individual Pro 3.X layer (.lyrx) 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 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 readme file (calo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (calo_geomorphology.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 (calo_geomorphology_metadata_faq.pdf). Please read the calo_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: 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: North Carolina Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (calo_geomorphology_metadata.txt or calo_geomorphology_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 Pro, 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).

The Digital Geologic-GIS Map of San Miguel Island, California 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 (smis_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 (smis_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 (smis_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.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_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 (smis_geology_metadata_faq.pdf). Please read the chis_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: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_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).