Users can browse the map interactively or search by lot ID or address. Available basemaps include aerial images, topographic contours, roads, town landmarks, conserved lands, and individual property boundaries. Overlays display landuse, zoning, flood, water resources, and soil characteristics in relation to neighborhoods or parcels. Integration with Google Street View offers enhanced views of the 2D map location. Other functionality includes map markup, printing, viewing the property record card, and links to official tax maps where available.NRPC's implementation of MapGeo dates back to 2013, however it is the decades of foundational GIS data development at NRPC and partner agencies that has enabled its success. NRPC refreshes the assessing data yearly; the map data is maintained in an ongoing manner.

Approximate boundaries for all land parcels in New Brunswick. The boundaries are structured as Polygons. The Property Identifier number or PID is included for each parcel.

This data provides the integrated cadastral framework for Canada Lands. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), registration plans (RS) and location sketches (LS) archived in the Canada Lands Survey Records.

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!

NZ Parcel Boundaries Wireframe provides a map of land, road and other parcel boundaries, and is especially useful for displaying property boundaries.
This map service is for visualisation purposes only and is not intended for download. You can download the full parcels data from the NZ Parcels dataset.
This map service provides a dark outline and transparent fill, making it perfect for overlaying on our basemaps or any map service you choose.
Data for this map service is sourced from the NZ Parcels dataset which is updated weekly with authoritative data direct from LINZ’s Survey and Title system. Refer to the NZ Parcel layer for detailed metadata.
To simplify the visualisation of this data, the map service filters the data from the NZ Parcels layer to display parcels with a status of 'current' only.
This map service has been designed to be integrated into GIS, web and mobile applications via LINZ’s WMTS and XYZ tile services. View the Services tab to access these services.
See the LINZ website for service specifications and help using WMTS and XYZ tile services and more information about this service.

Data attributes are a snapshot of the BLM-AK Land Information System Database and are only accurate as that database. Alaska, being a state within the Public Land Survey System (PLSS), describes land to the aliquot part (subsections of larger land plots) where ever possible. Where data is not able to be described with an aliquot part, the data is generalized to the nearest PLSS section (640 acres). Natural boundaries, such as ridge lines and rivers, are examples where aliquot part descriptions can not be used. USS and Rectangular survey data has not been integrated when creating the geospatial depiction from database records. At this time, all positions are as calculated or as derived from township and section protraction.

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

Statewide soil and land information can be discovered and viewed through eSPADE or SEED. Datasets include soil profiles, soil landscapes, soil and land resources, acid sulfate soil risk mapping, hydrogeological landscapes, land systems and land use. There are also various statewide coverages of specific soil and land characteristics, such as soil type, land and soil capability, soil fertility, soil regolith, soil hydrology and modelled soil properties.

Both eSPADE and SEED enable soil and land data to be viewed on a map. SEED focuses more on the holistic approach by enabling you to add other environmental layers such as mining boundaries, vegetation or water monitoring points. SEED also provides access to metadata and data quality statements for layers.

eSPADE provides greater functions and allows you to drill down into soil points or maps to access detailed information such as reports and images. You can navigate to a specific location, then search and select multiple objects and access detailed information about them. You can also export spatial information for use in other applications such as Google Earth™ and GIS software.

eSPADE is a free Internet information system and works on desktop computers, laptops and mobile devices such as smartphones and tablets and uses a Google maps-based platform familiar to most users. It has over 42,000 soil profile descriptions and approximately 4,000 soil landscape descriptions. This includes the maps and descriptions from the Soil Landscape Mapping program. eSPADE also includes the base maps underpinning Biophysical Strategic Agricultural Land (BSAL).

For more information on eSPADE visit: https://www.environment.nsw.gov.au/topics/land-and-soil/soil-data/espade

This dataset contains documentation on the 146 global regions used to organize responses to the ArchaeGLOBE land use questionnaire between May 18 and July 31, 2018. The regions were formed from modern administrative regions (Natural Earth 1:50m Admin1 - states and provinces, https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-1-states-provinces/). The boundaries of the polygons represent rough geographic areas that serve as analytical units useful in two respects - for the history of land use over the past 10,000 years (a moving target) and for the history of archaeological research. Some consideration was also given to creating regions that were relatively equal in size. The regionalization process went through several rounds of feedback and redrawing before arriving at the 146 regions used in the survey. No bounded regional system could ever truly reflect the complex spatial distribution of archaeological knowledge on past human land use, but operating at a regional scale was necessary to facilitate timely collaboration while achieving global coverage. Map in Google Earth Format: ArchaeGLOBE_Regions_kml.kmz Map in ArcGIS Shapefile Format: ArchaeGLOBE_Regions.zip (multiple files in zip file) The shapefile format is a digital vector file that stores geographic location and associated attribute information. It is actually a collection of several different file types: .shp — shape format: the feature geometry .shx — shape index format: a positional index of the feature geometry .dbf — attribute format: columnar attributes for each shape .prj — projection format: the coordinate system and projection information .sbn and .sbx — a spatial index of the features .shp.xml — geospatial metadata in XML format .cpg — specifies the code page for identifying character encoding Attributes: FID - a unique identifier for every object in a shapefile table (0-145) Shape - the type of object (polygon) World_ID - coded value assigned to each feature according to its division into one of seventeen ‘World Regions’ based on the geographic regions used by the Statistics Division of the United Nations (https://unstats.un.org/unsd/methodology/m49/), with small changes to better reflect archaeological scholarly communities. These large regions provide organizational structure, but are not analytical units for the study. World_RG - text description of each ‘World Region’ Archaeo_ID - unique identifier (1-146) corresponding to the region code used in the ArchaeoGLOBE land use questionnaire and all ArchaeoGLOBE datasets Archaeo_RG - text description of each region Total_Area - the total area, in square kilometers, of each region Land-Area - the total area minus the area of all lakes and reservoirs found within each region (source: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-lakes/) PDF of Region Attribute Table: ArchaeoGLOBE Regions Attributes.pdf Excel file of Region Attribute Table: ArchaeoGLOBE Regions Attributes.xls Printed Maps in PDF Format: ArchaeoGLOBE Regions.pdf Documentation of the ArchaeoGLOBE Regional Map: ArchaeoGLOBE Regions README.doc

Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD.

Coal Mines in Illinois Viewer (ILMINES)If you are experiencing issues with interacting with this map, please make sure you have the most up-to-date web browser or try a different web browser. At this time the map may not load properly in the Mobile Google Chrome web browser for android, please try a different web browsing app.Instructions: The Coal Mines in Illinois Viewer illustrates a general depiction of underground mining in the state and will help determine the proximity of coal mines and underground industrial mines to your home or business. Please follow the instructions below for using this viewer and linking to additional map products that contain more information. Read the disclaimer below and click “Okay” when finished. This will bring up the map and search box. In the box that says “Find address or place” enter the address you are looking for and click the magnifying glass to the right or click “enter” on your keyboard. The map will recenter to the location entered. You can also use the navigation tools on the map to navigate to the location you are interested in.Consult the legend on the left for the types of mines displayed. Click on a mine you are interested in. In the box that pops up you will find links to the corresponding Quadrangle and/or County studies for the mine you are looking at. What is the yellow area on the map?Data ExplanationThese data were compiled by the ISGS for known underground and surface coal mines as well as underground industrial mineral mines. For more information including links to coal mine maps and informational directories, coal resource maps, and coal logs please see the County Coal Map Series.The underground coal mine points consist of mine entrances and may also contain uncertain underground mine locations. The underground mine proximity region incorporates coal mines as well as industrial mineral mines, and it was calculated and constructed using the methodology outlined in ISGS Circular 575. These generalized areas are not meant to replace site-specific studies; they conservatively illustrate areas overlying and adjacent to underground coal and industrial mineral mines that may potentially be exposed to subsidence based on 1) angle of draw from the edge of the underground workings up to the land surface, and 2) potential inaccuracy or uncertainty in mine boundary locations. Please see ISGS Circular 575. for a full explanation. Areas outside the proximity region also could be undermined. Old, undocumented mine openings have been discovered in many parts of the state. However, most undocumented mines were prospect pits or short-term operations that undermined only a few acres.The maps and digital files used for this study were compiled from data obtained from a variety of public and private sources and have varying degrees of completeness and accuracy. They present reasonable interpretations of the geology of the area and are based on available data. Locations of some features may be offset by 500 feet or more due to errors in the original source maps, the compilation process, digitizing, or a combination of these factors. These data are not intended for use in site-specific screening or decision-making.If you believe that you have mine subsidence contact your insurance companyand download: Mine Subsidence in Illinois: Facts for Homeowners - Circular 569, 2013, 9 MB PDF fileData DisclaimerThe Illinois State Geological Survey and the University of Illinois make no guarantee, expressed or implied, regarding the correctness of the interpretations presented in this data set and accept no liability for the consequences of decisions made by others on the basis of the information presented here.ISGS Terms of Usehttps://isgs.illinois.edu/terms-useUniversity of Illinois web privacyhttps://www.vpaa.uillinois.edu/resources/web_privacyQuestions about ILMINES/Contact usEmail

This resource is a repository of the annual subsurface drainage (so-called "Tile Drainage") maps for the Bois de Sioux Watershed (BdSW), Minnesota and the Red River of the North Basin (RRB), separately. The RRB maps cover a 101,500 km2 area in the United States, which overlies portions of North Dakota, South Daokta, and Minnesota. The maps provide annual subsurface drainage system maps for recent four years, 2009, 2011, 2014, and 2017 (In 2017, the subsurface drainage maps including the Sentinel-1 Synthetic Aperture Radar as an additional input are also provided). Please see Cho et al. (2019) in Water Resources Research (WRR) for full details.

Map Metadata (Proj=longlat +datum=WGS84) Raster value key: 0 = NoData, masked by non-agricultural areas (e.g. urban, water, forest, or wetland land) and high gradient cultivated crop areas (slope > 2%) based on the USGS National Land Cover Dataset (NLCD) and the USGS National Elevation Dataset 1 = Undrained (UD) 2 = Subsurface Drained (SD)

Preferred citation: Cho, E., Jacobs, J. M., Jia, X., & Kraatz, S. (2019). Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine. Water Resources Research, 55. https://doi.org/10.1029/2019WR024892

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu)

Since their introduction in 2012, Local Climate Zones (LCZs) emerged as a new standard for characterizing urban landscapes, providing a holistic classification approach that takes into account micro-scale land-cover and associated physical properties. This global map of Local Climate Zones, at 100m pixel size and representative for the nominal year …

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This dataset was created to facilitate the BLM Greater Sage-Grouse Land Use Planning Strategy in the Utah Sub-Region. This data was developed and addressed, and used during preparation of a draft and final environmental impact statement and the record of decision to amend 14 BLM land use plans throughout the State of Utah. This planning process was initiated through issuance of a Notice of Intent published on December 6, 2011. This dataset is associated with the Record of Decision and Approved Resource Management Plan Amendments for the Great Basin Region, released to the public via a Notice of Availability on September 24, 2015. The purpose of the planning process was to address protection of greater sage-grouse, in partial response to a March 2010 decision by the U.S. Fish and Wildlife Service (FWS) that found the greater sage-grouse was eligible for listing under the authorities of the Endangered Species Act. The planning process resulted in preparation of a draft environmental impact statement (DEIS) and final environmental impact statement (FEIS) in close coordination with cooperating agencies for the planning effort. The planning effort addressed the adequacy of regulatory mechanisms found in the land use plans, as well as addressing the myriad threats to grouse and their habitat that were identified by the FWS. This polygon is largely based on the existing land use plan boundaries which had a Record of Decision as of the initiation of the amendment process.

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.

Maps of cropland conversion classes, year of conversion, and pre- and post-conversion land cover associated with Lark et al. (2020). This repository also includes maps of 'local' and 'national' yield differentials for corn, soybeans, and wheat that are associated with the same publication. Code used to generate these data can be found here.

• Lark, T.J., S.A. Spawn, M.F. Bougie, H.K. Gibbs. Cropland expansion in the United States produces marginal yields with disproportionate costs to wildlife. Nature Communications (In review)

Cropland conversion maps are included in a zipped ESRI Geodatabase titled "US_land_conversion_2008-16.gdb". Each feature layer encompasses all of the conterminous United States at a 30m spatial resolution. Feature layers include:

• mtr = "Multi-temporal results"; Classifies land as being one of five broad land use change classes during the 2008-16 study period:
  1. "stable non-cropland" -- areas of consistent non-cropland throughout the duration of the study period.
  2. "stable cropland" -- areas of consistent cropland throughout the duration of the study period.
  3. "cropland expansion" -- areas converted to crop production between 2008 and 2016.
  4. "cropland abandonment" -- areas converted away from crop production between 2008 and 2016.
  5. "intermittent cropland/confusion" -- areas that were cropped for at least two years but show no clear trend towards or away from cropland. These could include areas under a crop-pasture rotation, fallow rotations, or simply areas with repeated classifier confusion.
• ytc = "year to cropland"; Indicates the year in which pixels with an mtr classification of "3" (i.e. "cropland expansion") were converted from non-cropland to cropland. e.g., a value of 2009 represents land that was converted between the 2008 growing season and the 2009 growing season.
• yfc = "year from cropland"; Indicates the year in which pixels with an mtr classification of "4" (i.e. "cropland abandonment") were converted from cropland to non-cropland. e.g., a value of 2009 represents land that was still cropped in 2008 and no longer cropped during the 2009 growing season.
• bfc = "before first crop"; Indicates the last land cover class before a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• fc = "first crop"; Indicates the class of the first crop planted after a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• bfnc = "before first non-crop"; Indicates the last cropland class of a pixel before it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• fnc = "first non-crop"; Indicates the first non-crop class of a pixel after it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.

Yield differential maps are included in the "yieldDifferentials.zip" folder as GeoTIFF rasters with a ~10km spatial resolution. Raster values represent relative (%) differences between the representative yields of new croplands (mtr = 3) and those of stable croplands (mtr = 1) planted to that crop within either (i) the larger 10km x 10km gridcell in which those fields are situated ("local" differentials) or (ii) the entire nation ("national" differentials).

• corn_relDiff_local.tif = local yield differential (%) of corn grain.
• corn_relDiff_national.tif = national yield differential (%) of corn grain.
• soy_relDiff_local.tif = local yield differential (%) of soybeans.
• soy_relDiff_national.tif = national yield differential (%) of soybeans.
• wheat_relDiff_local.tif = local yield differential (%) of wheat.
• wheat_relDiff_national.tif = national yield differential (%) of wheat.

This dataset was created by the Transportation Planning and Programming (TPP) Division of the Texas Department of Transportation (TxDOT) for planning and asset inventory purposes, as well as for visualization and general mapping. County boundaries were digitized by TxDOT using USGS quad maps, and converted to line features using the Feature to Line tool. This dataset depicts a generalized coastline.Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

The Iran-wide land cover map was generated by processing Sentinel imagery within the Google Earth Engine Cloud platform. For this purpose, over 2,500 Sentinel-1 and over 11,000 Sentinel-2 images were processed to produce a single mosaic dataset for the year 2017. Then, an object-based Random Forest classification method was trained by a large number of reference samples for 13 classes to generate the Iran-wide land cover map.

BLM National America's National Conservation Lands Map