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.

Auburn Maine's parcel Inquiry map with optional zoning and high-resolution aerial photography. Optional zoning layers. Map provides detailed assessing data for each parcel as well as links to WebPro assessing records and Google Street View. Users can search for parcels using parcel ID, location, or owner name. Advanced search options provide ability to select and buffer parcels with an optional export to csv file.

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.

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.

APISCRAPY, your premier provider of Map Data solutions. Map Data encompasses various information related to geographic locations, including Google Map Data, Location Data, Address Data, and Business Location Data. Our advanced Google Map Data Scraper sets us apart by extracting comprehensive and accurate data from Google Maps and other platforms.

What sets APISCRAPY's Map Data apart are its key benefits:

1. Accuracy: Our scraping technology ensures the highest level of accuracy, providing reliable data for informed decision-making. We employ advanced algorithms to filter out irrelevant or outdated information, ensuring that you receive only the most relevant and up-to-date data.

2. Accessibility: With our data readily available through APIs, integration into existing systems is seamless, saving time and resources. Our APIs are easy to use and well-documented, allowing for quick implementation into your workflows. Whether you're a developer building a custom application or a business analyst conducting market research, our APIs provide the flexibility and accessibility you need.

3. Customization: We understand that every business has unique needs and requirements. That's why we offer tailored solutions to meet specific business needs. Whether you need data for a one-time project or ongoing monitoring, we can customize our services to suit your needs. Our team of experts is always available to provide support and guidance, ensuring that you get the most out of our Map Data solutions.

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1. B2B Marketing: Gain insights into customer demographics and behavior for targeted advertising and personalized messaging. Identify potential customers based on their geographic location, interests, and purchasing behavior.

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4. Geospatial Analysis: Leverage Map Data for spatial analysis, urban planning, and environmental monitoring. Identify trends and patterns in geographic data to inform decision-making in areas such as land use planning, resource management, and disaster response.

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Experience the power of APISCRAPY's Map Data solutions today and unlock new opportunities for your business. With our accurate and accessible data, you can make informed decisions, drive growth, and stay ahead of the competition.

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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.

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Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

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!

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.

California Department of Transportation (Caltrans), Division of Transportation Planning, Aeronautics Program provided airport layout drawings with estimated digitized airport property or fence lines with Google Pro images background.Caltrans Division of Research, Innovation and System Information (DRISI) GIS office digitized the airport boundary lines with Bing Maps Aerial background and built the boundary lines into a GIS polygon feature class.Generally, Airport Layout Plans do not show complete connected property or fence lines. In many cases the boundary lines were interpreted among the property and fence lines with our best judgment. The airport general information derived from FAA Airport Master Record and Reports with their URL are included in the attribute table.Airport boundary data is intended for general reference and does not represent official airport property boundary determinations.

Annual (1986-2020) land-use/land cover maps at 30-meter resolution of the Tucson metropolitan area, Arizona and the greater Santa Cruz Watershed including Nogales, Sonora, Mexico. Maps were created using a combination of Landsat imagery, derived transformation and indices, texture analysis and other ancillary data fed to a Random Forest classifier in Google Earth Engine. The maps contain 13 classes based on the National Land Cover Classification scheme and modified to reflect local land cover types. Data are presented as a stacked, multi-band raster with one "band" for each year (Band 1 = 1986, Band 2 = 1987 and so on). Note that the year 2012 was left out of our time series because of lack of quality Landsat data. A color file (.clr) is included that can be imported to match the color of the National Land Cover Classification scheme. This data release also contains two JavaScript files with the Google Earth Engine code developed for pre-processing Landsat imagery and for image classification, and a zip folder "Accuracy Data" with five excel files: 1) Accuracy Statistics describing overall accuracy for each LULC year, 2) Confusion Matrices for each LULC year, 3) Land Cover Evolution - changes in pixel count for each class per year, 4) LULC Change Matrix - to and from class changes over the period, and 5) Variable Importance - results of the Random Forest Classification.

Land use consists of reading and interpreting municipal land cover through the use of photo-cartographic documentation (orthophoto, cadastre, etc.) and software for cartography (Google Maps, Maps Street View, Google Earth, etc.).

It represents a polygonisation of the municipal soil in which each polygon is assigned a nomenclature according to the international standard of codification of the European model CORINE Land Cover.

The land use has been carried out by the Department of Systems, distributed IT and territory in collaboration with the Project Revision of the PRG.
It is constantly updated and given the complexity of the data (more than 12000 polygons) are welcome reports of any inaccuracies or improvements by writing to infogis@comune.trento.it

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.

Web App. Parcel map displaying Age of Housing, Residential Appraised Value and Land Use in St. Louis County, Missouri. Link to Metadata.

This service provides vector polygon dataset defining the official boundaries of the 100 counties within North Carolina as well as the boundaries between North Carolina and the states which border North Carolina.The North Carolina county polygon boundary service provides location information for North Carolina State and County Boundary lines derived from the best available survey and/or Geographic Information System (GIS) data. Sources for information are the North Carolina Geodetic Survey (NCGS), NC Department of Transportation (NCDOT), United States Geological Survey (USGS), and field surveys conducted by licensed surveyors in North Carolina and neighboring states that have been approved and recorded in their respective counties. Some boundaries cannot be surveyed in cases where boundaries are coincident with river centers. North Carolina Geodetic Survey assists counties on a cooperative basis (NC General Statute 153A-18) in defining and monumenting the location of uncertain or disputed boundaries as established by law. Some counties have completed boundary surveys for at least a portion of their county boundary. However, the majority of county boundaries have not been surveyed and are represented by the best currently available data from GIS sources, including NCDOT county maps (which originally came from the USGS) and updated county parcel maps.This data is updated annually, first quarter (usually in February).MetadataThe metadata for the contained layer of the NCDOT County Boundaries Service is available through the following link:County Boundaries PolygonPoint of Contact North Carolina Department of Information Technology -Transportation, GIS UnitGIS Data and Services ConsultantContact information:gishelp@ncdot.govCentury Center – Building B1020 Birch Ridge DriveRaleigh, NC 27610Hours of service: 9:00am - 5:00pm Monday – FridayContact instructions: Please send an email with any issues, questions, or comments regarding the County Boundaries data. If it is an immediate need, please indicate as such in the subject line in an email.NCDOT GIS Unit GO! NC Product Team

Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.1 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE).

Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder called "Sentinel2LULC_GeoTiff.zip" contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder called "Sentinel2LULC_JPEG.zip" contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder called "Sentinel2LULC_CSV.zip" includes 29 zip-compressed CSV files with as many rows as provided images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames):

• Land Cover Class ID: is the identification number of each LULC class
• Land Cover Class Short Name: is the short name of each LULC class
• Image ID: is the identification number of each image within its corresponding LULC class
• Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products
• GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image
• Latitude: is the latitude of the center point of each image
• Longitude: is the longitude of the center point of each image
• Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes
• Administrative Department Level1: is the administrative level 1 name to which each image belongs
• Administrative Department Level2: is the administrative level 2 name to which each image belongs
• Locality: is the name of the locality to which each image belongs
• Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when compositing and exporting its corresponding image tile

For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files:

• A CSV file that contains all exported images for this class
• A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images".

To clearly state the geographical coverage of images available in this dataset, we included in the version v2.1, a compressed folder called "Geographic_Representativeness.zip". This zip-compressed folder contains a csv file for each LULC class that provides the complete list of countries represented in that class. Each csv file has two columns, the first one gives the country code and the second one gives the number of images provided in that country for that LULC class. In addition to these 29 csv files, we provided another csv file that maps each ISO Alpha-2 country code to its original full country name.

© Sentinel2GlobalLULC Dataset by Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)

In late 1996, the Dept of Conservation (DOC) surveyed state and federal agencies about the county boundary coverage they used. As a result, DOC adopted the 1:24,000 (24K) scale U.S. Bureau of Reclamation (USBR) dataset (USGS source) for their Farmland Mapping and Monitoring Program (FMMP) but with several modifications. Detailed documentation of these changes is provided by FMMP and included in the lineage section of the metadata.A dataset was made available (approximately 2004) through CALFIRE - FRAP and the California Spatial Information Library (CaSIL), with additional updates throughout subsequent years. More recently, an effort was made to improve the coastal linework by using the previous interior linework from the 24k data, but replacing the coastal linework based on NOAA's ERMA coastal dataset (which used NAIP 2010). In this dataset, all bays (plus bay islands and constructed features) are merged into the mainland, and coastal features (such as islands and constructed features) are not included, with the exception of the Channel Islands which ARE included.This service represents the latest released version, and is updated when new versions are released. As of June, 2019 it represents cnty19_1.

The Sample can drive classification algorithms, thus is a prerequisite for accurate classification. Coastal areas are located in the transitional zone between land and sea, requiring more samples to describe diverse land covers. However, there are scarce studies sharing their sample datasets, leading to a repeat of the time-consuming and laborious sampling procedure. To alleviate the problem, we share a sample set with a total of 16,444 sample points derived from a study of mapping mangroves of China. The sample set contains a total of 10 categories, which are described as follows. 1) The mangroves refer to “true mangroves” (excluding the associate mangrove species). In sampling mangroves, we used the data from the China Mangrove Conservation Network (CMCN, http://www.china-mangrove.org/), a non-governmental organization aiming to promote mangrove ecosystems. The CMCN provides an interactive map that can be annotated by volunteers with text or photos to record mangrove status at a location. Although the locations were shifted due to coordinate system differences and positioning errors, mangroves could be found around the mangrove locations depicted by the CMCN’s map on Google Earth images. There is a total of 1887 mangrove samples. 2) The cropland is dominated by paddy rice. We collected a total 1383 points according to its neat arrangement based on Google Earth images. 3) Coastal forests neighboring mangroves are mostly salt-tolerant, such as Cocos nucifera Linn., Hibiscus tiliaceus Linn., and Cerbera manghas Linn. We collected a total 1158 samples according to their distance to the shoreline based on Google Earth images. 4) Terrestrial forests are forests far from the shoreline, and are intolerant to salt. By visual inspection on Google Earth, we sampled 1269 points based on their appearances and distances to the shoreline. 5) For the grass category, we collected 1282 samples by visual judgement on Google Earth. 6) Saltmarsh, dominated by Spartina alterniflora, covering large areas of tidal flats in China. We collected 2065 samples according to Google Earth images. 7) The tidal flats category was represented by 1517 samples, which were sampled using the most recent global tidal flat map for 2014–2016 and were visually corrected. 8) The “sand or rock” category refers to sandy and pebble beaches or rocky coasts exposed to air, which are not habitats of mangroves. We collected 1622 samples on Google Earth based on visual inspection. 9) For the permanent water category, samples were first randomly sampled from a threshold result of NDWI (> 0.2), and then were visually corrected. A total of 2056 samples were obtained. 10) As to the artificial impervious surfaces category, we randomly sampled from a threshold result corresponding to normal difference built-up index (NDBI) (> 0.1), and corrected them based on Google Earth. The artificial impervious surface category was represented by 2205 samples. This sample dataset covers the low-altitude coastal area of five Provinces (Hainan, Guangdong, Fujian, Zhejiang, and Taiwan), one Autonomous region (Guangxi), and two Special Administrative Regions (Macau and Hong Kong) (see “study_area.shp” in the zip for details). It can be used to train models for coastal land cover classification, and to evaluate classification results. In addition to mangroves, it can also be used in identifying tidal flats, mapping salt marsh, extracting water bodies, and other related applications.Compared with the V1 version, we added a validation dataset for mangrove maps (Mangrove map validation dataset.rar), and thus can evaluate mangrove maps under the same dataset, which benefit the comparison of different mangrove maps. The validation dataset contains 10 shp files, in which each shp file contains 600 mangrove samples (cls_new field = 1) and 600 non-mangrove samples (cls_new field = 0).Compared with the V2 version, we added two classes of forest near water and grass near water, in addition to suppress the prevalent misclassified patches due to the spectral similarity between mangroves and those classes.

The Unpublished Digital Geologic Map of Bering Land Bridge National Preserve and Vicinity, Alaska is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (bela_geology.gdb), a 10.1 ArcMap (.MXD) map document (bela_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (bela_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (bela_gis_readme.pdf). Please read the bela_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. 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 (bela_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/bela/bela_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:500,000 and United States National Map Accuracy Standards features are within (horizontally) 254 meters or 833.3 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 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. 2.2. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone AD_1983_Alaska_AlbersN, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Bering Land Bridge National Preserve.

This repository provides extended documentation, code, and updated links to access the Soil Landscapes of the United States (SOLUS) 100-meter soil property maps. It provides supporting materials for a peer reviewed paper (Nauman et al., Soil Science Society of America Journal, 1–20. https://doi.org/10.1002/saj2.20769) documenting the theory and novel application of hybridized legacy training datasets used to inform the machine learning models used to create the new soil property maps presented here. The SOLUS dataset includes 20 different soil properties (listed below) with most properties predicted for seven standard depths (0, 5, 15, 30, 60, 100, and 150 cm). Further details on these properties and all included files are available in the README.docx document. Also included is a git repository formatted as a hybrid R package that includes all code used to create the soil property maps. All SOLUS100 mapping layers are available as cloud optimized geotiffs at: https://storage.googleapis.com/solus100pub/index.html Metadata: https://storage.googleapis.com/solus100pub/SOLUS100_metadata_pub.html List of files at this URL are listed at: https://storage.googleapis.com/solus100pub/Final_Layer_Table_20231215.csv Note that many of the raster files are scaled by multipliers of 10, 100, or 1000 to store the values as integers to decrease file size. The ‘scalar’ field of the file list table (Final_Layer_Table_20231215.csv) files provide those values. The actual rasters must be divided by the scalars to get the actual units of the properties. To download files, simply concatenate the google API URL with a forward slash and the file name listed in the table into a browser (e.g. EC at 0 cm would be https://storage.googleapis.com/solus100pub/ec_15_cm_p.tif). To automate downloads, a loop in python, R or your language of choice that builds file download urls from the file list in the csv can be implemented. Alternatively, some GIS programs (e.g. QGIS) will let you visualize and interact with the files without downloading the files by entering the URL. All raster environmental covariates used in mapping are available here: https://storage.googleapis.com/cov100m/index.html Properties included in SOLUS100:

Bulk density (oven dry) Calcium carbonate Cation Exchange Capacity (pH 7) Clay Coarse sand Electrical Conductivity (sat. paste) Effective cation exchange capacity Fine sand Gypsum (in

The global Mobile Mapping Systems market is experiencing robust growth, projected to reach $20,740 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.0% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of autonomous vehicles and the need for highly accurate and detailed maps for navigation and advanced driver-assistance systems (ADAS) are significantly fueling market demand. Furthermore, the growth of smart cities initiatives, requiring comprehensive infrastructure mapping for efficient urban planning and management, is a major contributor. Government and public sector investments in infrastructure projects, coupled with rising demand for location-based services across various sectors like transportation and logistics, real estate, and video entertainment, are also boosting market growth. The shift towards cloud-based solutions and the integration of advanced technologies like LiDAR and GPS are further enhancing the capabilities and efficiency of mobile mapping systems, attracting broader adoption. The market is segmented by system type (Direct Mobile Mapping System and Backpack Mobile Mapping System) and application (Automobile, Transportation & Logistics, Government & Public Sector, Video Entertainment, Real Estate, Travel & Hospitality, and Other). While the Automobile sector currently holds a significant market share, the Government & Public Sector and Transportation & Logistics segments are expected to witness substantial growth due to increasing infrastructure development and the need for efficient logistics management. Competition in the market is intense, with major players including Ericsson, Microsoft, Apple, Google, and TomTom continuously innovating and expanding their product offerings to cater to the evolving demands of various industries. The market's geographical distribution is diverse, with North America and Europe currently leading in adoption, followed by the Asia-Pacific region, which is expected to demonstrate significant growth potential in the coming years driven by economic development and increasing urbanization. This comprehensive report analyzes the burgeoning Mobile Mapping Systems (MMS) market, projected to reach $15 billion by 2030. It delves into key trends, competitive landscapes, and growth drivers, providing invaluable insights for businesses and investors alike. The report leverages extensive market research and data analysis to provide actionable intelligence on this rapidly evolving technology. Keywords: Mobile Mapping, LiDAR, 3D Mapping, GIS, Location-Based Services, Autonomous Vehicles, Mapping Technology, Geospatial Data.

This resource is a repository of the map products for the Annual Irrigation Maps - Republican River Basin (AIM-RRB) dataset produced in Deines et al. 2017. It also provides the training and test point datasets used in the development and evaluation of the classifier algorithm. The maps cover a 141,603 km2 area in the northern High Plains Aquifer in the United States centered on the Republican River Basin, which overlies portions of Colorado, Kansas, and Nebraska. AIM-RRB provides annual irrigation maps for 18 years (1999-2016). Please see Deines et al. 2017 for full details.

Preferred citation: Deines, J.M., A.D. Kendall, and D.W. Hyndman. 2017. Annual irrigation dynamics in the US Northern High Plains derived from Landsat satellite data. Geophysical Research Letters. DOI: 10.1002/2017GL074071

Map Metadata Map products are projected in EPSG:5070 - CONUS Albers NAD83 Raster value key: 0 = Not irrigated 1 = Irrigated 254 = NoData, masked by urban, water, forest, or wetland land used based on the National Land Cover Dataset (NLCD) 255 = NoData, outside of study boundary

Training and test point data sets supply coordinates in latitude/longitude (WGS84). Column descriptions for each file can be found below in the "File Metadata" tab when the respective file is selected in the content window.

Corresponding author: Jillian Deines, jillian.deines@gmail.com