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.

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 The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations.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.

This dataset overlays a grid on the County to assist in locating a parcel. The grid squares are 3,500 by 4,500 square feet. The data was derived from original MAPINDX: Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography. Tiles are numbered in a clockwise spiral fashion starting with #1 at the point in downtown Pittsburgh. Each tile contains 16 Blocks. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.

If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (https://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (https://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.

Category: Civic Vitality and Governance

Organization: Allegheny County

Department: Geographic Information Systems Group; Department of Information Technology

Temporal Coverage: 2002

Data Notes:

Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot

Development Notes: none

Other: none

Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/1yyJ_RKU2brFBYU8mh8ZIr6P_Uy3iUWOQL2ZYBv398LY/edit?usp=sharing)

Frequency - Data Change: Multiple times per hour

Frequency - Publishing: Daily

Data Steward Name: Eli Thomas

Data Steward Email: gishelp@alleghenycounty.us

The Digital Geologic-GIS Map of Wilson's Creek National Battlefield and Vicinity, Missouri 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 (wicr_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 (wicr_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 (wicr_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 (wicr_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (wicr_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 (wicr_geology_metadata_faq.pdf). Please read the wicr_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: Missouri Department of Natural Resources, Division of Geology and Land 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 (wicr_geology_metadata.txt or wicr_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).

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.

While the Waimakariri District Council has taken all reasonable care in providing correct information, all information should be considered as being illustrative and indicative only. Your use of this information is entirely at your own risk. You should independently verify the accuracy of any information before taking any action in reliance upon it.Read full disclaimer here.Abstract:This layer is derived from current primary parcels, as per the NZ Parcels layer on the LINZ Data Service, joined to data on matching current/future properties in WDC’S rating database.Note, this dataset includes a boundary for the primary property address only (as identified in WDC’s rating database) and does not include a boundary for all addresses that may exist on a property.Other information:Addresses:The address datasets contain street number, street name and suburb for physical addresses in Waimakariri.There can be multiple addresses on a property and an example of these are granny flats, farm cottages etc.Click here to view Address Boundary LayerClick here to view Address Point LayerUpdate Frequency:DailyPoint of Contact:Waimakariri District CouncilLineage:Data has been compiled from a number of sources and its accuracy may vary (e.g. Field Verification, Deposited Plans, AsBuilt plans and forms, sketches, aerial photo, Google Street View). There may be delays before data is updated to reflect changes in an area.

This layer contains detailed outlines of Maryland counties. The Maryland land county boundaries were built using political county boundaries and the National Hydrology Data (NHD). Land boundaries are a key geographic featue in our mapping process.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Last Updated: UnknownFeature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_PhysicalBoundaries/FeatureServer/0

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.

Identifies location, the type of sport played and condition, age and other details about the recreational facility. Data contains approximately 80 types of sport including private gyms and fitness centres. The location of facilities was checked using a range of spatial data including current aerial photos, LGA and property boundaries as well as Google maps. If feasible they were geocoded via Victorian Mapping Address System (VMAS).

The Global Land Survey (GLS) 1975 is a global collection of imagery from the Landsat Multispectral Scanner (MSS). Most scenes were acquired by Landsat 1-3 in 1972-1983. A few gaps in the Landsat 1-3 data have been filled with scenes acquired by Landsat 4-5 during the years 1982-1987. These data …

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. http://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The data set includes the Romanian land cover layer of the Romania-Bulgaria cross-border area (Mehedinți, Dolj, Olt, Teleorman, Giurgiu, Calarasi, Constanta), developed within the project “Common strategy for territorial development of the cross-border area Romania-Bulgaria”, code MIS-ETC 171, financed from the Romania-Bulgaria Cross Border Cooperation Programme 2007-2013.

The data set is published in the coordinate system WGS 84/UTM zones 35N (to be compatible with the similar data set on the Bulgarian side).

The data set is in line with the conceptual framework described in the Land Cover Data Specifications for the implementation of the INSPIRE Directive (version 3.0). The information layer was developed on the basis of a methodology developed within the project, which was carried out in the following way: Analysis and harmonisation of the land cover classification system; Obtaining and processing the reference data listed below; Checking and validating the quality of the spatial data produced;

More information and reference data sets were analysed in the development of the data set: LPIS (Land Parcel Identification System), spatial data set owned by the Agency for Payments and Interventions in Agriculture — Orthophotos from 2005, held by the National Agency for Cadastre and Real Estate Advertising RapidEye images provided by the European Space Agency (Copernicus CORE 01 data set), obtained in 2011 @-@ 2012, mainly in March and October, with an accuracy of 5 m and 5 spectral bands — SPOT images provided by the European Space Agency (Copernicus CORE 03 data set), obtained in 2011 @-@ 2013, with an accuracy of 5 m, including infrared Information obtained through the use of Google Earth and StreetView — Field check — CORINE Land Cover data layer (CLC) Land cover data set resulting from the implementation of the FAO TCP project/ROM/2801, financed by the United Nations Food and Agriculture Organisation Other relevant data sources (e.g.: topographic maps, forestry plans, cadastral plans)

The dataset on the land cover layer is one of the common resources needed to develop the common territorial development strategy and monitor the impact for the Romania-Bulgaria cross-border area.

To use the data set, it is necessary to download all files: .shp,.shx,.dbf,.prj,.cpg.

The data set can also be downloaded in the.zip archive from http://spatial.mdrap.ro.

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

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 …

The Land Cover dataset demarcates 14 land cover types by area; such as Residential, Commercial, Industrial, Forest, Agriculture, etc. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Geography Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 1994 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: The dataset was created by Chester Environmental through combined image processing and GIS analysis of Landsat TM imagery of October 2, 1992, existing aerial photography, hardcopy and digital mapping sources and Census Bureau demographic data. The original dataset was created in 1993, then updated by Chester in 1994. Other: none Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/1VfUflfki42mpLSkr1R-up_OXGD3mHnv8tqeXf6XS9O0/edit?usp=sharing) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

This data is from the master's thesis titled- Repeatable methods for classification of alien and native vegetation in the Montane grasslands. Figure 2.6 in the thesis is the result of this dataset. The dataset includes a shapefile of the South African boundary; a GeoTIFF generated from Google Earth Engine containing the classified map of the study area; as well as a QGIS file containing the final map produced for Figure 2.6.Date of data collection: February 2020.Location of data collection: Blyde River Conservancy and its surrounds, in Mpumalanga/Limpopo Provinces, South Africa.

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.

"*_albert.tif" are projected files via a proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".

CLCD in 2023 is now available.

1. Given that the USGS no longer maintains the Landsat Collection 1 data, we are now using the Collection 2 SR data to update the CLCD.

2. All files in this version have been exported as Cloud Optimized GeoTIFF for more efficient processing on the cloud. Please check here for more details.

3. Internal overviews and color tables are built into each file to speed up software loading and rendering.

This data set provides land cover and land use(LCLU) classification product at 30-m spatial resolution for Puerto Rico in 2010. The LCLU data was derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data around the year of 2010. The ground reference data were acquired by historical LCLU map, field trip surveys, and visual interpretation of high spatial resolution imagery from Google Earth and aerial photos. The classification model was created with Random Forest classifier. The data was produced by the Department of Environmental Sciences, University of Puerto Rico-Rio Piedras. Please participate in the data usage survey and give some suggestion (https://goo.gl/forms/JshGAXNoqSO3NNxw1), so that we can improve the data.

A book of one km2 sampling cell habitat cover maps generated using the OSi PRIME 2 dataset (OSi 2022a) and enhanced using OSi orthoimagery and Google Street View interpretation. The book includes two sets of 15 sampling cells: 1) a baseline set generated using OSi Ortho 2000 imagery [reference year: 2000], and 2) a second, updated set generated using OSi Digital Globe imagery reference year: 2013

References: ​​OSi, 2022a. PRIME2 Data, The National Map. Ordinance Survey Ireland. ​〈https://osi.ie/about/future-developments/the-national-map/〉. (Accessed 1 May 2021).​ ​​OSi, 2022b. Aerial Imagery Maps and Data. Ordinance Survey Ireland. ​〈https://osi.ie/products/professional-mapping/osi-aerial-imagery/〉. (Accessed 1 May 2021).​