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This map interface (MapSeries) shows the current and forecast weather for areas around the country. This display is an experimental method for both maintaining situational awareness, but also can be used as a tool for use in briefing NWS Partners.This interface is planned to be embedded at this web-facing location: https://www.weather.gov/gis/

State Authority for Geospatial Information (ASIG) was established in 2013, according to law 72/2012 "On the organization and functioning of the national infrastructure of geospatial information in the Republic of Albania".

ASIG objectives:
Creation of geodetic framework to European standards to enable the support of a unique map of the entire territory of the Republic of Albania
Establishment of national infrastructure geospatial data in the Republic of Albania, through Geoportal where everyone can access to geospatial data that possesses Albanian state.
Designing and development in the field of geo-information standards and their implementation in institutions, whether manufacturer or the geo Update.

The National Geoportal is a "door" that allows professional users, and interested public, to watch and access in a very simple way geospatial data and Web services available by various Government institutions. This Geoportal is a very important step in the framework of the Open Governance (OGP), which basically has the policy of open data to citizens, providing services away bureaucracy and close quality. This Geoportal serves as a "bridge" for interagency cooperation within the efficiency in the civil service. It is also a necessary step in the establishment of geospatial Data Infrastructure (NSDI), a priority under-Goverment that brings Albania closer to the European Digital Agenda.

National Geoportal is in the initial phase of it’s structuring, performing a harmonization of geospatial data in order for them to be as complete, accurate and up to date. For the users concerned about these geospatial information (citizens, scholars, etc..), we suggest that the data obtained from the Geoportal, get firstly verified and confirmed in the relevant institutions, in order to have a final information as accurate and safe.

A special thanks is dedicated to the aid of the Norwegian Government, through the Norwegian Mapping and Cadastral Authority "Statens KARTVERK", which is providing an important contribution for the State Authority for Geospatial Information, to take its initial guidelines based on best European experiences.

Historical Points in the Geographic Names Information System (GNIS)This feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Geological Survey, displays historical points from the Geographic Names Information System (GNIS). Per USGS, “the Geographic Names Information System (GNIS) is the federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types.”Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (geonames) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: Geographic Names Information System (GNIS) - USGS National Map Downloadable Data CollectionGeoplatform: Geographic Names Information System (GNIS) - USGS National Map Downloadable Data CollectionFor more information, please visit: U.S. Board on Geographic NamesFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Cultural Resources Theme Community. Per the Federal Geospatial Data Committee (FGDC), Cultural Resources are defined as "features and characteristics of a collection of places of significance in history, architecture, engineering, or society. Includes National Monuments and Icons."For other NGDA Content: Esri Federal Datasets

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector point of the location of all currently inventoried and mappable fires occurring between calendar year 1984 and the current MTBS release for CONUS, Alaska, Hawaii and Puerto Rico. Please visit https://mtbs.gov/announcements to determine the current release. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires were not discernable from available imagery. The point location represents the geographic centroid for the _BURN_AREA_BOUNDARY polygon(s) associated with each fire. MetadataThis 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 OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

Earth Data Analysis Center (EDAC) at The University of New Mexico (UNM) develops, manages, and enhances the New Mexico Resource Geographic Information System (RGIS) Program and Clearinghouse. Nationally, NM RGIS is among the largest of state-based programs for digital geospatial data and information and continues to add to its data offerings, services, and technology.

The RGIS Program mission is to develop and expand geographic information and use of GIS technology, creating a comprehensive GIS resource for state and local governments, educational institutions, nonprofit organizations, and private businesses; to promote geospatial information and GIS technology as primary analytical tools for decision makers and researchers; and to provide a central Clearinghouse to avoid duplication and improve information transfer efficiency.

As a repository for digital geospatial data acquired from local and national public agencies and data created expressly for RGIS, the clearinghouse serves as a major hub in New Mexico’s network for inter-agency and intergovernmental coordination, data sharing, information transfer, and electronic communication. Data sets available for download include political and administrative boundaries, place names and locations, census data (current and historical), 30 years of digital orthophotography, 80 years of historic aerial photography, satellite imagery, elevation data, transportation data, wildfire boundaries and natural resource data.

Shows links to Kansas county websites, GIS websites, and parcel search websites where available. Some parcel search websites are password protected. Data is updated as new or corrected information is found or reported. Please report any updated or erroneous links to DASC at kgs.ku.edu.The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/

The FS National Forests Dataset (US Forest Service Proclaimed Forests) is a depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.Metadata and Downloads - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Original+Proclaimed+National+ForestsThis 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 OGC WMS CSV Shapefile GeoJSON KML OGC WFS OGC WMS For complete information, please visit https://data.gov.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled land use classes for each year. See additional information about land use in the Entity_and_Attribute_Information 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 annual Landsat and Sentinel 2 composites, 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). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. 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, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.This 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.

Link to State of South Dakota GIS Data.

[Metadata] Description: Streets for the island of Oahu only as of August 2024.

The Hennepin GIS Hub is a public portal into the county map gallery, open data site and other useful resources.

TY2023CY24 Collection Information - Unindexed.

The Division of Forestry Geographic Information Systems home page provides information on GIS information, Spatial Data, GIS Web Applications depicting current wild land fire information and forest resource information for the entire state of Alaska.

The map provides details on parcels including dimensions and location. Some geospatial data and other related materials, including derived maps, are intended for property tax assessment purposes only. The geospatial information is based upon recorded deeds, plans, and other public sources. These primary sources should be consulted to verify the information. Due to conflicts, errors, and omissions in the primary sources, the information and other mapping products should be considered to be a representation of the editors judgment, based upon the available evidence. The geospatial data and related materials are not legal evidence of size, shape, location, or ownership of real estate, roads, or municipal boundaries.

The map provides details on parcels including dimensions and location. Some geospatial data and other related materials, including derived maps, are intended for property tax assessment purposes only. The geospatial information is based upon recorded deeds, plans, and other public sources. These primary sources should be consulted to verify the information. Due to conflicts, errors, and omissions in the primary sources, the information and other mapping products should be considered to be a representation of the editors judgment, based upon the available evidence. The geospatial data and related materials are not legal evidence of size, shape, location, or ownership of real estate, roads, or municipal boundaries.

The map provides details on parcels including dimensions and location. Some geospatial data and other related materials, including derived maps, are intended for property tax assessment purposes only. The geospatial information is based upon recorded deeds, plans, and other public sources. These primary sources should be consulted to verify the information. Due to conflicts, errors, and omissions in the primary sources, the information and other mapping products should be considered to be a representation of the editors judgment, based upon the available evidence. The geospatial data and related materials are not legal evidence of size, shape, location, or ownership of real estate, roads, or municipal boundaries.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information 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 annual Landsat and Sentinel 2 composites, 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). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. 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, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.This 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.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual fast loss into a single layer showing the year LCMS detected fast loss with the highest model confidence. 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 annual Landsat and Sentinel 2 composites, 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). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. 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, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.This 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.

OIT-GIS Team bios and information