USGS task order 140G0218F0420 required Winter, 2018/Spring, 2019 LiDAR surveys to be collected over 32,562 square miles covering part or all of 82 counties in Georgia and 3 partial counties in South Carolina in support of the State of Georgia and the USGS 3DEP program. Aerial LiDAR data for this task order was planned, acquired, processed and produced at an aggregate nominal pulse spacing (ANPS...

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

Elevation datasets in New Jersey have been collected over several years as several discrete projects. Each project covers a geographic area, which is a subsection of the entire state, and has differing specifications based on the available technology at the time and project budget. The geographic extent of one project may overlap that of a neighboring project. Each of the 18 projects contains deliverable products such as LAS (Lidar point cloud) files, unclassified/classified, tiled to cover project area; relevant metadata records or documents, most adhering to the Federal Geographic Data Committee’s (FGDC) Content Standard for Digital Geospatial Metadata (CSDGM); tiling index feature class or shapefile; flights lines feature class or shapefile; Digital Elevation Model in image format or Esri grid format; other derivative data products such as contour lines feature class or shapefile.

This is compilation of Maine DEMs generated from lidar as a hillshade for MGS web applications. Not all view scales have been created.Users looking for lidar data and/or data derivatives should contact, in order:United States Interagency Elevation Inventory (USIEI): https://coast.noaa.gov/inventory/Maine GeoLibrary Elevation Discovery and Download: https://www1.maine.gov/geolib/ediscovery/site/landing.htmlNational Map (USGS) ftp: ftp://rockyftp.cr.usgs.gov/vdelivery/Datasets/Staged/

Montgomery and Prince George's counties project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (HARN), State Plane, Feet and vertical datum of NAVD88 (GEOID12B), Feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 1,413 individual 4000 ft x 6000 ft tiles, as tiled Intensity Rasters, and as tiled bare-earth DEM; all tiled to the same 4000 ft x 6000 ft schema. Ground Conditions: Lidar was collected in early to mid 2018, while no snow was on the ground and rivers were at or below normal levels. Sensor errors in mulitple locations were identified in processing, additonal acquisition on 7/20/2018 was completed to remedy the errors. 2016 lidar data was used with approval in a single area of restricted airspace. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc. established a total of 23 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 79 independent accuracy check points in Open Terrain and Urban landcovers were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/lidar/rest/services/Montgomery/MD_montgomery_dem_ft/ImageServer

This map service presents spatial information about the U.S. Interagency Elevation Inventory services across the United States and Territories in the Web Mercator projection. The service was developed as part of a collaboration between the National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management, the U. S. Geological Survey (USGS), the Federal Emergency Management Agency (FEMA), U.S. Department of Agriculture (USDA), the U.S. Army Corps of Engineers (USACE) and the National Park Service (NPS). The U.S. Interagency Elevation Inventory contains data and information from a variety of sources, including non-NOAA data. NOAA provides the information "as-is" and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov). These layers show the coverage of the best available elevation data by type. Information about each data set and either a download link or Point of Contact is provided. Metadata for the U.S. Interagency Elevation Inventory is available at (https://coast.noaa.gov/htdata/Metadata/MetadataNationalElevationInventory.html). The Office for Coastal Management will make every effort to provide continual access to this service but it may need to be taken down during routine IT maintenance or in case of an emergency. If you plan to ingest this service into your own application and would like to be informed about planned and unplanned service outages or changes to existing services, please register for our Data Services Newsletter (https://coast.noaa.gov/digitalcoast/publications/subscribe). We will do our best to provide you with information about any status changes to our map services.

Montgomery and Prince George's counties project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (HARN), State Plane, Feet and vertical datum of NAVD88 (GEOID12B), Feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 1,413 individual 4000 ft x 6000 ft tiles, as tiled Intensity Rasters, and as tiled bare-earth DEM; all tiled to the same 4000 ft x 6000 ft schema. Ground Conditions: Lidar was collected in early to mid 2018, while no snow was on the ground and rivers were at or below normal levels. Sensor errors in mulitple locations were identified in processing, additonal acquisition on 7/20/2018 was completed to remedy the errors. 2016 lidar data was used with approval in a single area of restricted airspace. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc. established a total of 23 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 79 independent accuracy check points in Open Terrain and Urban landcovers were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/lidar/rest/services/Montgomery/MD_montgomery_slope_m/ImageServer

Geographic Extent: SANDY_Restoration_DE_MD_QL2 Area of Interest covers approximately 3.096 square miles. Lot #5 contains the full project area Dataset Description: The SANDY_Restoration_DE_MD_QL2 project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1. The data was developed based on a horizontal projection/datum of State Plane Zone Maryland (1900), NAD83, feet and vertical datum of NAVD1988 (GEOID12A), feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.2 Files formatted to 3842 individual 1500m x 1500m tiles, and corresponding Intensity Images and Bare Earth DEMs tiled to the same 1500m x 1500m schema, and Breaklines in ESRI shapefile format. Ground Conditions: LiDAR was collected in Winter 2013 / Spring 2014, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 78 QA control points and 99 Land Cover control points that were used to calibrate the LIDAR to known ground locations established throughout the SANDY_Restoration_DE_MD_QL2 project area.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/lidar/rest/services/Caroline/MD_caroline_aspect_m/ImageServer

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://coast.noaa.gov/slr.

 This metadata record describes the North Carolina, Southern 1 digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. This DEM includes the best available lidar known to exist at the time of DEM creation that met project specifications. This DEM includes data for Bladen, Brunswick, Columbus, New Hanover, and Pender Counties.

 The DEM was produced from the following lidar data sets:
 1. 2014 NGS Coastal Mapping Program Topobathy Lidar: Post-Sandy Atlantic Seaboard
 2. 2014 NC Statewide Lidar - Phase 2

 The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

The global 3D mapping and modeling market is expected to grow significantly in the next few years as demand increases for detailed and accurate representations of physical environments in three-dimensional space. Estimated to be valued at USD 38.62 billion in the year 2025, the market was expected to grow at a CAGR of 14.5% from 2025 to 2033 and was estimated to reach an amount of USD 90.26 billion by the end of 2033. The high growth rate is because of improvement in advanced technologies with the development of high-resolution sensors and methods of photogrammetry that make possible higher-resolution realistic and immersive 3D models.Key trends in the market are the adoption of virtual and augmented reality (VR/AR) applications, 3D mapping with smart city infrastructure, and increased architecture, engineering, and construction utilization of 3D models. Other factors are driving the growing adoption of cloud-based 3D mapping and modeling solutions. The solutions promise scalability, cost-effectiveness, and easy access to 3D data, thus appealing to business and organizations of all sizes. Recent developments include: Jun 2023: Nomoko (Switzerland), a leading provider of real-world 3D data technology, announced that it has joined the Overture Maps Foundation, a non-profit organization committed to fostering collaboration and innovation in the geospatial domain. Nomoko will collaborate with Meta, Amazon Web Services (AWS), TomTom, and Microsoft, to create interoperable, accessible 3D datasets, leveraging its real-world 3D modeling capabilities., May 2023: The Sanborn Map Company (Sanborn), an authority in 3D models, announced the development of a powerful new tool, the Digital Twin Base Map. This innovative technology sets a new standard for urban analysis, implementation of Digital Cities, navigation, and planning with a fundamental transformation from a 2D map to a 3D environment. The Digital Twin Base Map is a high-resolution 3D map providing unprecedented detail and accuracy., Feb 2023: Bluesky Geospatial launched the MetroVista, a 3D aerial mapping program in the USA. The service employs a hybrid imaging-Lidar airborne sensor to capture highly detailed 3D data, including 360-degree views of buildings and street-level features, in urban areas to create digital twins, visualizations, and simulations., Feb 2023: Esri, a leading global provider of geographic information system (GIS), location intelligence, and mapping solutions, released new ArcGIS Reality Software to capture the world in 3D. ArcGIS Reality enables site, city, and country-wide 3D mapping for digital twins. These 3D models and high-resolution maps allow organizations to analyze and interact with a digital world, accurately showing their locations and situations., Jan 2023: Strava, a subscription-based fitness platform, announced the acquisition of FATMAP, a 3D mapping platform, to integrate into its app. The acquisition adds FATMAP's mountain-focused maps to Strava's platform, combining with the data already within Strava's products, including city and suburban areas for runners and other fitness enthusiasts., Jan 2023: The 3D mapping platform FATMAP is acquired by Strava. FATMAP applies the concept of 3D visualization specifically for people who like mountain sports like skiing and hiking., Jan 2022: GeoScience Limited (the UK) announced receiving funding from Deep Digital Cornwall (DDC) to develop a new digital heat flow map. The DDC project has received grant funding from the European Regional Development Fund. This study aims to model the heat flow in the region's shallower geothermal resources to promote its utilization in low-carbon heating. GeoScience Ltd wants to create a more robust 3D model of the Cornwall subsurface temperature through additional boreholes and more sophisticated modeling techniques., Aug 2022: In order to create and explore the system's possibilities, CGTrader worked with the online retailer of dietary supplements Hello100. The system has the ability to scale up the generation of more models, and it has enhanced and improved Hello100's appearance on Amazon Marketplace.. Key drivers for this market are: The demand for 3D maps and models is growing rapidly across various industries, including architecture, engineering, and construction (AEC), manufacturing, transportation, and healthcare. Advances in hardware, software, and data acquisition techniques are making it possible to create more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations.

. Potential restraints include: The acquisition and processing of 3D data can be expensive, especially for large-scale projects. There is a lack of standardization in the 3D mapping modeling industry, which can make it difficult to share and exchange data between different software and systems. There is a shortage of skilled professionals who are able to create and use 3D maps and models effectively.. Notable trends are: 3D mapping and modeling technologies are becoming essential for a wide range of applications, including urban planning, architecture, construction, environmental management, and gaming. Advancements in hardware, software, and data acquisition techniques are enabling the creation of more accurate, detailed, and realistic 3D maps and models. Digital twins, which are virtual representations of real-world assets or systems, are driving the demand for 3D mapping and modeling technologies for the creation of accurate and up-to-date digital representations..

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://coast.noaa.gov/slr.

 This metadata record describes the New Jersey, Southern digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. This DEM includes the best available lidar known to exist at the time of DEM creation that met project specifications. This DEM includes data for Atlantic, Camden, Cape May, Cumberland, Gloucester, and Salem Counties.

 The DEM was produced from the following lidar data sets:
 1. 2018 New Jersey South Jersey FEMA
 2. 2018 NJ Southern NJ
 3. 2015 USGS Delaware Valley
 4. 2014 NGS Coastal Mapping Program Topobathy Lidar: Post-Sandy Atlantic Seaboard


 The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

This dataset contains an inventory of landslides in many of the most landslide-prone parts of Minnesota. This project was created to improve our understanding of the landslide hazard in Minnesota and to provide a nearly statewide base map of landslide data. The mapping was performed by geologists from the U.S. Geological Survey, the Freshwater Society, and several academic institutions where undergraduate students, graduate students and faculty performed mapping. Contributing academic institution include the University of Minnesota Duluth, the University of Minnesota Twin Cities, the University of Wisconsin-Superior, Gustavus Adolphus College, Winona State University, Minnesota State University, Mankato, St. Thomas University, and North Dakota State University. These landslides were identified using several methods. These include analysis of historical records, direct field observation, location using satellite or aerial imagery, and identification in topographic data products derived from the statewide lidar data coverage. Most of the mapped landslides were identified using lidar derivatives and have not been evaluated in the field by geologists or engineers. These data should be considered a preliminary survey and are not intended to represent a complete and accurate inventory of landslides for these areas. There may be a range in the accuracy, detail, and completeness with which landslides are mapped, and in the information associated with a given landslide; however, all mapped landslides were reviewed by USGS personnel and the senior project members. Mapping procedures including the assignment of numerical values for confidence follow guidelines found in DOGAMI Special Paper 42: https://www.oregongeology.org/pubs/sp/p-SP-42.htm. Site-specific investigations should be completed before using these data for land development or management decisions. This Data Release consists of: 1) Minnesota_Landslides_v1_1.gdb.zip which contains the landslide inventory mapping data and the areas that were mapped, to be used in a GIS, 2) Minnesota_Landslides_v1_3.sd which is an ESRI service layer definition file that enables use of the data in online and offline GIS, 3) MN_Landslide_Photos.zip that contains a collection of geotagged photos showing landslides; these can be imported into a GIS, and 4) metadata.xml which contains metadata for all included files. Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

VDEQ Spring SITESThe VDEQ Spring SITES database contains data describing the geographic locations and site attributes of natural springs throughout the commonwealth. This data coverage continues to evolve and contains only spring locations known to exist with a reasonable degree of certainty on the date of publication. The dataset does not replace site specific inventorying or receptor surveys but can be used as a starting point. VDEQ's initial geospatial dataset of approximately 325 springs was formed in 2008 by digitizing historical spring information sheets created by State Water Control Board geologists in the 1970s through early 1990s. Additional data has been consolidated from the EPA STORET database, the U.S. Geological Survey's Ground Water Site Inventory (GWSI) and Geographic Names Inventory System (GNIS), the Virginia Department of Health SDWIS database, the Virginia DEQ Virginia Water Use Data Set (VWUDS), the Commonwealth of Virginia Division of Water Resources and Power Bulletin No. 1: "Springs of Virginia" by Collins et al., 1930 as well as several VDWR&P Surface Water Supply bulletins from the 1940's - 1950's. A 1992 Virginia Department of Game and Inland Fisheries / Virginia Tech sponsored study by Helfrich et al. titled "Evaluation of the Natural Springs of Virginia: Fisheries Management Implications", a 2004 Rockbridge County groundwater resources report written by Frits van der Leeden, and several smaller datasets from consultants and citizens were evaluated and added to the database when confidence in locational accuracy was high or could be verified with aerial or LIDAR imagery. Significant contributions have been made throughout the years by VDEQ Groundwater Characterization staff site visits as well as other geologists working in the region including: Matt Heller at Virginia Division of Geology and Mineral Resources (VDMME), Wil Orndorff at the Virginia Department of Conservation and Recreation Karst Program (VDCR), and David Nelms and Dan Doctor of the U.S. Geological Survey (USGS). Substantial effort has been made to improve locational accuracy and remove duplication present between data sources. Hundreds of spring locations that were originally obtained using topographic maps or unknown methods were updated to sub-meter locational accuracy using post-processed differential GPS (PPGPS) and through the use of several generations of aerial imagery (2002-2017) obtained from Virginia's Geographic Information Network (VGIN) and 1-meter LIDAR, where available. Scores of new spring locations were also obtained by systematic quadrangle by quadrangle analysis in areas of the Shenandoah Valley where 1-meter LIDAR datasets where obtained from the U.S. Geological Survey. Future improvements to the dataset will result when statewide 1-meter LIDAR datasets becomes available and through continued field work by DEQ staff and other contributors working in the region. Please do not hesitate to contact the author to correct mistakes or to contribute to the database.VDEQ_Springs_FIELD_MEASUREMENTSThe VDEQ Spring FIELD MEASUREMENTS database contains data describing field derived physio-chemical properties of spring discharges measured throughout the Commonwealth of Virginia. Field visits compiled in this dataset were performed from 1928 to 2019 by geologists with the State Water Control Board, the Virginia Division of Water and Power, the Virginia Department of Environmental Quality, and the U.S. Geological Survey with contributions from other sources as noted. Values of -9999 indicate that measurements were not performed for the referenced parameter. Please do not hesitate to contact the author to add data to the database or correct errors.VDEQ_Springs_WQThe VDEQ_Spring_WQ database is a geodatabase containing groundwater sample information collected from springs throughout Virginia. Sample specific information include: location and site information, measured field parameters, and lab verified quantifications of major ionic concentrations, trace element concentrations, nutrient concentrations, and radiological data. The VDEQ_Spring_WQ database is a subset of the VDEQ GWCHEM database which is a flat-file geodatabase containing groundwater sample information from groundwater wells and springs throughout Virginia. Sample information has been correlated via DEQ Well # and projected using coordinates in VDEQ_Spring_SITES database. The GWCHEM database is comprised of historic groundwater sample data originally archived in the United States Geological Survey (USGS) National Water Information System (NWIS) and the Environmental Protection Agency (EPA) Storage and Retrieval (STORET) data warehouse. Archived STORET data originated as groundwater sample data collected and uploaded by Virginia State Water Control Board Personnel. While groundwater sample data in the STORET data warehouse are static, new groundwater sample data are periodically uploaded to NWIS and spring laboratory WQ data reflect NWIS downloaded on 9/30/2019. Recent groundwater sample data collected by Virginia Department of Environmental Quality (DEQ) personnel as part of the Ambient Groundwater Sampling Program are entered into the database as lab results are made available by the Division of Consolidated Laboratory Services (DCLS). When possible, charge balances were calculated for samples with reported values for major ions including (at a minimum) calcium, magnesium, potassium, sodium, bicarbonate, chloride, and sulfate. Reported values for Nitrate as N, carbonate, and fluoride were included in the charge balance calculation when available. Field determined values for bicarbonate and carbonate were used in the charge balance calculation when available. For much of the legacy DEQ groundwater sample data, bicarbonate values were derived from lab reported values of alkalinity (as mg/CaCO3) under the assumption that there was no contribution by carbonate to the reported alkalinity value. Charge balance values are reported in the "Charge Balance" column of the GWCHEM geodatabase. The closer the charge balance value is to unity (1), the lower the assumed charge balance error.In order to preserve the numerical capabilities of the database, non- numeric lab qualifiers were given the following numeric identifiers:- (minus sign) = less than the concentration specified to the right of the sign-11110 = estimated-22220 = presence verified but not quantified-33330 = radchem non-detect, below sslc-4440 = analyzed for but not detected-55550 = greater than the concentration to the right of the zero-66660 = sample held beyond normal holding time-77770 = quality control failure. Data not valid.-88880 = sample held beyond normal holding time. Sample analyzed for but not detected. Value stored is limit of detection for proces in use.-11120 = Value reported is less than the criteria of detection.-9999 = no data (parameter not quantified)A more in depth descprition and hydrogeologic analysis of the database can be found hereAn in Depth data fact sheet can be found here