USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Rang ...

The NTI is an aggregation of structure inventory and appraisal data of tunnels located on public roads submitted by States, Federal agencies and Tribal governments in accordance with the National Tunnel Inspection Standards (NTIS) which requires each State prepare and maintain an inventory of all tunnels. The NTI data is used as a data source to assist in the oversight of the National Tunnel Inspection Program and to respond to inquiries from different entities on the Nation’s tunnels.

Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.

Data in the NBI is used to meet legislative reporting requirements and provide bridge owners, the Federal Highway Administration (FHWA) and the general public with information on the number and condition of the Nation’s bridges.The National Bridge Inventory dataset is as of June 27, 2024 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 615,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal and Tribal lands. The inventory data present a complete picture of the location, description, classification, and general condition data for each bridge. The element data present a breakdown of the condition of each structural and bridge management element for each bridge on the National Highway System (NHS). The Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation's Bridges contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519105.

The National Bridge Inventory dataset is as of June 20, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 624,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and local roads, as well as publicly-accessible bridges on Federal and Tribal lands. The inventory data presented includes information on the location, description, classification, and general condition for each bridge. The Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation's Bridges (Coding Guide) contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519105. For additional questions regarding regulations for the National Bridge Inventory or the Coding Guide please contact the National Bridge and Tunnel Inventory team at NBTIS_Support@dot.gov. For questions on the geospatial component of the dataset, contact the NTAD team at NTAD@dot.gov. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1519105

The NBI System is the collection of bridge inspection information and costs associated with bridge replacements of structurally deficient bridges on and off the NHS. This data is collected under the auspices of the National Bridge Inspection Standards (NBIS) as prescribed by law. The NBI System collects the information that is used to determine eligibility for NHS projects, performance measure reporting, NHS penalty determination, and reporting to Congress. It supports oversight of the NBIS through various report tools, and provides data reporting that supports agency strategic goals.

The NBI System is the collection of bridge inspection information and costs associated with bridge replacements of structurally deficient bridges on and off the NHS. This data is collected under the auspices of the National Bridge Inspection Standards (NBIS) as prescribed by law. The NBI System collects the information that is used to determine eligibility for NHS projects, performance measure reporting, NHS penalty determination, and reporting to Congress. It supports oversight of the NBIS through various report tools, and provides data reporting that supports agency strategic goals.

DHS, FIMA, FEMA’s Response Geospatial Office, Oak Ridge National Laboratory, and the U.S. Geological Survey collaborated to build and maintain the nation’s first comprehensive inventory of all structures larger than 450 square feet for use in Flood Insurance Mitigation, Emergency Preparedness and Response.

Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.

The NBI is an aggregation of State, Federal agency and Tribal government bridge and associated highway data submitted to and maintained by the Federal Highway Administration (FHWA). It contains inspection and appraisal data of more than 600,000 of the Nation’s highway bridges located on public roads in accordance with the National Bridge Inspection Standards. The NBI data is used to determine the condition of the Nation’s bridges that is included in reports to Congress, as a data source for executing various sections of the Federal-aid program which involve highway bridges, for assessing the bridge penalty provisions of Title 23 United States Code (U.S.C.) section 119, as the data source for the evaluation of bridge performance measures established in Title 23 U.S.C. section 150, to assist in the oversight of the National Bridge Inspection Program, as a data source to assess and inform the condition and funding needs of highway bridges, and for strategic national defense needs.

The National Inventory of Architectural Heritage (NIAH) is a state initiative under the administration of the Department of Housing, Local Government and Heritage and established on a statutory basis under the provisions of the Architectural Heritage (National Inventory) and Historic Monuments (Miscellaneous Provisions) Act 1999.The purpose of the NIAH is to identify, record, and evaluate the post-1700 architectural heritage of Ireland, uniformly and consistently as an aid in the protection and conservation of the built heritage. NIAH surveys provide the basis for the recommendations of the Minister for Housing, Local Government and Heritage to the planning authorities for the inclusion of particular structures in their Record of Protected Structures (RPS). This dataset is provided for re-use in a number of ways and the technical options are outlined below. For a live and current view of the data, please use the web services or the data extract tool in the Historic Environment Viewer. The NIAH also provide an Open Data snapshot of its national dataset in CSV as a bulk data download. It contains all the Ministerial Recommendations published to date and is updated as surveys are published. Open Data Bulk Data Downloads (version date: 11/10/2023) The NIAH Survey data is provided as a national download in Comma Separated Value (CSV) format. This format can be easily integrated into a number of software clients for re-use and analysis. The Longitude and Latitude coordinates are also provided to aid its re-use in web mapping systems, however, the ITM easting/northings coordinates should be quoted for official purposes. GIS Web Service APIs (live views): For users with access to GIS software please note that the NIAH data is also available as spatial data web services. By accessing and consuming the web service users are deemed to have accepted the Terms and Conditions. The web services are available at the URL endpoints advertised below: NIAH Feature Service: https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/NIAHBuildingsOpenData/FeatureServer Historic Environment Viewer - Query Tool The "Query" tool can alternatively be used to selectively filter and download the data represented in the Historic Environment Viewer. The instructions for using this tool in the Historic Environment Viewer are detailed in the associated Help file: https://www.archaeology.ie/sites/default/files/media/pdf/HEV_UserGuide_v01.pdf .hidden { display: none } Public Dashboards

Structures adjacent to the project area are described and photographed in relation to the project boundaries. Basic data gathered for each structure included location, function, and age of the structure. For structures that are older than 50 years, information that documents the history of ownership, architectural description, modifications, integrity, associated outbuildings and landscape features was also collected. For structures that appear to be potentially eligible for inclusion in the National Register, a DOT Structure Inventory Form, modified from the OPRHP form, was completed and a detailed architectural description is included. There are five structures in the project area that appear to meet the criteria for eligibility to the National Register.

Graph and download economic data for Existing Single-Family Home Sales: Housing Inventory (HSFINVUSM495N) from Jul 2024 to Jul 2025 about inventories, 1-unit structures, family, sales, housing, and USA.

The NBI System is the collection of bridge inspection information and costs associated with bridge replacements of structurally deficient bridges on and off the NHS. This data is collected under the auspices of the National Bridge Inspection Standards (NBIS) as prescribed by law. The NBI System collects the information that is used to determine eligibility for NHS projects, performance measure reporting, NHS penalty determination, and reporting to Congress. It supports oversight of the NBIS through various report tools, and provides data reporting that supports agency strategic goals.

We developed Pan-European maps of timber volume (V), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10 x 10 m² for the reference year 2020 using a combination of a Sentinel 2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data.

For mapping, we used the k-Nearest Neighbor (kNN, k=7) approach with a harmonized database of species-specific V and AGB from 14 NFIs across Europe. This database encompasses approximately 151,000 sample plots, which were intersected with the above-mentioned Earth observation data. The maps cover 40 European countries, forming a continuous coverage of the western part of the European continent.

A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to 73 % the South-Eastern area.

The created maps are the first of their kind as they are utilizing a huge amount of harmonized NFI observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high V and AGB values tend to be underestimated. Summarizing map values (pixel counting) over large regions such as countries or whole Europe will consequently result in biased estimates that need to be interpreted with care.

The author list is sorted by last name except for the first and last authors who also serve as corresponding authors.

Corresponding authors: Jukka.Miettinen@vtt.fi, Johannes.Breidenbach@nibio.no

The NBI System is the collection of bridge inspection information and costs associated with bridge replacements of structurally deficient bridges on and off the NHS. This data is collected under the auspices of the National Bridge Inspection Standards (NBIS) as prescribed by law. The NBI System collects the information that is used to determine eligibility for NHS projects, performance measure reporting, NHS penalty determination, and reporting to Congress. It supports oversight of the NBIS through various report tools, and provides data reporting that supports agency strategic goals.

The NBI System is the collection of bridge inspection information and costs associated with bridge replacements of structurally deficient bridges on and off the NHS. This data is collected under the auspices of the National Bridge Inspection Standards (NBIS) as prescribed by law. The NBI System collects the information that is used to determine eligibility for NHS projects, performance measure reporting, NHS penalty determination, and reporting to Congress. It supports oversight of the NBIS through various report tools, and provides data reporting that supports agency strategic goals.

The critical facilities data are derived from the USGS Structures Inventory Database (June, 2016). The structures in the derived dataset displays aggregated totals of law enforcement facilities, fire stations and EMS facilities, hospital and other medical facilities, and schools within several coastal footprints. These footprints include Coastal Shoreline Counties, Coastal Watershed Counties, Coastal States, the Coastal Zone, and FEMA Flood Zones.

Supplementary Data Sample Plot Inventory Sihlwald The Sihlwald is one of the largest contiguous beech forests in the Swiss Plateau region. In the year 2000, timber harvesting was abandoned. Since 2007 the forest has been under strict protection as a natural forest reserve on an area of 1098 ha and since 2008 as a cantonal nature and landscape conservation area (SVO Sihlwald). Since 2010, it carries the national label ‘Nature discovery park’ (‘Naturerlebnispark’). As part of the national monitoring in nature forest reserves, a sampling inventory (calipering threshold of 7 cm) with 226 plots on an area of 917 ha was carried out in the Sihlwald in autumn and early winter 2017. The aim was to describe the state and development of the forest structure and make comparisons with earlier sampling inventories in the same area from 1981, 1989 and 2003. This dataset contains supplementary tables for the publication by Brändli et al. (2020). The metadata file describes the structure of the tables.

Organic soils in the boreal forest commonly store as much carbon as the vegetation above ground. While recent efforts through the National Forest Inventory has yielded new spatial datasets of forest structure across the vast area of Canada’s boreal forest, organic soils are poorly mapped. In this geospatial dataset, we produce a map primarily of forested and treed peatlands, those with more than 40 cm of peat accumulation and over 10% tree canopy cover. National Forest Inventory ground plots were used to identify the range of forest structure that corresponds to the presence of over 40 cm of peat soils. Areas containing that range of forest cover were identified using the National Forest Inventory k-NN forest structure maps and assigned a probability (0-100% as integer) of being a forested or treed peatland according to a statistical model. While this mapping product captures the distribution of forested and treed peatlands at a 250 m resolution, open, completely treeless peatlands are not fully captured by this mapping product as forest cover information was used to create the maps. The methodology used in the creation of this product is described in: Thompson DK, Simpson BN, Beaudoin A. 2016. Using forest structure to predict the distribution of treed boreal peatlands in Canada. Forest Ecology and Management, 372, 19-27. https://cfs.nrcan.gc.ca/publications?id=36751 This distribution uses an updated forest attribute layer current to 2011 from: Beaudoin A, Bernier PY, Villemaire P, Guindon L, Guo XJ. 2017. Species composition, forest properties and land cover types across Canada’s forests at 250m resolution for 2001 and 2011. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/ec9e2659-1c29-4ddb-87a2-6aced147a990 Additionally, this distribution varies slightly from the original published in 2016 in that here slope data is derived from the CDEM: https://open.canada.ca/data/en/dataset/7f245e4d-76c2-4caa-951a-45d1d2051333 The above peatland probability map was further processed to delineate bogs vs fens (based on mapped Larix content via the k-NN maps), as well as an approximation of the extent of open peatlands using EOSD data. The result is a 9-type peatland map with a more complete methodology as detailed in: Webster, K. L., Bhatti, J. S., Thompson, D. K., Nelson, S. A., Shaw, C. H., Bona, K. A., Hayne, S. L., & Kurz, W. A. (2018). Spatially-integrated estimates of net ecosystem exchange and methane fluxes from Canadian peatlands. Carbon Balance and Management, 13(1), 16. https://doi.org/10.1186/s13021-018-0105-5 In plain text, the legend for the 9-class map is as follows: value="0" label="not peat" alpha="0" value="1" label="Open Bog" alpha="255" color="#0a4b32" value="2" label="Open Poor Fen" alpha="255" color="#5c5430" value="3" label="Open Rich Fen" alpha="255" color="#792652" value="4" label="Treed Bog" alpha="255" color="#6a917b" value="5" label="Treed Poor Fen" alpha="255" color="#aba476" value="6" label="Treed Rich Fen" alpha="255" color="#af7a8f" value="7" label="Forested Bog" alpha="255" color="#aad7bf" value="8" label="Forested Poor Fen" alpha="255" color="#fbfabc" value="9" label="Forested Rich Fen" alpha="255" color="#ffb6db" This colour scale is given in qml/xml format in the resources below. The 9-type peatland map from Webster et al 2018 was further refined slightly following two simple conditions: (1) any 250-m raster cell with greater than 40% pine content is classified as upland (non-peat); (2) all 250-m raster cells classified as water or agriculture via the NRCan North American Land Cover Monitoring System (https://doi.org/10.3390/rs9111098) is also classified as non-peatland (value of zero in the 9-class map. This mapping scheme was used at a regional scale in the following paper: Thompson, D. K., Simpson, B. N., Whitman, E., Barber, Q. E., & Parisien, M.-A. (2019). Peatland Hydrological Dynamics as A Driver of Landscape Connectivity and Fire Activity in the Boreal Plain of Canada. Forests, 10(7), 534. https://doi.org/10.3390/f10070534 And is reproduced here at a national scale. Note that this mapping product does not fully capture all permafrost peatland features covered by open canopy spruce woodland with lichen ground cover. Nor are treeless peatlands near the northern treeline captured in the training data, resulting in unknown mapping quality in those regions.