The dataset contains constructed unique geospatial identifier for buildings. A buildings UBID is the north axis aligned "bounding box" of its footprint represented as the centroid (in the GDAL grid reference system format), which is represented by the first set of characters before the first dash, and four cardinal extents, which are represented by the four sets of numbers after the first dash (North, East, South, West),The data has been constructed by spatially joining the latest (2019) building footprints published in DC Open Data with the Common Ownership Lot shapefile. The UBIDs were coded using US DOE’s Implementation code. Please note that the current data set may include some unnecessary structures identified as buildings. These included sheds, overhangs, bus stops, and other structures that do not need to be assigned a UBID. An updated version of the UBID dataset will be released when this issue is resolved. This project is the result of the US DOE Better Buildings Building Energy Data Analysis (BEDA) Accelerator. US DOE is working with stakeholders including state and local governments, commercial and residential building data aggregators, property owners, and product and service providers to develop the UBID system and to pilot it in real-world settings. US DOE and its partners are demonstrating the benefits of UBID in managing and cross-referencing large building datasets and in reducing the costs and enhancing the value proposition of leveraging building energy data. UBIDs For more information regarding UBIDs please visit: https://www.energy.gov/eere/buildings/unique-building-identifier-ubid

Geospatial data about Aspen, Colorado Unique Building Identifier. Export to CAD, GIS, PDF, CSV and access via API.

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.

Buildings within the territory of the City of Sherbrooke and belonging to one of the following categories: commerce, hospital, school or municipal building. These categories are associated with subtype codes 2, 3, 4 and 5 respectively. * BUILDING IDG: Unique building identifier* SUSTYPE: Building subtype code This third party metadata element was translated using an automated translation tool (Amazon Translate).

This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413.

Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).

DATA HAS BEEN MIGRATED TO https://data.ess-dive.lbl.gov/view/doi:10.15485/2283980

The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1".

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).

There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2".

1. Data, separated by state - minimalist list of each building (rows) for the following fields (columns)
   1. ID - unique building ID
   2. Footprint2D - lat/lon vertices of building footprint
   3. State_Abbrev - Abbreviation for the from which building is located
   4. Area - estimate of total conditioned floor area (ft²)
   5. Area2D - footprint area (ft²)
   6. CZ - ASHRAE Climate Zone designation
   7. Height - building height (ft)
   8. NumFloors - number of floors (above-grade)
   9. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)
   10. CZ - US climate zone designation
   11. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards
   12. Standard - building vintage (determined by building age)

This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA).

Abstract

"The Building Performance Database (BPD) is the nation's largest dataset of information about the energy-related characteristics of commercial and residential buildings. The BPD combines, cleanses and anonymizes data collected by federal, state and local governments, utilities, energy efficiency programs, building owners and private companies, and makes it available to the public" (Lawrence Berkeley National Laboratory, 2022). Data curated by Carnegie Mellon University Libraries.

Methodology

Data were combined across the datasets listed on the BPD website (Menu button -%3E Public Datasets -%3E List of Files).

Usage

• The raw data were collected from public sources by the BPD.

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• The BPD-curated data are for public use.
• Data from multiple years are included.

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• The building identifiers in the id column may be duplicated across data sources.

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Footprint of buildings on Laval territory produced by the City of Laval. To help understand the data, here are the descriptions of the fields used: ESPG: 32188 Field Properties Name Description Type References/Comments ID_STRUCTURE Unique Building Identifier Long TYPE Indicates the type of building Text (50) Primary, secondary, agricultural, agricultural, railway station, railway station, railway station, railway station NAME_BUILDING When relevant, indicate the name of the building Text (200) DATE_RELEASE Date of creation or geometric modification of the target entity. In the absence of information, it means that we do not have information available. Date YYYY-MM-DD TYPE_RELEVE Defines by what technical means the digitization of the data was obtained. For example, the process could be geoprocessing or manual scanning. In the absence of information, it means that we do not have information available. Text (50) Field of values: Lidar geoprocessing Manual scanning Manual scanning Photo-interpretation (2D) Photogrammetry (3D) Field survey Remote sensing Automatic vectoring Undetermined**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Introduction

This dataset contains all residential buildings in Germany with their construction year, size class, refurbishment state, and TABULA archetype. It is a partial dump of the ETHOS.BUILDA database (version v8_20240916). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset.

ETHOS.BUILDA is made available under the Open Database License (ODbL). The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.

Data structure and file overview

Building data is provided per federal state, the files are named according to the NUTS-1 region names. The building data has the following fields:

field name description

ID unique identifier of the building

position location of building centroid in WKT-format, EPSG:3035

construction_year

value: construction year,

source: source of the construction year data,

lineage: construction year assignment method

size_class

value: size class of the building,

source: source of the size class data,

lineage: size class assignment method

refurbishment_state

value: refurbishment state of the building,

source: source of the refurbishment state data,

lineage: refurbishment state assignment method

tabula_type

value: TABULA type of the building,

source: source of the TABULA data,

lineage: TABULA type assignment method

A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no entry for the source "v3_model.json", as it refers to the internally trained machine learning model for the respective attribute and not to an external data source.

The full footprint polygons from which the centroids are derived and the sources of the footprints are found in the related dataset linked as "is supplemented by".

Acknowledgements

This work was supported by the Helmholtz Association under the program "Energy System Design".

Graph and download economic data for New Private Housing Structures Authorized by Building Permits for Latah County, ID (BPPRIV016057) from 1990 to 2024 about Latah County, ID; ID; permits; buildings; private; housing; and USA.

Local Law 84 of 2009 (LL84) requires annual energy and water benchmarking data to be submitted by owners of buildings with more than 50,000 square feet. This data is collected via the Environmental Protection Agency's (EPA) Portfolio Manager website Each property is identified by it's EPA assigned property ID, and can contain one or more tax lots identified by one or more BBLs (Borough, Block, Lot) or one or more buildings identified by one or more building identification numbers (BIN) Please visit DOB's Benchmarking and Energy Efficiency Rating page for additional information.

Unit identification points are added to buildings and/or parcels where more than one structure or Unit exists at the parcel level. The purpose is so permits can be issued against a Unit. This layer is limited to only those units that have been identified for the purpose of assigning a unit to an address as they relate to possible permit activity where multiple units exist on a parcel. ADR_IDs are contained in the dataset for maintenance of a relationship class with address points and parcels.Click here to visit Pima County's Open Data site.PurposeProvide a location for permits to be issued against a unit.Dataset ClassificationLevel 0 - OpenKnown UsesThis layer is not in any known applications.Known Errors Not all existing Units are represented, only those for which in recent years have required that a building permit be issued at the Unit level. Placement of Unit ID points is approximate and continues to be refined as building and site plans become available. The Unit_ID is intended to be unique.ContactPima CountyUpdate FrequencyAs Needed

The full list of buildings required to comply with the Chicago Energy Benchmarking Ordinance. As of 2016, this list includes all commercial, institutional, and residential buildings larger than 50,000 square feet. The information in this dataset should be used by building owners / managers or other building representatives to determine if your property needs to comply by the annual deadline of June 1st. This data can also be used to look up your property's unique 6-digit Chicago Energy Benchmarking ID, which is required for compliance. (The ID is also included the notification letters sent by the City.) The Energy Benchmarking Ordinance calls on existing municipal, commercial, and residential buildings larger than 50,000 square feet to track whole-building energy use, report to the City annually, and verify data accuracy every three years. The law, which phases in from 2014-2017, covers less than 1% of Chicago’s buildings, which account for approximately 20% of total energy used by all buildings. For more details, including ordinance text, rules and regulations, and timing, please visit www.CityofChicago.org/EnergyBenchmarking. Note that the ordinance authorizes the City to make individual building data readily-available to the public, beginning with the second year in which a building is covered. The Covered Buildings List is distinct from the publicly-shared energy use data, and does not include energy use or any other information reported in the benchmarking process. For the building-specific energy use dataset, see https://data.cityofchicago.org/d/xq83-jr8c. If your property is on this list, and you do not believe you are covered by the ordinance, please submit the appropriate online exemption form at: www.CityofChicago.org/EnergyBenchmarking. If your property is not on the list, and it meets the compliance criteria, please check for any alternative building addresses. If the building is still not found, please submit the Building ID Request Form (http://www.cityofchicago.org/city/en/depts/mayor/iframe/Benchmarking_ID_Request.html). The Chicago Energy Benchmarking Help Center can assist with any other questions, and can be reached at (855)858-6878, or by email: Info@ChicagoEnergyBenchmarking.org. This dataset will be refreshed periodically as additional information becomes available. It is advisable to use the then-current version of any dataset, if possible.

These footprint extents are collapsed from an earlier 3D building model provided by Pictometry of 2010, and have been refined from a version of building masses publicly available on the open data portal for over two years.The building masses were manually split with reference to parcel lines, but using vertices from the building mass wherever possible.These split footprints correspond closely to individual structures even where there are common walls; the goal of the splitting process was to divide the building mass wherever there was likely to be a firewall. An arbitrary identifier was assigned based on a descending sort of building area for 177,023 footprints. The centroid of each footprint was used to join a property identifier from a draft of the San Francisco Enterprise GIS Program's cartographic base, which provides continuous coverage with distinct right-of-way areas as well as selected nearby parcels from adjacent counties. See accompanying document SF_BldgFoot_2017-05_description.pdf for more on methodology and motivation

Graph and download economic data for New Private Housing Structures Authorized by Building Permits for Kootenai County, ID (BPPRIV016055) from 1990 to 2024 about Kootenai County, ID; Coeur D'Alene; ID; permits; buildings; private; housing; and USA.

Graph and download economic data for New Private Housing Units Authorized by Building Permits: 1-Unit Structures for Boise City, ID (MSA) (BOIS216BP1FH) from Jan 1988 to May 2025 about Boise City, privately owned, ID, 1-unit structures, permits, family, buildings, housing, and USA.

Dataset

This dataset was created by whznpu

Contents

Graph and download economic data for All Employees: Construction: Foundation, Structure, and Building Exterior Contractors in Boise City, ID (MSA) (SMU16142602023810001SA) from Jan 1991 to May 2025 about contractors, Boise City, ID, buildings, construction, employment, and USA.

The Cadastre Building Point – Annual Version 2024. The Matrikkelen building point contains a small extract of the building information registered in the Matrikkelen, Norway’s official register of real estate, including buildings. The data set contains representation point, building type, building number which is the building’s identification regardless of system, current status, connection key against Riksantikvaren registers and building type. Expired buildings are not included, nor are building changes such as superstructures, extensions. Distributions are set up against a distribution solution that is based on change log service from the matricle system. The different distributions have different refresh rates, from 15 minutes delay on WFS and downloading freely selected area from maps, daily for municipal files and weekly for county and country files (new file only if changes have occurred in the Cadastre). In case of major changes/loads, delays can be longer.