The Open Buildings 2.5D Temporal Dataset contains data about building presence, fractional building counts, and building heights at an effective1 spatial resolution of 4m (rasters are provided at 0.5m resolution) at an annual cadence from 2016-2023. It is produced from open-source, low-resolution imagery from the Sentinel-2 collection. The dataset is …

The Open Database of Buildings (ODB) is a collection of open data on buildings made available under the Open Government License - Canada. The ODB brings together 530 datasets originating from 107 government sources of open data. The database aims to enhance access to a harmonized collection of building features across Canada.

“Automatically Extracted Buildings” is a raw digital product in vector format created by NRCan. It consists of a single topographical feature class that delineates polygonal building footprints automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources.

Footprint outlines of buildings in New York City. Please see the following link for additional documentation: https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md

For additional resources, please refer to: https://nycmaps-nyc.hub.arcgis.com/search?tags=building&type=feature%2520service%2Cfeature%2520layer

Thinking Machines Data Science is releasing TM Open Buildings, a dataset of manually-drawn building outlines covering 12 Philippine cities with detailed annotations on building and roof attributes as seen over satellite imagery. We contribute the buildings in OpenStreetMap and also made available for download in Kaggle. This is made possible with the support from the Lacuna Fund.

Producing the dataset

The team has consulted HOTOSM Asia Pacific and community architects from the Philippine Action for Community-led Shelter Initiatives (PACSII) to validate our attributes and to ensure that our contributions are documented properly. We also looked at street-level views to check tags whenever available. We will take into consideration the feedback from local mappers as local knowledge always precedes, and will always provide changeset comments that are in compliance with OSM guidelines.

You may view more details of our process in our wiki page. Kindly use our Github Issues tab to file any specific concerns about the dataset.

License

This TM Open Buildings dataset is made available by Thinking Machines under the Open Database License (ODbL). Any rights in individual contents of the database are licensed under the Database Contents License.

Building Definitions

We define the buildings we mapped, as well as the attributes included, in the table below. Please refer to our wiki page for more details. | Building Type |Subtype | Definition | Mapped Attributes |
|----------------|--------|----------------------------------------------------------------------------------------|---------------------------------------------------------| | Settlement | Single | Residential houses that are individually distinct from surrounding structures | Roof material, Roof layout, Is within gated community? |
| | Dense | Tight clusters of small residential houses that do not have distinguishable boundaries | - |
| Non-settlement | | Commercial, industrial, or institutional buildings | Building height |

Coverage

The dataset covers selected 250m x 250m tiles in 12 Philippine cities, namely Dagupan City, Palayan City, City of Navotas, City of Mandaluyong, City of Muntinlupa, Legazpi City, Tacloban City, Iloilo City, Mandaue City, Cagayan de Oro City, Davao City, and Zamboanga City. The tiles are chosen to focus on residential areas that lie on a wide variety of terrains (urban, coastal, riparian, agricultural, etc.). All settlements and non-settlements within each tile are drawn manually. Data on the locations of the tiles are given in the following table.

Attributes

The following table contains the definitions of the attributes and how it is tagged in OSM. | Attribute | Type | Characteristics | OSM Key and Tag | |------------------------|------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------| | Roof Material | Natural/Galvanized Iron (GI)/Mixed | Looks rusty when old, silver/gray when new, lines and patches are usually evident. | roof:material = metal_sheet | | | Metal/Tiled | Whole roof is usually one solid color, tiled roofs have texture. | roof:material = roof_tiles | | | Concrete | Flat, usually has raised white edges, no visible roof “folds”, may be smooth or have objects on top. ...

Building structures include parking garages, ruins, monuments, and buildings under construction along with residential, commercial, industrial, apartment, townhouses, duplexes, etc. Buildings equal to or larger than 9.29 square meters (100 square feet) are captured. Buildings are delineated around the roof line showing the building "footprint." Roof breaks and rooflines, such as between individual residences in row houses or separate spaces in office structures, are captured to partition building footprints. This includes capturing all sheds, garages, or other non-addressable buildings over 100 square feet throughout the city. Atriums, courtyards, and other “holes” in buildings created as part of demarcating the building outline are not part of the building capture. This includes construction trailers greater than 100 square feet. Memorials are delineated around a roof line showing the building "footprint."Bleachers are delineated around the base of connected sets of bleachers. Parking Garages are delineated at the perimeter of the parking garage including ramps. Parking garages sharing a common boundary with linear features must have the common segment captured once. A parking garage is only attributed as such if there is rooftop parking. Not all rooftop parking is a parking garage, however. There are structures that only have rooftop parking but serve as a business. Those are captured as buildings. Fountains are delineated around the base of fountain structures.

This vector polygon dataset represents the building features in Yosemite National Park. This dataset utilizes the updated NPS Building Spatial Data Standard dated 12/15/20217. There are ongoing efforts to improve the spatial and attribute information of the buildings.Initial polygons were digitized from various sources with unknown provenance, likely satellite imagery or CAD files. Many existing building polygons were added or updated from 3D building footprints covering the 2019 Yosemite National Park 3DEP Lidar project area. New buildings are COGOed where possible, otherwise digitized from satellite imagery or extracted from DWG files on an as-needed basis. In 2025 a volunteer georeferenced old maps of the park and digitized some buildings that have been removed since the time of the maps' making. Information about how each building polygon was created is in the Map Method, Map Source, and Source Date fields.Polygons are meant to represent the building footprint, though there are still buildings represented with roof outlines, particularly private residences and others digitized from satellite imagery. Buildings with more than one FMSS Locations are split to delineate the multiple assets, even though the footprint is connected. When two or more footprints share a roof they are represented with multi-part polygons that represent the foundations of the buildings.Attributes in this dataset include identifier fields (building name and label fields, FMSS Location ID, and various other ID fields), the current state of the structure (Status and Is Extant fields), classification (Functional and Facility Use fields as well as Seasonality, Building Code, and Building Type), as well as record level metadata fields. Efforts by various staff members over the years have standardized and corrected many of these fields for most of the buildings, but inaccuracies remain.This dataset is meant for both public and internal use, with sharing status described in the Public Map Display and Data Access fields. Non-extant buildings are marked as No Public Map Display but remain a part of the dataset to provide insight into what the park used to look like.IRMA Data Store Reference

This data shows the digitized building footprints of buildings located within the City of Winchester, Virginia. This data was collected off Eagleview 2017 aerial imagery and was provided to the City after the flight.

NYS Building Footprints - metadata info:The New York State building footprints service contains building footprints with address information. The footprints have address point information folded in from the Streets and Address Matching (SAM - https://gis.ny.gov/streets/) address point file. The building footprints have a field called “Address Range”, this field shows (where available) either a single address or an address range, depending on the address points that fall within the footprint. Ex: 3860 Atlantic Avenue or Ex: 32 - 34 Wheatfield Circle Building footprints in New York State are from four different sources: Microsoft, Open Data, New York State Energy Research and Development Authority (NYSERDA), and Geospatial Services. The majority of the footprints are from NYSERDA, except in NYC where the primary source was Open Data. Microsoft footprints were added where the other 2 sources were missing polygons. Field Descriptions: NYSGeo Source : tells the end user if the source is NYSERDA, Microsoft, NYC Open Data, and could expand from here in the futureAddress Point Count: the number of address points that fall within that building footprintAddress Range : If an address point falls within a footprint it lists the range of those address points. Ex: if a building is on a corner of South Pearl and Beaver Street, 40 points fall on the building, and 35 are South Pearl Street it would give the range of addresses for South Pearl. We also removed sub addresses from this range, primarily apartment related. For example, in above example, it would not list 30 South Pearl, Apartment 5A, it would list 30 South Pearl.Most Common Street : the street name of the largest number of address points. In the above example, it would list “South Pearl” as the most common street since the majority of address points list it as the street. Other Streets: the list of other streets that fall within the building footprint, if any. In the above example, “Beaver Street” would be listed since address points for Beaver Street fall on the footprint but are not in the majority.County Name : County name populated from CIESINs. If not populated from CIESINs, identified by the GSMunicipality Name : Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GSSource: Source where the data came from. If NYSGeo Source = NYSERDA, the data would typically list orthoimagery, LIDAR, county data, etc.Source ID: if NYSGeo Source = NYSERDA, Source ID would typically list an orthoimage or LIDAR tileSource Date: Date the footprint was created. If the source image was from 2016 orthoimagery, 2016 would be the Source Date. Description of each footprint source:NYSERDA Building footprints that were created as part of the New York State Flood Impact Decision Support Systems https://fidss.ciesin.columbia.edu/home Footprints vary in age from county to county.Microsoft Building Footprints released 6/28/2018 - vintage unknown/varies. More info on this dataset can be found at https://blogs.bing.com/maps/2018-06/microsoft-releases-125-million-building-footprints-in-the-us-as-open-data.NYC Open Data - Building Footprints of New York City as a polygon feature class. Last updated 7/30/2018, downloaded on 8/6/2018. Feature Class of footprint outlines of buildings in New York City. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.mdSpatial Reference of Source Data: UTM Zone 18, meters, NAD 83. Spatial Reference of Web Service: Spatial Reference of Web Service: WGS 1984 Web Mercator Auxiliary Sphere.

Cet ensemble de données Open Source à grande échelle se compose de contours de bâtiments dérivés d'images satellite haute résolution de 50 cm. Il contient 1,8 milliard de détections de bâtiments en Afrique, en Amérique latine, dans les Caraïbes, en Asie du Sud et en Asie du Sud-Est. L'inférence a porté sur une superficie de 58 millions de km². Pour chaque bâtiment de cet ensemble de données, nous incluons le polygone décrivant son emprise au sol, un score de confiance indiquant notre degré de certitude qu'il s'agit d'un bâtiment et un Plus Code correspondant au centre du bâtiment. Aucune information n'est disponible sur le type de bâtiment, son adresse ou d'autres détails que sa géométrie. Les empreintes de bâtiments sont utiles pour de nombreuses applications importantes : de l'estimation de la population à la planification urbaine et à l'aide humanitaire, en passant par les sciences de l'environnement et du climat. Le projet est basé au Ghana, avec un accent initial sur le continent africain et de nouvelles informations sur l'Asie du Sud, l'Asie du Sud-Est, l'Amérique latine et les Caraïbes. L'inférence a été effectuée en mai 2023. Pour en savoir plus, consultez le site Web officiel de l'ensemble de données Open Buildings.

Este conjunto de datos abiertos a gran escala consta de esquemas de edificios derivados de imágenes satelitales de alta resolución de 50 cm. Contiene 1,800 millones de detecciones de edificios en África, América Latina, el Caribe, el sur de Asia y el sudeste asiático. La inferencia abarcó un área de 58 millones de km². Para cada edificio de este conjunto de datos, incluimos el polígono que describe…

Esse conjunto de dados aberto em grande escala consiste em contornos de edifícios derivados de imagens de satélite de alta resolução de 50 cm. Ele contém 1,8 bilhão de detecções de edifícios na África, América Latina, Caribe, Ásia Meridional e Sudeste Asiático. A inferência abrangeu uma área de 58 milhões de km². Para cada edifício nesse conjunto de dados, incluímos o polígono que descreve a área de contato no chão, uma pontuação de confiança que indica a probabilidade de ser um edifício e um Plus Code correspondente ao centro do edifício. Não há informações sobre o tipo de edifício, o endereço ou outros detalhes além da geometria. As pegadas de edifícios são úteis para uma série de aplicações importantes: desde estimativa de população, planejamento urbano e resposta humanitária até ciência ambiental e climática. O projeto é baseado em Gana, com foco inicial no continente africano e novas atualizações sobre o sul da Ásia, o sudeste da Ásia, a América Latina e o Caribe. A inferência foi realizada em maio de 2023. Para mais detalhes, consulte o site oficial do conjunto de dados Open Buildings.

To outline the locations of buildings on Parks Canada sites, buildings that Parks Canada manages, and other buildings of interest to Parks Canada. Polygon file to map building footprints of buildings on Parks Canada sites. Footprints may be derived by tracing the roof outline (for example from an airphoto) or using more detailed measurements of the ground floor. Data is not necessarily complete - updates will occur weekly.

This data release provides 100-m resolution building characteristics dataset for the Global South covering each year from 2016 to 2023, derived from Google OBT. These data were produced by the WorldPop Research Group at the University of Southampton in collaboration with researchers at the University of Bristol. This work was part of the FuturePop: Constructing high spatial resolution population projections and supporting the provision, access and updates of WorldPop spatial demographic datasets project, funded by the Wellcome Trust (grant 308679/Z/23/Z).

The BuildingsBench datasets consist of:

• Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock.
• 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF.

Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB).

BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below:

1. ElectricityLoadDiagrams20112014
2. Building Data Genome Project-2
3. Individual household electric power consumption (Sceaux)
4. Borealis
5. SMART
6. IDEAL
7. Low Carbon London

A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

Building footprint polygons are updated weekly by ECGIS. They provide a general reference of where buildings in Eaton County are located. These are not survey-grade.

Footprint outlines of buildings in New York City. Please see the following link for additional documentation: https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md

P Layers are the centroid layers for the Building and Building Historic layers. They contain the same data as those layers but are represented as points instead of polygons.

For additional resources, please refer to https://nycmaps-nyc.hub.arcgis.com/search?tags=building&type=feature%2520service%2Cfeature%2520layer

Tập dữ liệu mở quy mô lớn này bao gồm đường viền của các toà nhà được lấy từ hình ảnh vệ tinh có độ phân giải cao 50 cm. Tập dữ liệu này chứa 1,8 tỷ lượt phát hiện toà nhà ở Châu Phi, Mỹ La-tinh, vùng Caribbean, Nam Á và Đông Nam Á. Suy luận trên diện tích 58 triệu km². Đối với mỗi toà nhà trong tập dữ liệu này, chúng tôi đều có đa giác mô tả dấu vết của toà nhà trên mặt đất, điểm số độ tin cậy cho biết mức độ chắc chắn rằng đây là một toà nhà và một Plus Code tương ứng với tâm của toà nhà. Không có thông tin về loại toà nhà, địa chỉ đường phố hoặc bất kỳ thông tin nào khác ngoài hình học của toà nhà. Đường viền toà nhà rất hữu ích cho nhiều ứng dụng quan trọng: từ ước tính dân số, quy hoạch đô thị và hoạt động ứng phó nhân đạo đến khoa học môi trường và khí hậu. Dự án này có trụ sở tại Ghana, ban đầu tập trung vào lục địa Châu Phi và các thông tin cập nhật mới về Nam Á, Đông Nam Á, Mỹ Latinh và vùng Caribbean. Hoạt động suy luận được thực hiện trong tháng 5 năm 2023. Để biết thêm thông tin chi tiết, hãy xem trang web chính thức của tập dữ liệu Open Buildings.

The DAS Building Roof Outline layer contains polygon features as a graphical representation for individual building roof edge lines. The layer shows the spatial locations of building roof outlines located throughout the City of Calgary.

High resolution buildings dataset for Lake County, IN. The primary sources used to derive this buildings layer were 2013 LiDAR data and 2013 Ortho imagery. Ancillary data sources included GIS data provided by Lake County or created by the UVM Spatial Analysis Laboratory. This buildingsdataset is considered current as of Summer, 2013. Object-based image analysis techniques (OBIA) were employed to extract building information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2,500 and all observable errors were corrected.This dataset contains footprints for buildings and some large out buildings. Many garages and sheds are not included in this dataset. The Lake County Surveyor's Office ran the Regularize Building Footprints geoprocessing tool after the layer was created to make the building footprints more aesthetically appealing.