Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. The project being based in Ghana, the current focus is on the continent of Africa.

Image credit: Google AI

“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.

This dataset pulls from many different data sources to identify individual building characteristics of all buildings in Boston. It also identifies high-potential retrofit options to reduce carbon emissions in multifamily buildings, using the best available data and assumptions from building experts.

Building characteristics will require on-site verification before an owner can act on them.

Find out more about carbon targets for Boston's existing large buildings.

We manually edited an aerial and a satellite imagery dataset of building samples and named it a WHU building dataset. The aerial dataset consists of more than 220, 000 independent buildings extracted from aerial images with 0.075 m spatial resolution and 450 km2 covering in Christchurch, New Zealand. The satellite imagery dataset consists of two subsets. One of them is collected from cities over the world and from various remote sensing resources including QuickBird, Worldview series, IKONOS, ZY-3, etc. The other satellite building sub-dataset consists of 6 neighboring satellite images covering 550 km2 on East Asia with 2.7 m ground resolution.

Collapsed Buildings

## Overview

Collapsed Buildings is a dataset for object detection tasks - it contains Collapsed House annotations for 250 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Building that houses a branch of government; in which the business of a department of government administration is carried out

Buildings

## Overview

Buildings is a dataset for instance segmentation tasks - it contains Buildings annotations for 598 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is a merged from different datasets of different categories.

db images are from RescueNet, Cyclone Wildfire Flood Earthquake Database, AIDERdata

Aug_db are db's Augmented images

db2 are extracted images from Ukraine Images 2023

Damaged Buildings

## Overview

Damaged Buildings is a dataset for object detection tasks - it contains Buildings UdCq annotations for 422 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Building that houses a branch of government; in which the business of a department of government administration is carried out

The goal of the ZuBuD Image Database is to share image data sets with researcheres around the world. To facilitate this, we have created this site, which contains over 1005 images about Zurich city building. The detail information about the database can be found on our Technical Report:TR-​260.

LIM_411

Address: Agkiras 147

The building, located in the heart of the Turkish Cypriot quarter and very close to the Cami Cedit Mosque, was originally a school for Turkish Cypriot girls. It is unclear when it was built. The buildings surrounding it provided accommodation for the students. At some point it was used as a police station. Today, it functions as the Bi-communal Multi-functional Centre of the Municipality of Limassol.

The building’s typology is very simple. It is a single-storey building, consisting of two large rooms and a smaller one between them. All three rooms have entrances on both the front and the back of the building. A covered exterior area is created on both sides.

The appearance of the building is dominated by visible stone which is used in structural elements and decorative details. The main south entrance is surrounded by a carved stone arc and columns with engraved details. Visible stone can also be found on the frame of the small room’s south door, the frames of the windows, the niches, the corners and the base of the building and on its top part under the roof.

The walls of the building are made of stone. The floor consists of painted tiles in the covered exterior areas, tiles in the two big rooms and newer wooden parquet in the small room. All the doors and windows are made of wood while the ceilings are made of wooden planks. The roof structure is composed of timber beams and tiles.

Newer additions include the plasterboard walls that partly separate the space in the big rooms and the structure at the back of the west room that is used as a sanitary space.

The building was included in the catalogue of listed buildings in 2000 for its significance as a Turkish school building with traditional and neo-classical characteristics.

"Portal for heritage buildings integration into the contemporary built environment" (URBAN PERISCOPE), is funded by the Cyprus Research & Innovation Foundation Restart Programs 2016-2020 "Integrated Projects". Project Coordinator: The Cyprus Institute; Partners: Cyprus University of Technology (Cyprus), Frederic Research Center (Cyprus), Fondazione Bruno Kessler (Italy), University of Catania (Italy), Department of Urban Planning and Housing, Municipality of Strovolos, Municipality of Limassol, HIT- Hypertech Innovations, NetU Consultations and Talos RTD. The project [Grant number: INTEGRATED/0918/0034] is co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research Innovation Foundation.

Labeling Construction Buildings

## Overview

Labeling Construction Buildings is a dataset for object detection tasks - it contains Hats annotations for 209 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Building that houses a branch of government; in which the business of a department of government administration is carried out

It is an open data set from 507 non-residential buildings that includes hourly whole building electrical meter data for one year. Each of the buildings has meta data such as or area, weather, and primary use type. This data set can be used to benchmark various statistical learning algorithms and other data science techniques. It can also be used simply as a teaching or learning tool to practice dealing with measured performance data from large numbers of non-residential buildings. The charts below illustrate the breakdown of the buildings according to location, building industry, sub-industry, and primary use type.

Object Detection Buildings

## Overview

Object Detection Buildings is a dataset for object detection tasks - it contains Buildings annotations for 557 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Current building datasets suffer from incomplete coverage of building multi-attributes. This paper presents the first national-scale Multi-Attribute Building dataset (CMAB) with artificial intelligence, covering 3,667 spatial cities, 31 million buildings, and 23.6 billion m² of rooftops with an F1-Score of 89.93% in OCRNet-based extraction, totaling 363 billion m³ of building stock. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating morphology, location, and function features. Using multi-source data, including billions of remote sensing images and 60 million street view images (SVIs), we generated rooftop, height, structure, function, style, age, and quality attributes for each building with machine learning and large multimodal models. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation, mostly above 80%. Our dataset and results are crucial for global SDGs and urban planning.Data records: A building dataset with a total rooftop area of 23.6 billion square meters in 3,667 natural cities in China, including the attribute of building rooftop, height, structure, function, age, style and quality, as well as the code files used to calculate these data. The deep learning models used are OCRNet, XGBoost, fine-tuned CLIP and Yolo-v8.Supplementary note: The architectural structure, style, and quality are affected by the temporal and spatial distribution of street views in China. Regarding the recognition of building colors, we found that the existing CLIP series model can not accurately judge the composition and proportion of building colors, and then it will be accurately calculated and supplemented by semantic segmentation and image processing. Please contact zhangyec23@mails.tsinghua.edu.cn or ylong@tsinghua.edu.cn if you have any technical problems.Reference Format: Zhang, Y., Zhao, H. & Long, Y. CMAB: A Multi-Attribute Building Dataset of China. Sci Data 12, 430 (2025). https://doi.org/10.1038/s41597-025-04730-5.

Building that houses a branch of government; in which the business of a department of government administration is carried out

Drone Buildings

## Overview

Drone Buildings is a dataset for object detection tasks - it contains Buildings annotations for 2,365 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Laos Imports of furniture, lighting signs, prefabricated buildings was US$32.52 Million during 2023, according to the United Nations COMTRADE database on international trade. Laos Imports of furniture, lighting signs, prefabricated buildings - data, historical chart and statistics - was last updated on June of 2025.