Buildings Instance Segmentation Instance Segmentation Dataset and Pre-Trained Model by Roboflow Universe Projects

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Explore Dataset

Here are a few use cases for this project:

Urban Planning and Development: Utilizing the "Buildings Instance Segmentation" model to analyze aerial images of a city, urban planners can identify different types of buildings and their distribution to make informed decisions about zoning, infrastructure, and future developments.
Damage Assessment and Emergency Response: In the aftermath of natural disasters, the model can be used to analyze aerial images to quickly identify damaged or destroyed buildings, helping emergency responders prioritize rescue efforts and allocate resources more efficiently.
Real Estate Market Analysis: Real estate professionals can use the model to analyze aerial views of neighborhoods, identifying different types of buildings and their locations to offer better insights into neighborhood characteristics and trends for potential property buyers.
Energy Efficiency and Environmental Impact Analysis: By identifying different building classes and their distribution, researchers can evaluate energy consumption patterns and develop strategies for improving energy efficiency and reducing the environmental impact in urban areas.
Historical Preservation and Cultural Heritage: The model can be employed to identify and track the presence of culturally significant or historically important buildings for preservation efforts, ensuring their protection and integration into urban development plans.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite this Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ buildings-instance-segmentation_dataset,
    title = { Buildings Instance Segmentation Dataset },
    type = { Open Source Dataset },
    author = { Roboflow Universe Projects },
    howpublished = { \url{ https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation } },
    url = { https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jan },
    note = { visited on 2023-12-02 },
}