Universe

Buildings Instance Segmentation Computer Vision Model

byMMUAITask:
Instance Segmentation
License:CC BY 4.0

How to use the Buildings Instance Segmentation Segmentation API

Try This Model

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Or try a test image 

Code Snippets

from inference_sdk import InferenceHTTPClient

CLIENT = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY"
)

result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="buildings-instance-segmentation-6gnuh/1")
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Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "Building"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 
See all 9.7k imagesBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sampleBuildings Instance Segmentation buildings sampleBuildings Instance Segmentation buildings sample showing BuildingBuildings Instance Segmentation buildings sample showing Building

About Buildings Instance Segmentation Model

Here are a few use cases for this project:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

Cite This Project

LicenseCC BY 4.0

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

@misc{ buildings-instance-segmentation-6gnuh_dataset,
  title = { Buildings Instance Segmentation Dataset },
  type = { Open Source Dataset },
  author = { MMUAI },
  howpublished = { \url{ https://universe.roboflow.com/mmuai/buildings-instance-segmentation-6gnuh } },
  url = { https://universe.roboflow.com/mmuai/buildings-instance-segmentation-6gnuh },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2025 },
  month = { mar },
  note = { visited on 2026-07-29 },
}

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