Universe

NMPU Computer Vision Dataset

byJianing LiuTask:
Object Detection
License:CC BY 4.0105 views6 downloads
See all 651 imagesNMPU SAR sample showing planeNMPU SAR sample showing bridgeNMPU SAR sample showing portNMPU SAR sample showing baseball fieldNMPU SAR sampleNMPU SAR sample showing baseball field, track-and-field groundNMPU SAR sample showing planeNMPU SAR sample showing baseball field, track-and-field groundNMPU SAR sample showing baseball field, track-and-field groundNMPU SAR sampleNMPU SAR sample showing tennis courtNMPU SAR sample showing track-and-field ground

About NMPU Dataset

Here are a few use cases for this project:

  1. Urban Planning: The model can be used to analyze aerial images of city regions for urban planning and infrastructural development. The identification of sports facilities, airports, ports, and transportation routes can provide critical data about an area, facilitating better decision-making.

  2. Surveillance and Security: Security agencies can use the NMPU model to monitor activity in sensitive areas like oil tanks, airports, ports, and bridges. Quick identification of unexpected items or activities could help ensure security and swift response to any potential crisis.

  3. Sports Management: The model could be used to plan, build, and manage sports infrastructure by identifying the distribution and availability of different kinds of sports courts and fields in a city or town.

  4. Traffic Management and Control: By identifying car and plane types and tallying numbers, the model can contribute to traffic analysis, monitoring, and control. This usage can facilitate more efficient traffic flow and capacity planning.

  5. Environment Impact Assessment: The model could help in identifying the presence and spread of human-made structures like ports, oil tanks, and sports grounds. This usage can assist in assessing the environmental impact created by these features and planning for more sustainable methods moving forward.

Use Free Car, Plane and Bridge Detection API

Powered by general detection model

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": "car, plane, bridge, tennis court, baseball field"
  },
  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 

Cite This Project

LicenseCC BY 4.0

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

@misc{ nmpu_dataset,
  title = { NMPU Dataset },
  type = { Open Source Dataset },
  author = { Jianing Liu },
  howpublished = { \url{ https://universe.roboflow.com/jianing-liu/nmpu } },
  url = { https://universe.roboflow.com/jianing-liu/nmpu },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2021 },
  month = { oct },
  note = { visited on 2026-07-29 },
}

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