CVUSA_ground_semantic_segmentation Computer Vision Dataset
About CVUSA_ground_semantic_segmentation Dataset
Here are a few use cases for this project:
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Urban Planning and Development: The CVUSA_ground_semantic_segmentation model can analyze satellite images and provide data on existing infrastructures, land usage, and greenery. This information can be valuable in making data-driven decisions for developing sustainable urban spaces, managing city expansions, and designing public transport systems.
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Environmental Conservation: This computer vision model can identify tree-covered areas and distinguish them from other urban elements. It can monitor the distribution of green spaces, flag deforestation issues, and help governments or environmental organizations plan targeted reforestation or preservation efforts.
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Infrastructure Maintenance: By identifying various elements like roads, pavements, and buildings, the model can be applied in assessing the condition of existing infrastructure. Government agencies and civil engineers can use this information to prioritize maintenance and repair tasks.
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Disaster Management and Response: The CVUSA_ground_semantic_segmentation model can be used to assess the impact of natural disasters such as floods or earthquakes by comparing images of affected areas before and after an event. This data can help emergency response teams and humanitarian organizations to locate and concentrate resources in the areas with the most damages.
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Real Estate and Property Analysis: This model can be used by real estate professionals to analyze neighborhoods, identifying the availability of green spaces, the density of buildings, the quality of roads and pavements, and other factors affecting property value. It can also help potential buyers or tenants make informed decisions about the location of their future homes or workplaces.
Use Free Tree, Building and Road Detection API
Powered by general detection model
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": "tree, building, road, pavement"
},
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
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ cvusa_ground_semantic_segmentation_dataset,
title = { CVUSA_ground_semantic_segmentation Dataset },
type = { Open Source Dataset },
author = { Tesi },
howpublished = { \url{ https://universe.roboflow.com/tesi-uzai4/cvusa_ground_semantic_segmentation } },
url = { https://universe.roboflow.com/tesi-uzai4/cvusa_ground_semantic_segmentation },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { feb },
note = { visited on 2026-07-29 },
}