SolarDetector_journal Instance Segmentation Dataset and Pre-Trained Model by Solarfinderlabel

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Explore Dataset

Here are a few use cases for this project:

Solar Energy Analysis: This model could be used to estimate the coverage of solar energy production in different regions. By identifying the number of houses or buildings using solar arrays, energy researchers and companies could make more accurate projections and plans.
Urban Planning: City and regional planners could use the SolarDetector_journal to assess renewable energy adoption in urban/rural areas. This could help promote the benefits of solar power and plan for its increased integration into the power grid.
Real Estate Features: Real estate firms could utilize the model to identify properties equipped with solar panels, adding it as a selling point in their listings or aiding in selecting properties for green investor portfolios.
Monitoring Energy Transformation: Organizations focusing on climate change could use the model to track the progress of the transformation to renewable energies, providing real-world data to support their efforts.
Disaster Recovery: In post-disaster scenarios, the model could be used to assess the state of solar infrastructure, helping prioritize recovery efforts and maximizing the restoration of self-sufficient energy sources.

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{ solardetector_journal_dataset,
    title = { SolarDetector_journal Dataset },
    type = { Open Source Dataset },
    author = { Solarfinderlabel },
    howpublished = { \url{ https://universe.roboflow.com/solarfinderlabel/solardetector_journal } },
    url = { https://universe.roboflow.com/solarfinderlabel/solardetector_journal },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { may },
    note = { visited on 2023-12-08 },
}