Solar panles instance segmentation Instance Segmentation Dataset and Pre-Trained Model by fabio-conti

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Explore Dataset

Here are a few use cases for this project:

Solar Panel Installation Planning: The model can assist in aerially identifying suitable spaces for solar panel installation by classifying and marking areas with already placed solar panels on residential and commercial buildings. This would allow for more efficient solar energy utilization.
Energy Potential Assessment: The computer vision model can be used by renewable energy companies to estimate the current usage of solar energy in a specific area. By evaluating the number and size of solar panels in an area, companies can better understand solar energy potential and devise investment plans.
Remote Monitoring and Maintenance: Using drone or satellite imagery, the model can help solar energy providers to remotely track the condition of solar panels. It can help in predicting maintenance requirements, thereby increasing the efficiency of panels.
Urban Planning and Environment Study: Researchers and city planners could use this model to study urban development trends with respect to green energy adoption. It can help them understand how solar panels are distributed across the city and raise awareness for a more sustainable strategy.
Real Estate Property Evaluation: Real estate companies can use this model to identify properties with solar panels installation. By leveraging this information, they can provide more precise evaluations, considering the green energy capabilities of the properties.

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{
                            solar-panles-instance-segmentation_dataset,
                            title = { Solar panles instance segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { fabio-conti },
                            howpublished = { \url{ https://universe.roboflow.com/fabio-conti/solar-panles-instance-segmentation } },
                            url = { https://universe.roboflow.com/fabio-conti/solar-panles-instance-segmentation },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-06-22 },
                            }