merged_data 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 Research: Researchers could use the "merged_data" model to automatically identify and analyze placement of solar panels in aerial or street-view images. This could help understand trends in solar power adoption and optimization of panel placement with respect to trees and building structures.
Urban Planning: City or county urban planners could use the model to identify installations of solar panels, the intrusion of trees, and the presence of shadows on buildings. Such data will be valuable for future urban development planning, considering renewable energy sources, vegetation, and the optimization of natural light.
Renewable Energy Promotion: Government agencies or NGOs could use the model to scan neighborhoods and identify houses or buildings with solar panels and those without. They could then target the buildings without solar panels for promotion of solar energy benefits and subsidies.
Real Estate Market Analysis: Real estate agencies could use the model to identify properties with solar arrays to potentially value them higher due to their energy efficiency. They could also provide insights about possible renovations on properties for higher resale value like adding solar panels or cutting obstructing trees.
Energy Efficiency Rating: Energy rating agencies can use this model to identify the presence of solar panels, window size, and obstructions like trees or chimneys that may affect a building's energy efficiency. This information can be used to develop a more accurate energy efficiency rating for the building.

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{
                            merged_data-qq7d0_dataset,
                            title = { merged_data Dataset },
                            type = { Open Source Dataset },
                            author = { Solarfinderlabel },
                            howpublished = { \url{ https://universe.roboflow.com/solarfinderlabel/merged_data-qq7d0 } },
                            url = { https://universe.roboflow.com/solarfinderlabel/merged_data-qq7d0 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-06-18 },
                            }