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Deep learning model to identify solar photovoltaic parks using Sentinel-2 imagery. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: May 19, 2022 Item updated: Dec 30, 2024 Number of downloads: 3,097

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Description

Solar power is a clean source of energy. To replace conventional power sources, solar power generation must be scaled. This is being done by creating large solar photovoltaic parks. Some of large parks even span up to thousands of acres and are equipped with millions of solar panels. This information can serve government, policy makers, international organizations, companies, researchers in various ways.

Traditional ways of obtaining information on solar photo voltaic parks, such as surveys and on-site visits, are time consuming and error prone. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of creating classified maps indicating concentration of solar parks, reducing time and effort required significantly.

Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.

Input

Sentinel-2 L2A imagery (bottom of atmosphere) having 12 bands and 10 meter resolution in form of a raster product, mosaic dataset, or image service. 

Output

Classified raster denoting solar photovoltaic parks.

Applicable geographies

The model is expected to work well globaly.

Model architecture

This model uses the UNet model architecture implemented in ArcGIS API for Python.

Accuracy metrics

This model has an overall accuracy of 99.0 %. Solar photovoltaic park class has a precision of 0.966, recall of 0.967 and F1 score of 0.967.

Training data
This model has been trained on an Esri proprietary solar photovoltaic park classification dataset.

Limitations

  •  False positives are observed near costal areas, mountains and cloudy regions.

Sample results
Here are a few results from the model. To view more, see this web map.










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https://downloads.esri.com/blogs/arcgisonline/esrilogo_new.png This work is licensed under the Esri Master License Agreement.

No special restrictions or limitations on using the item's content have been provided.

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