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Deep learning model to classify parking lots in high-resolution satellite or aerial imagery. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: Jun 15, 2023 Item updated: Jan 1, 2025 Number of downloads: 5,028

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Description

Parking lots in the USA occupy significant land area, particularly in urban and suburban areas. Using parking spaces for solar panel installation in the USA is a growing trend, known as "solar parking lots" or "solar carports". While solar energy has made substantial progress in the United States, there is still untapped potential. By installing solar panels on parking structures, it is possible to utilize this space for solar energy generation without requiring additional land. By doing so, it not only provides shade for parked vehicles but also generates clean energy and reduce the carbon footprint of buildings and facilities. They can also be combined with electric vehicle (EV) charging infrastructure, to estimate the potential demand for electric vehicles, which can be powered by the solar panels installed in the parking lot, promoting the adoption of clean transportation and reducing reliance on fossil fuels and further enhancing sustainability. But traditionally, parking areas are manually digitized and classified, which is a very labour and time-intensive task. Automating the task using deep learning models for parking space detection and solar panel capacity calculation outperforms traditional methods in terms of efficiency, accuracy, scalability, adaptability, real-time monitoring, and integration with renewable energy goals.

The use of GeoAI for parking space detection and solar panel installation capacity calculation can have potential applications in urban planning, land use optimization, renewable energy deployment, sustainable transportation and contribute to the country's renewable energy goals. 


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
8-bit, 3-band high resolution (30 centimeters -1.2 meters) imagery. For detecting small sized parking lots, higher resolution imagery is highly recommended.


Output

Feature layer representing classified parking lots. 


Applicable geographies

The model is expected to work well in the United States.


Model architecture

This model uses the MMSegmentation based DeepLabV3Plus model architecture implemented in ArcGIS API for Python.


Accuracy metrics

This model has an average precision score of 0.75 and recall of 0.68.


Training data
This model has been trained on an Esri proprietary parking lot classification dataset.

Limitations

     1. The model is expected to work well on commercial paved parking lots.

     2. The model might get confused with paved surface having similar reflectance.

Sample results

Here are a few results from the model. To view more, see this story.



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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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          Size: 223.915 MB

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          ID: 2618363f856044348029c6868a02753c

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          dlpk, pretrained model, LivingAtlasDLPK, Parking Lot Detection

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