Parking Lot Classification

Parking lots in the USA occupy significant land areas, 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 reduces 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, and sustainable transportation and contribute to the country's renewable energy goals. 

Model Access  Link 

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

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