Detecting Parking Spaces in a Parcel using Satellite Images
This work addresses the need for automated parking space detection in remote sensing applications, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of localizing parking spaces and vehicles in parking lots using satellite images, achieving an average class accuracy of 97.56% for both categories with a Feature Pyramid based Mask RCNN.
Remote Sensing Images from satellites have been used in various domains for detecting and understanding structures on the ground surface. In this work, satellite images were used for localizing parking spaces and vehicles in parking lots for a given parcel using an RCNN based Neural Network Architectures. Parcel shapefiles and raster images from USGS image archive were used for developing images for both training and testing. Feature Pyramid based Mask RCNN yields average class accuracy of 97.56% for both parking spaces and vehicles