  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
