we proposean end-to-end convolutional neural network (CNNs)architecture, termed NAdir-view to Street-view NETwork (NaSNet), to learn a transformation between street-view and overhead-view images. The network extracts features from both street-view and aerial images and learns adaptive transformation matrices to map the aerial image feature maps to the corresponding street-view image feature maps. The pixel-wise difference between the transformed feature maps and the “ground truth” feature maps, which are directly extracted from the matching street-view images, are minimized using the Huber loss.

Network Architecture

Requirements

• Python2.7
• NumPy
• Tensorflow>=1.4
• scipy

Experiment Dataset

• CVUSA datset: a dataset in America, with pairs of ground-level images and satellite images. All ground-level images are panoramic images.
  The dataset can be accessed from https://github.com/viibridges/crossnet

Running

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Training:

$ python main.py