- Road Structure Refined CNN for Road Extraction in Aerial Image - May 2017
- pix2pix
- Machine Learning for Aerial Image Labeling
- SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS
- Learning to Detect Roads in High-Resolution Aerial Images
- CycleGAN
- Fully Convolutional Networks for Semantic Segmentation (CVPR)
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (2016)
- [Kaggle Satellite Image Segmentation Competition] (https://deepsense.io/deep-learning-for-satellite-imagery-via-image-segmentation/) (http://blog.kaggle.com/2017/04/26/dstl-satellite-imagery-competition-1st-place-winners-interview-kyle-lee/?utm_source=Mailing%20list&utm_campaign=f1be30eedd-Kaggle_Newsletter_05-04-2017&utm_medium=email&utm_term=0_f42f9df1e1-f1be30eedd-400419961)
- Road and Building Detection Datasets
- Maps dataset from pix2pix work
- Maps dataset from CycleGAN work
- Vlodmir Mnih Dataset
- Fix the dataset options a. pix2pix dataset + rotation b. CIL dataset + rotation c. Mnih dataset (if we figure out how to remove the white patches in some of the images)
- Decide on the input size of the image (next step depends on this)
- Get the distance map (see the loss function part of the paper)
- If we're using the pix2pix dataset, how to convert google map images to road segmentation maps (white for road, black for non-road)
- Get the deconv according to the paper efficiently (use the conv2d_transpose and upsample right now)
After the above, we will be able to run the network and get numbers