UNetLSTM

Code of the following manuscript:

'Detecting Urban Changes With Recurrent Neural Networks From Multitemporal Sentinel-2 Data'

https://arxiv.org/abs/1910.07778

Steps

1. Preprocessing with preprocess.py

Create a folder (e.g 'images') of the raw data with the following structure:

/ images / city / imgs_i / (13 tif 2D images of sentinel channels)

where i=[1,2,3,4,5]

and city = ['abudhabi', 'aguasclaras', 'beihai', 'beirut', 'bercy', 'bordeaux', 'brasilia', 'chongqing', 'cupertino', 'dubai', 'hongkong', 'lasvegas', 'milano', 'montpellier', 'mumbai', 'nantes', 'norcia', 'paris', 'pisa', 'rennes', 'rio', 'saclay_e', 'saclay_w', 'valencia']

Use preprocess.py to preprocess the images of the OSCD dataset.

2. Create csv file with (x,y) locations for patch extraction during the training process using make_xys.py

Here you need to specify the folder with the OSCD dataset's Labels.

Note that 'train_areas' list should be defined in the same way both in make_xys.py and main.py

3. Start the training process with main.py

4. Make predictions on the OSCD dataset's testing images with inference.py

Comments are included in the scripts for further instructions.

If you find this work useful, please consider citing: M.Papadomanolaki, Sagar Verma, M. Vakalopoulou, S. Gupta, K., 'Detecting Urban Changes With Recurrent Neural Networks From Multitemporal Sentinel-2 Data', IGARSS 2019, Yokohama, Japan