This notebook presents a data preparation workflow that finds same-date and overlapped Landsat8 and Sentinel2 image patches and provide a way to download the processed images to the local machine.
This is a part of the ongoing research project Landsat2Sentinel.
The whole notebook is available on my Github repository.
import ee ee.Initialize() from IPython import display import zipfile import urllib from ipywidgets import IntProgress Function for downloading images from GEE def download_tif(image, scale, name, folder): url = ee.
Recently, during a discussion with a colleague about his CNN model architecture on remote sensing image fusion task, he mentioned something that was interesting. Specifically, in his network, he used FCN implementations Keras.layers.Dense and torch.nn.Linear in his code, the input to the FCN is a 2D image with many channels with size (160, 160, channels). Traditionally, I think that to pass through a FCN layer, the neuron numbers of the first FCN layer in this case, should be 160 * 160 * channels, which basically means to flat the volume to a 1D array and feed in a traditional neural network.
Recently I have been trying to install GDAL on my Mac and I cannot find a way to link the Python libraries to the Python program I am using so that I can import GDAL libraries by the import gdal statement in my python program.
The GDAL is designed to run in Mac OS X Terminals, but the installation include python libraries which are not automatically linked to the program. Therefore, the only thing we need to do to make it work in python is to link those libraries properly.