The task is to reduce the computation time of calculating MNDWI and NDVI in- dices for satellite images from the DynamicEarthNet Dataset and storing them in GeoTIFF format using Dask. DynamicEarthNet is the first dataset that provides this unique combination of daily measurements and high-quality labels. It consists of daily, multi-spectral satellite observations of 75 selected areas of interest (AOIs) distributed over the globe with imagery from Planet Labs. These observations are paired with pixel-wise monthly semantic segmentation labels of 7 land use and land cover (LULC) classes. The dataset can be downloaded from DynamicEarthNet. Out of these 75 AOIs, we will focus mainly on two AOIs i.e. planet16N and planet18N.
Before running the notebook, create two folders ndvi-images and mndwi-images in the same directory. The following modules/libraries should be installed in your system before running the notebook:
- rasterio: A Python module for reading and writing geospatial raster data. It allows users to work with a wide variety of raster data formats, including GeoTIFF.
# pip and python must be installed in your system
$ pip install rasterio- Dask: Dask is a parallel computing library for Python that enables parallel and distributed computing across multiple CPU cores or clusters. It allows users to work with large datasets that are too big to fit into memory on a single machine, by breaking them into smaller partitions and distributing them across multiple processors or machines.
# pip and python must be installed in your system
$ pip install dask- PyTorch is a Python package that helps in Tensor computation (like NumPy) with strong GPU acceleration
# pip and python must be installed in your system
$ pip install torch- Make sure modules like numpy, matpotlib are installed.
# pip and python must be installed in your system
$ pip install numpy
$ pip install matplotlibPlease go through the report which contains a detailed explanation of ndvi and mndwi indices calculations and the results obtained.