This is an advanced course of Python applications in remote sensing. You can find the syllabus here.
1- Learn how to read metadata from .geojson files using JSON library and show the results as a map or on the plot.
2- Learn how to manipulate image datasets and prepare them for machine learning or deep learning procedures.
3- Read remote sensing high resolution images and perform change detection methodologies to create a change map.
4- Read Landsat-8 images with Rasterio/GDAL and show the results on a map using Cartopy.
5- Read point cloud datasets in either las/laz formats using laspy and show the point cloud in a 3D plot.
6- Manipulate the point cloud dataset using its attributes such as classification, intensity, etc.
7- Introduce ICESat-2 data and read its photon heights product with python and display the graphs. It's analysis-ready!
8- Become familiar with satellite's attitude and position information which is broadcast to ground stations (case study: NASA's ACE satellite).
9- Show the path of the satellite around the Earth and around the Sun.
10- Analyze the position and velocity of a spacecraft in solar system.
- Matplotlib tutorial.
- Cartopy introduction and its gallery.
- Rasterio as an alternative for GDAL.
- GDAL as the most complete geospatial library.
- Steps to add Open Street Map (OSM) as a background layer to QGIS.
- PDAL, a library for point cloud processing in Python.
- Laspy helps you read and write .LAS/.LAZ files in Python.
- netCDF4 is a library for working with .nc files. It can be used for some EO data manipulation.
- h55py is an standard package which enables you to read HDF files. EO datasets are usually distributed in this format.
- HDFView is a software for reading .h5 (HDF) files.
- Panoply is a software package for visualizing climate data such as netCDF4 files. Here's another tutorial link for this software.