I’ve just wrapped up a small job refreshing teaching materials for the Australian National University Centre for Water and Landscape Dynamics – an innovative group using remote sensing tech to address landscape-scale analysis of things that matter for assessing the state of the environment (see, for example: http://wald.anu.edu.au/data_services/data/continuous-and-comprehensive-national-environmental-reporting-australias-environment/).
The task? convert a MATLAB and ArcGIS-based LiDAR tutorial into an open-source equivalent using Jupyter notebooks. The results are here (links will be updated to the ANU WALD repository as soon as the latest pull request is accepted):
https://github.com/adamsteer/remote-sensing-workshops/blob/master/5_lidar_waveforms.ipynb
…and here:
https://github.com/adamsteer/remote-sensing-workshops/blob/master/6_lidar_pointclouds.ipynb
They’re not meant to cover all of LiDAR, or make the readers into LiDAR experts – the aim is to provide a reasonably straightforward walk through some key concepts and use cases for data they may come across. You can find more deep detail about airborne LiDAR in particular, and other used for data in previous posts here: https://spatialised.net/category/lidar/
I was fortunate enough to crash test the material with summer school students, and have already incorporated the first iteration of feedback. I hope the materials continue to evolve, and stay useful.
And of course, if you like what you see, I’d be happy to discuss training materials for your organisation – drop me a line!